Molecular Repair of the Brain

by Ralph C. Merkle, Ph.D.

This article was published in two parts in Cryonics magazine, January and April 1994. A more highly annotated version can be found on Ralph Merkle’s Cryonics Pages.

See also cryobiologist Dr. Gregory Fahy’s critique of this paper and Dr. Merkle’s response. For an alternative repair scenario contributed by an anonymous biologist that circumvents Dr. Fahy’s concerns, see “Realistic Scenario for Nanotechnological Repair of the Frozen Human Brain“.

A short version of this paper entitled “The Technical Feasibility of Cryonics,” appeared in Medical Hypotheses Vol. 39, 1992; 6-16.

ABSTRACT

Cryonic suspension is a method of stabilizing the condition of someone who is terminally ill so that they can be transported to the medical care facilities that will be available in the late 21st or 22nd century. There is little dispute that the condition of a person stored at the temperature of liquid nitrogen is stable, but the process of freezing inflicts a level of damage which cannot be reversed by current medical technology. Whether or not the damage inflicted by current methods can ever be reversed depends both on the level of damage and the ultimate limits of future medical technology. The failure to reverse freezing injury with current methods does not imply that it can never be reversed in the future, just as the inability to build a personal computer in 1890 did not imply that such machines would never be economically built. This paper considers the limits of what medical technology should eventually be able to achieve (based on the currently understood laws of chemistry and physics) and the kinds of damage caused by current methods of freezing. It then considers whether methods of repairing the kinds of damage caused by current suspension techniques are likely to be achieved in the future.

INTRODUCTION

Tissue preserved in liquid nitrogen can survive centuries without deterioration [note 1]. This simple fact provides an imperfect time machine that can transport us almost unchanged from the present to the future: we need merely freeze ourselves in liquid nitrogen. If freezing damage can someday be cured, then a form of time travel to the era when the cure is available would be possible. While unappealing to the healthy this possibility is more attractive to the terminally ill, whose options are somewhat limited. Far from being idle speculation, this option is available to anyone who so chooses. First seriously proposed in the 1960’s by Ettinger[80] there are now three organizations in the U.S. that provide cryonic suspension services.

Perhaps the most important question in evaluating this option is its technical feasibility: will it work?

Given the remarkable progress of science during the past few centuries it is difficult to dismiss cryonics out of hand. The structure of DNA was unknown prior to 1953; the chemical (rather than “vitalistic”) nature of living beings was not appreciated until early in the 20th century; it was not until 1864 that spontaneous generation was put to rest by Louis Pasteur, who demonstrated that no organisms emerged from heat-sterilized growth medium kept in sealed flasks; and Sir Isaac Newton’s Principia established the laws of motion in 1687, just over 300 years ago. If progress of the same magnitude occurs in the next few centuries, then it becomes difficult to argue that the repair of frozen tissue is inherently and forever infeasible.

Hesitation to dismiss cryonics is not a ringing endorsement and still leaves the basic question in considerable doubt. Perhaps a closer consideration of how future technologies might be applied to the repair of frozen tissue will let us draw stronger conclusions — in one direction or the other. Ultimately, cryonics will either (a) work or (b) fail to work. It would seem useful to know in advance which of these two outcomes to expect. If it can be ruled out as infeasible, then we need not waste further time on it. If it seems likely that it will be technically feasible, then a number of nontechnical issues should be addressed in order to obtain a good probability of overall success.

The reader interested in a general introduction to cryonics is referred to other sources[23, 24, 80]. Here, we focus on technical feasibility.

While many isolated tissues (and a few particularly hardy organs) have been successfully cooled to the temperature of liquid nitrogen and rewarmed[59], further successes have proven elusive. While there is no particular reason to believe that a cure for freezing damage would violate any laws of physics (or is otherwise obviously infeasible), it is likely that the damage done by freezing is beyond the self-repair and recovery capabilities of the tissue itself. This does not imply that the damage cannot be repaired, only that significant elements of the repair process would have to be provided from an external source. In deciding whether such externally provided repair will (or will not) eventually prove feasible, we must keep in mind that such repair techniques can quite literally take advantage of scientific advances made during the next few centuries. Forecasting the capabilities of future technologies is therefore an integral component of determining the feasibility of cryonics. Such a forecast should, in principle, be feasible. The laws of physics and chemistry as they apply to biological structures are well understood and well defined. Whether the repair of frozen tissue will (or will not) eventually prove feasible within the framework defined by those laws is a question which we should be able to answer based on what is known today.

Current research (outlined below) supports the idea that we will eventually be able to examine and manipulate structures molecule by molecule and even atom by atom. Such a technical capability has very clear implications for the kinds of damage that can (and cannot) be repaired. The most powerful repair capabilities that should eventually be possible can be defined with remarkable clarity. The question we wish to answer is conceptually straightforward: will the most powerful repair capability that is likely to be developed in the long run (perhaps over a few centuries) be adequate to repair tissue that is frozen using the best available current methods?[note 2] Eigler and Schweizer[49] have already developed the capability “… to fabricate rudimentary structures of our own design, atom by atom.” Eigler said[129], “…by the time I’m ready to kick the bucket, we might be able to store enough information on my exact physical makeup that someday we’ll be able to reassemble me, atom by atom.”

The general purpose ability to manipulate structures with atomic precision and low cost is often called nanotechnology (also called molecular engineering, molecular manufacturing, molecular nanotechnology , etc.). There is widespread belief that such a capability will eventually be developed [1, 2, 3, 4, 7, 8, 10, 19, 41, 47, 49, 83, 84, 85, 106, 107, 108, 116, 117, 118, 119, 121, 122] though exactly how long it will take is unclear. The long storage times possible with cryonic suspension make the precise development time of such technologies noncritical. Development any time during the next few centuries would be sufficient to save the lives of those suspended with current technology.

In this paper, we give a brief introduction to nanotechnology and then clarify the technical issues involved in applying it in the conceptually simplest and most powerful fashion to the repair of frozen tissue.

NANOTECHNOLOGY

Broadly speaking, the central thesis of nanotechnology is that almost any structure consistent with the laws of chemistry and physics that can be specified can in fact be built. This possibility was first advanced by Richard Feynman in 1959 [4] when he said: “The principles of physics, as far as I can see, do not speak against the possibility of maneuvering things atom by atom.” (Feynman won the 1965 Nobel prize in physics).

This concept is receiving increasing attention in the research community. There have been two international research conferences directly on molecular manufacturing [83, 84, 116, 121] as well as a broad range of conferences on related subjects. Science [47, page 26] said “The ability to design and manufacture devices that are only tens or hundreds of atoms across promises rich rewards in electronics, catalysis, and materials. The scientific rewards should be just as great, as researchers approach an ultimate level of control — assembling matter one atom at a time.” “Within the decade, [John] Foster [at IBM Almaden] or some other scientist is likely to learn how to piece together atoms and molecules one at a time using the STM [Scanning Tunneling Microscope].”

Eigler and Schweizer[49] at IBM reported on “…the use of the STM at low temperatures (4 K) to position individual xenon atoms on a single-crystal nickel surface with atomic precision. This capacity has allowed us to fabricate rudimentary structures of our own design, atom by atom. The processes we describe are in principle applicable to molecules also. In view of the device-like characteristics reported for single atoms on surfaces [omitted references], the possibilities for perhaps the ultimate in device miniaturization are evident.”

J. A. Armstrong, IBM Chief Scientist and Vice President for Science and Technology[106] said “I believe that nanoscience and nanotechnology will be central to the next epoch of the information age, and will be as revolutionary as science and technology at the micron scale have been since the early ’70’s…. Indeed, we will have the ability to make electronic and mechanical devices atom-by-atom when that is appropriate to the job at hand.”

The New York Times said[107]: “Scientists are beginning to gain the ability to manipulate matter by its most basic components — molecule by molecule and even atom by atom.” “That ability, while now very crude, might one day allow people to build almost unimaginably small electronic circuits and machines, producing, for example, a super computer invisible to the naked eye. Some futurists even imagine building tiny robots that could travel through the body performing surgery on damaged cells.”

Drexler[1,10,19,41, 85] has proposed the assembler, a small device resembling an industrial robot which would be capable of holding and positioning reactive compounds in order to control the precise location at which chemical reactions take place. This general approach should allow the construction of large atomically precise objects by a sequence of precisely controlled chemical reactions.

The best technical discussion of nanotechnology has recently been provided by Drexler[ 85].

Ribosomes

The plausibility of this approach can be illustrated by the ribosome. Ribosomes manufacture all the proteins used in all living things on this planet. A typical ribosome is relatively small (a few thousand cubic nanometers) and is capable of building almost any protein by stringing together amino acids (the building blocks of proteins) in a precise linear sequence. To do this, the ribosome has a means of grasping a specific amino acid (more precisely, it has a means of selectively grasping a specific transfer RNA, which in turn is chemically bonded by a specific enzyme to a specific amino acid), of grasping the growing polypeptide, and of causing the specific amino acid to react with and be added to the end of the polypeptide[14].

The instructions that the ribosome follows in building a protein are provided by mRNA (messenger RNA). This is a polymer formed from the 4 bases adenine, cytosine, guanine, and uracil. A sequence of several hundred to a few thousand such bases codes for a specific protein. The ribosome “reads” this “control tape” sequentially, and acts on the directions it provides.

Assemblers

In an analogous fashion, an assembler will build an arbitrary molecular structure following a sequence of instructions. The assembler, however, will provide three-dimensional positional and full orientational control over the molecular component (analogous to the individual amino acid) being added to a growing complex molecular structure (analogous to the growing polypeptide). In addition, the assembler will be able to form any one of several different kinds of chemical bonds, not just the single kind (the peptide bond) that the ribosome makes.

Calculations indicate that an assembler need not inherently be very large. Enzymes “typically” weigh about 10⁵ amu (atomic mass units [note 3]), while the ribosome itself is about 3 x 10⁶ amu[14]. The smallest assembler might be a factor of ten or so larger than a ribosome. Current design ideas for an assembler are somewhat larger than this: cylindrical “arms” about 100 nanometers in length and 30 nanometers in diameter, rotary joints to allow arbitrary positioning of the tip of the arm, and a worst-case positional accuracy at the tip of perhaps 0.1 to 0.2 nanometers, even in the presence of thermal noise[ 85]. Even a solid block of diamond as large as such an arm weighs only sixteen million amu, so we can safely conclude that a hollow arm of such dimensions would weigh less. Six such arms would weigh less than 10⁸ amu.

Molecular Computers

The assembler requires a detailed sequence of control signals, just as the ribosome requires mRNA to control its actions. Such detailed control signals can be provided by a computer. A feasible design for a molecular computer has been presented by Drexler[2, 19, 85]. This design is mechanical in nature, and is based on sliding rods that interact by blocking or unblocking each other at “locks.” [note 4] This design has a size of about 5 cubic nanometers per “lock” (roughly equivalent to a single logic gate). Quadrupling this size to 20 cubic nanometers (to allow for power, interfaces, and the like) and assuming that we require a minimum of 10⁴ “locks” to provide minimal control results in a volume of 2 x 10⁵ cubic nanometers (.0002 cubic microns) for the computational element. This many gates is sufficient to build a simple 4-bit or 8-bit general purpose computer. For example, the 6502 8-bit microprocessor can be implemented in about 10,000 gates, while an individual 1-bit processor in the Connection Machine has about 3,000 gates. Assuming that each cubic nanometer is occupied by roughly 100 atoms of carbon, this 2 x 10⁵ cubic nanometer computer will have a mass of about 2 x 10⁸ amu.

An assembler might have a kilobyte of high speed (rod-logic based) RAM, (similar to the amount of RAM used in a modern one-chip computer) and 100 kilobytes of slower but more dense “tape” storage — this tape storage would have a mass of 10⁸ amu or less (roughly 10 atoms per bit — see below). Some additional mass will be used for communications (sending and receiving signals from other computers) and power. In addition, there will probably be a “toolkit” of interchangeable tips that can be placed at the ends of the assembler’s arms. When everything is added up a small assembler, with arms, computer, “toolkit,” etc. should weigh less than 10⁹ amu.

E. coli (a common bacterium) weighs about 10¹² amu[14, page 123]. Thus, an assembler should be much larger than a ribosome, but much smaller than a bacterium.

Self Replicating Systems

It is also interesting to compare Drexler’s architecture for an assembler with the von Neumann architecture for a self replicating device. Von Neumann’s “universal constructing automaton”[45] had both a universal Turing machine to control its functions and a “constructing arm” to build the “secondary automaton.” The constructing arm can be positioned in a two-dimensional plane, and the “head” at the end of the constructing arm is used to build the desired structure. While von Neumann’s construction was theoretical (existing in a two dimensional cellular automata world), it still embodied many of the critical elements that now appear in the assembler.

Further work on self-replicating systems was done by NASA in 1980 in a report that considered the feasibility of implementing a self-replicating lunar manufacturing facility with conventional technology[48]. One of their conclusions was that “The theoretical concept of machine duplication is well developed. There are several alternative strategies by which machine self-replication can be carried out in a practical engineering setting.” They estimated it would require 20 years (and many billions of dollars) to develop such a system. While they were considering the design of a macroscopic self-replicating system (the proposed “seed” was 100 tons) many of the concepts and problems involved in such systems are similar regardless of size.

Positional Chemistry

Chemists have been remarkably successful at synthesizing a wide range of compounds with atomic precision. Their successes, however, are usually small in size (with the notable exception of various polymers). Thus, we know that a wide range of atomically precise structures with perhaps a few hundreds of atoms in them are quite feasible. Larger atomically precise structures with complex three-dimensional shapes can be viewed as a connected sequence of small atomically precise structures. While chemists have the ability to precisely sculpt small collections of atoms there is currently no ability to extend this capability in a general way to structures of larger size. An obvious structure of considerable scientific and economic interest is the computer. The ability to manufacture a computer from atomically precise logic elements of molecular size, and to position those logic elements into a three-dimensional volume with a highly precise and intricate interconnection pattern would have revolutionary consequences for the computer industry.

A large atomically precise structure, however, can be viewed as simply a collection of small atomically precise objects which are then linked together. To build a truly broad range of large atomically precise objects requires the ability to create highly specific positionally controlled bonds. A variety of highly flexible synthetic techniques have been considered by Drexler [ 85]. We shall describe two such methods here to give the reader a feeling for the kind of methods that will eventually be feasible.

We assume that positional control is available and that all reactions take place in a hard vacuum. The use of a hard vacuum allows highly reactive intermediate structures to be used, e.g., a variety of radicals with one or more dangling bonds. Because the intermediates are in a vacuum, and because their position is controlled (as opposed to solutions, where the position and orientation of a molecule are largely random), such radicals will not react with the wrong thing for the very simple reason that they will not come into contact with the wrong thing.

It is difficult to maintain biological structures in a hard vacuum at room temperature because of water vapor and the vapor of other small compounds. By sufficiently lowering the temperature, however, it is possible to reduce the vapor pressure to effectively 0.

Normal solution-based chemistry offers a smaller range of controlled synthetic possibilities. For example, highly reactive compounds in solution will promptly react with the solution. In addition, because positional control is not provided, compounds randomly collide with other compounds. Any reactive compound will collide randomly and react randomly with anything available (including itself). Solution-based chemistry requires extremely careful selection of compounds that are reactive enough to participate in the desired reaction, but sufficiently non-reactive that they do not accidentally participate in undesired side reactions. Synthesis under these conditions is somewhat like placing the parts of a radio into a box, shaking, and pulling out an assembled radio. The ability of chemists to synthesize what they want under these conditions is amazing.

Much of current solution-based chemical synthesis is devoted to preventing unwanted reactions. With assembler-based synthesis, such prevention is a virtually free by-product of positional control.

To illustrate positional synthesis in vacuum somewhat more concretely, let us suppose we wish to bond two compounds, A and B. As a first step, we could utilize positional control to selectively abstract a specific hydrogen atom from compound A. To do this, we would employ a radical that had two spatially distinct regions: one region would have a high affinity for hydrogen while the other region could be built into a larger “tip” structure that would be subject to positional control. A simple example would be the 1-propynyl radical, which consists of three co-linear carbon atoms and three hydrogen atoms bonded to the sp3 carbon at the “base” end. The radical carbon at the radical end is triply bonded to the middle carbon, which in turn is singly bonded to the base carbon. In a real abstraction tool, the base carbon would be bonded to other carbon atoms in a larger diamondoid structure which would provide positional control, and the tip might be further stabilized by a surrounding “collar” of unreactive atoms attached near the base that would limit lateral motions of the reactive tip.

The affinity of this structure for hydrogen is quite high. Propyne (the same structure but with a hydrogen atom bonded to the “radical” carbon) has a hydrogen-carbon bond dissociation energy in the vicinity of 132 kilocalories per mole. As a consequence, a hydrogen atom will prefer being bonded to the 1-propynyl hydrogen abstraction tool in preference to being bonded to almost any other structure. By positioning the hydrogen abstraction tool over a specific hydrogen atom on compound A, we can perform a site specific hydrogen abstraction reaction. This requires positional accuracy of roughly a bond length (to prevent abstraction of an adjacent hydrogen). Quantum chemical analysis of this reaction by Musgrave et. al.[108] show that the activation energy for this reaction is low, and that for the abstraction of hydrogen from the hydrogenated diamond (111) surface (modeled by isobutane) the barrier is very likely zero.

Having once abstracted a specific hydrogen atom from compound A, we can repeat the process for compound B. We can now join compound A to compound B by positioning the two compounds so that the two dangling bonds are adjacent to each other, and allowing them to bond.

This illustrates a reaction using a single radical. With positional control, we could also use two radicals simultaneously to achieve a specific objective. Suppose, for example, that two atoms A1 and A2 which are part of some larger molecule are bonded to each other. If we were to position the two radicals X1 and X2 adjacent to A1 and A2, respectively, then a bonding structure of much lower free energy would be one in which the A1-A2 bond was broken, and two new bonds A1-X1 and A2-X2 were formed. Because this reaction involves breaking one bond and making two bonds (i.e., the reaction product is not a radical and is chemically stable) the exact nature of the radicals is not critical. Breaking one bond to form two bonds is a favored reaction for a wide range of cases. Thus, the positional control of two radicals can be used to break any of a wide range of bonds.

A range of other reactions involving a variety of reactive intermediate compounds (carbenes are among the more interesting ones) are proposed in [85], along with the results of semi-empirical and ab initio quantum calculations and the available experimental evidence.

Another general principle that can be employed with positional synthesis is the controlled use of force. Activation energy, normally provided by thermal energy in conventional chemistry, can also be provided by mechanical means. Pressures of 1.7 megabars have been achieved experimentally in macroscopic systems[30]. At the molecular level such pressure corresponds to forces that are a large fraction of the force required to break a chemical bond. A molecular vise made of hard diamond-like material with a cavity designed with the same precision as the reactive site of an enzyme can provide activation energy by the extremely precise application of force, thus causing a highly specific reaction between two compounds.

To achieve the low activation energy needed in reactions involving radicals requires little force, allowing a wider range of reactions to be caused by simpler devices (e.g., devices that are able to generate only small force). Further analysis is provided in [85].

Feynman said: “The problems of chemistry and biology can be greatly helped if our ability to see what we are doing, and to do things on an atomic level, is ultimately developed — a development which I think cannot be avoided.” Drexler has provided the substantive analysis required before this objective can be turned into a reality. We are nearing an era when we will be able to build virtually any structure that is specified in atomic detail and which is consistent with the laws of chemistry and physics. This has substantial implications for future medical technologies and capabilities.

Repair Devices

A repair device is an assembler which is specialized for repair of tissue in general, and frozen tissue in particular. We assume that a repair device has a mass of between 10⁹ and 10¹⁰ amu (e.g., we assume that a repair device might be as much as a factor of 10 more complicated than a simple assembler). This provides ample margin for increasing the capabilities of the repair device if this should prove necessary.

A single repair device of the kind described will not, by itself, have sufficient memory to store the programs required to perform all the repairs. However, if it is connected to a network (in the same way that current computers can be connected into a local area network) then a single large “file server” can provide the needed information for all the repair devices on the network. The file server can be dedicated to storing information: all the software and data that the repair devices will need. Almost the entire mass of the file server can be dedicated to storage, it can service many repair devices, and can be many times the size of one device without greatly increasing system size. Combining these advantages implies the file server will have ample storage to hold whatever programs might be required during the course of repair. In a similar fashion, if further computational resources are required they can be provided by “large” compute servers located on the network.

Cost

One consequence of the existence of assemblers is that they are cheap. Because an assembler can be programmed to build almost any structure, it can in particular be programmed to build another assembler. Thus, self reproducing assemblers should be feasible and in consequence the manufacturing costs of assemblers would be primarily the cost of the raw materials and energy required in their construction. Eventually (after amortization of possibly quite high development costs), the price of assemblers (and of the objects they build) should be no higher than the price of other complex structures made by self-replicating systems. Potatoes — which have a staggering design complexity involving tens of thousands of different genes and different proteins directed by many megabits of genetic information — cost well under a dollar per pound.

DESCRIBING THE BRAIN AT THE MOLECULAR LEVEL

In principle we need only repair the frozen brain, for the brain is the most critical and important structure in the body. Faithfully repairing the liver (or any other secondary tissue) molecule by molecule (or perhaps atom by atom) appears to offer no benefit over simpler techniques — such as replacement. The calculations and discussions that follow are therefore based on the size and composition of the brain. It should be clear that if repair of the brain is feasible, then the methods employed could (if we wished) be extended in the obvious way to the rest of the body.

The brain, like all the familiar matter in the world around us, is made of atoms. It is the spatial arrangement of these atoms that distinguishes an arm from a leg, the head from the heart, and sickness from health. This view of the brain is the framework for our problem, and it is within this framework that we must work. Our problem, broadly stated, is that the atoms in a frozen brain are in the wrong places. We must put them back where they belong (with perhaps some minor additions and removals, as well as just rearrangements) if we expect to restore the natural functions of this most wonderful organ.

In principle, the most that we could usefully know about the frozen brain would be the coordinates of each and every atom in it (though confer note 5 ). This knowledge would put us in the best possible position to determine where each and every atom should go. This knowledge, combined with a technology that allowed us to rearrange atomic structure in virtually any fashion consistent with the laws of chemistry and physics, would clearly let us restore the frozen structure to a fully functional and healthy state.

In short, we must answer three questions:

Where are the atoms?
Where should they go?
How do we move them from where they are to where they should be?

Regardless of the specific technical details involved, any method of restoring a person in suspension must answer these three questions, if only implicitly. Current efforts to freeze and then thaw tissue (e.g., experimental work aimed at freezing and then reviving sperm, kidneys, etc.) answer these three questions indirectly and implicitly. Ultimately, technical advances should allow us to answer these questions in a direct and explicit fashion.

Rather than directly consider these questions at once, we shall first consider a simpler problem: how would we go about describing the position of every atom if somehow this information was known to us? The answer to this question will let us better understand the harder questions.

Other work which considers the information required to describe a human being exists[127, 128].

How Many Bits to Describe One Atom

Each atom has a location in three-space that we can represent with three coordinates: X, Y, and Z. Atoms are usually a few tenths of a nanometer apart. If we could record the position of each atom to within 0.01 nanometers, we would know its position accurately enough to know what chemicals it was a part of, what bonds it had formed, and so on. The brain is roughly .1 meters across, so .01 nanometers is about 1 part in 10¹⁰. That is, we would have to know the position of the atom in each coordinate to within one part in ten billion. A number of this size can be represented with about 33 bits. There are three coordinates, X, Y, and Z, each of which requires 33 bits to represent, so the position of an atom can be represented in 99 bits. An additional few bits are needed to store the type of the atom (whether hydrogen, oxygen, carbon, etc.), bringing the total to slightly over 100 bits [note 5].

Thus, if we could store 100 bits of information for every atom in the brain, we could fully describe its structure in as exacting and precise a manner as we could possibly need. (Dancoff and Quastler[128], using a somewhat better encoding scheme, say that 24.5 bits per atoms should suffice). A memory device of this capacity should be quite literally possible. To quote Feynman[4]: “Suppose, to be conservative, that a bit of information is going to require a little cube of atoms 5 x 5 x 5 — that is 125 atoms.” This is indeed conservative. Single stranded DNA already stores a single bit in about 16 atoms (excluding the water that it’s in). It seems likely we can reduce this to only a few atoms[1]. The work at IBM[49] suggests a rather obvious way in which the presence or absence of a single atom could be used to encode a single bit of information (although some sort of structure for the atom to rest upon and some method of sensing the presence or absence of the atom will still be required, so we would actually need more than one atom per bit in this case). If we conservatively assume that the laws of chemistry inherently require 10 atoms to store a single bit of information, we still find that the 100 bits required to describe a single atom in the brain can be represented by about 1,000 atoms. Put another way, the location of every atom in a frozen structure is (in a sense) already encoded in that structure in an analog format. If we convert from this analog encoding to a digital encoding, we will increase the space required to store the same amount of information. That is, an atom in three-space encodes its own position in the analog value of its three spatial coordinates. If we convert this spatial information from its analog format to a digital format, we inflate the number of atoms we need by perhaps as much as 1,000. If we digitally encoded the location of every atom in the brain, we would need 1,000 times as many atoms to hold this encoded data as there are atoms in the brain. This means we would require roughly 1,000 times the volume. The brain is somewhat over one cubic decimeter, so it would require somewhat over one cubic meter of material to encode the location of each and every atom in the brain in a digital format suitable for examination and modification by a computer.

While this much memory is remarkable by today’s standards, its construction clearly does not violate any laws of physics or chemistry. That is, it should literally be possible to store a digital description of each and every atom in the brain in a memory device that we will eventually be able to build.

How Many Bits to Describe a Molecule

While such a feat is remarkable, it is also much more than we need. Chemists usually think of atoms in groups — called molecules. For example, water is a molecule made of three atoms: an oxygen and two hydrogens. If we describe each atom separately, we will require 100 bits per atom, or 300 bits total. If, however, we give the position of the oxygen atom and give the orientation of the molecule, we need: 99 bits for the location of the oxygen atom + 20 bits to describe the type of molecule (“water”, in this case) and perhaps another 30 bits to give the orientation of the water molecule (10 bits for each of the three rotational axes). This means we can store the description of a water molecule in only 150 bits, instead of the 300 bits required to describe the three atoms separately. (The 20 bits used to describe the type of the molecule can describe up to 1,000,000 different molecules — many more than are present in the brain).

As the molecule we are describing gets larger and larger, the savings in storage gets bigger and bigger. A whole protein molecule will still require only 150 bits to describe, even though it is made of thousands of atoms. The canonical position of every atom in the molecule is specified once the type of the molecule (which occupies a mere 20 bits) is given. A large molecule might adopt many configurations, so it might at first seem that we’d require many more bits to describe it. However, biological macromolecules typically assume one favored configuration rather than a random configuration, and it is this favored configuration that we will describe [note 6].

We can do even better: the molecules in the brain are packed in next to each other. Having once described the position of one, we can describe the position of the next molecule as being such-and-such a distance from the first. If we assume that two adjacent molecules are within 10 nanometers of each other (a reasonable assumption) then we need only store 10 bits of “delta X,” 10 bits of “delta Y,” and 10 bits of “delta Z” rather than 33 bits of X, 33 bits of Y, and 33 bits of Z. This means our molecule can be described in only 10+10+10+20+30 or 80 bits.

We can compress this further by using various other clever stratagems (50 bits or less is quite achievable), but the essential point should be clear. We are interested in molecules, and describing a molecule takes fewer bits than describing an atom.

Do We Really Need to Describe Each Molecule?

A further point will be obvious to any biologist. Describing the exact position and orientation of a hemoglobin molecule within a red blood cell is completely unnecessary. Each hemoglobin molecule bounces around within the red blood cell in a random fashion, and it really doesn’t matter exactly where it is, nor exactly which way it’s pointing. All we need do is say, “It’s in that red blood cell!” So, too, for any other molecule that is floating at random in a “cellular compartment:” we need only say which compartment it’s in. Many other molecules, even though they do not diffuse freely within a cellular compartment, are still able to diffuse fairly freely over a significant range. The description of their position can be appropriately compressed.

While this reduces our storage requirements quite a bit, we could go much further. Instead of describing molecules, we could describe entire sub-cellular organelles. It seems excessive to describe a mitochondrion by describing each and every molecule in it. It would be sufficient simply to note the location and perhaps the size of the mitochondrion, for all mitochondria perform the same function: they produce energy for the cell. While there are indeed minor differences from mitochondrion to mitochondrion, these differences don’t matter much and could reasonably be neglected.

We could go still further, and describe an entire cell with only a general description of the function it performs: this nerve cell has synapses of a certain type with that other cell, it has a certain shape, and so on. We might even describe groups of cells in terms of their function: this group of cells in the retina performs a “center surround” computation, while that group of cells performs edge enhancement. Cherniak[115] said: “On the usual assumption that the synapse is the necessary substrate of memory, supposing very roughly that (given anatomical and physiological “noise”) each synapse encodes about one binary bit of information, and a thousand synapses per neuron are available for this task: 10¹⁰ cortical neurons x 10³ synapses = 10¹³ bits of arbitrary information (1.25 terabytes) that could be stored in the cerebral cortex.”

How Many Bits Do We Really Need?

This kind of logic can be continued, but where does it stop? What is the most compact description which captures all the essential information? While many minor details of neural structure are irrelevant, our memories clearly matter. Any method of describing the human brain which resulted in loss of long term memory has rather clearly gone too far. When we examine this quantitatively, we find that preserving the information in our long term memory might require as little as 10⁹ bits (somewhat over 100 megabytes)[37]. We can say rather confidently that it will take at least this much information to adequately describe an individual brain. The gap between this lower bound and the molecule-by-molecule upper bound is rather large, and it is not immediately obvious where in this range the true answer falls. We shall not attempt to answer this question, but will instead (conservatively) simply adopt the upper bound.

CRITERIA OF DEATH

death \’deth\ n [ME deeth, fr. OE death; akin to ON dauthi death, deyja to die — more at DIE] 1: a permanent cessation of all vital functions : the end of life
Webster’s New Collegiate Dictionary

Determining when “a permanent cessation of all vital functions” has occurred is not easy. Historically, premature declarations of death and subsequent burial alive have been a major problem. In the seventh century, Celsus wrote “… Democritus, a man of well merited celebrity, has asserted that there are in reality, no characteristics of death sufficiently certain for physicians to rely upon.”[87, page 166].

Montgomery, reporting on the evacuation of the Fort Randall Cemetery, states that nearly two percent of those exhumed were buried alive[87].

Many people in the nineteenth century, alarmed by the prevalence of premature burial, requested, as part of the last offices, that wounds or mutilations be made to assure that they would not awaken … embalming received a considerable impetus from the fear of premature burial.

New Criteria

Current criteria of “death” are sufficient to insure that spontaneous recovery in the mortuary or later is a rare occurrence. When examined closely, however, such criteria are simply a codified summary of symptoms that have proven resistant to treatment by available techniques. Historically, they derive from the fear that the patient will spontaneously recover in the morgue or crypt. There is no underlying theoretical structure to support them, only a continued accumulation of ad hoc procedures supported by empirical evidence. To quote Robert Veach[15]: “We are left with rather unsatisfying results. Most of the data do not quite show that persons meeting a given set of criteria have, in fact, irreversibly lost brain function. They show that patients lose heart function soon, or that they do not “recover.” Autopsy data are probably the most convincing. Even more convincing, though, is that over the years not one patient who has met the various criteria and then been maintained, for whatever reason, has been documented as having recovered brain function. Although this is not an elegant argument, it is a reassuring.” In short, current criteria are adequate to determine when current medical technology will fail to revive the patient, but are silent on the capabilities of future medical technology.

Each new medical advance forces a reexamination and possible change of the existing ad hoc criteria. The criteria used by the clinician today to determine “death” are dramatically different from the criteria used 100 years ago, and have changed more subtly but no less surely in the last decade [note 7]. It seems almost inevitable that the criteria used 100 years from now will differ dramatically from the criteria commonly employed today.

These ever shifting criteria for “death” raise an obvious question: is there a definition which will not change with advances in technology? A definition which does have a theoretical underpinning and is not dependent on the technology of the day?

The answer arises from the confluence and synthesis of many lines of work, ranging from information theory, neuroscience, physics, biochemistry and computer science to the philosophy of the mind and the evolving criteria historically used to define death.

When someone has suffered a loss of memory or mental function, we often say they “aren’t themselves.” As the loss becomes more serious and all higher mental functions are lost, we begin to use terms like “persistent vegetative state.” While we will often refrain from declaring such an individual “dead,” this hesitation does not usually arise because we view their present state as “alive” but because there is still hope of recovery to a healthy state with memory and personality intact. From a physical point of view we believe there is a chance that their memories and personalities are still present within the physical structure of the brain, even though their behavior does not provide direct evidence for this. If we could reliably determine that the physical structures encoding memory and personality had in fact been destroyed, then we would abandon hope and declare the person dead.

The Information Theoretic Criterion of Death

Clearly, if we knew the coordinates of each and every atom in a person’s brain then we would (at least in principle) be in a position to determine with absolute finality whether their memories and personality had been destroyed in the information theoretic sense, or whether their memories and personality were preserved but could not, for some reason, be expressed. If such final destruction had taken place, then there would be little reason for hope. If such destruction had not taken place, then it would in principle be possible for a sufficiently advanced technology to restore the person to a fully functional and healthy state with their memories and personality intact.

Considerations like this lead to the information theoretic criterion of death [note 8]. A person is dead according to the information theoretic criterion if their memories, personality, hopes, dreams, etc. have been destroyed in the information theoretic sense. That is, if the structures in the brain that encode memory and personality have been so disrupted that it is no longer possible in principle to restore them to an appropriate functional state then the person is dead. If the structures that encode memory and personality are sufficiently intact that inference of the memory and personality are feasible in principle, and therefore restoration to an appropriate functional state is likewise feasible in principle, then the person is not dead.

A simple example from computer technology is in order. If a computer is fully functional then its memory and “personality” are completely intact. If it fell out the seventh floor window to the concrete below, it would rapidly cease to function. However, its memory and “personality” would still be present in the pattern of magnetizations on the disk. With sufficient effort, we could completely repair the computer with its memory and “personality” intact [note 9].

In a similar fashion, as long as the structures that encode the memory and personality of a human being have not been irretrievably “erased” (to use computer jargon) then restoration to a fully functional state with memory and personality intact is in principle feasible. Any technology independent definition of “death” should conclude that such a person is not dead, for a sufficiently advanced technology could restore the person to a healthy state.

On the flip side of the coin, if the structures encoding memory and personality have suffered sufficient damage to obliterate them beyond recognition, then death by the information theoretic criterion has occurred. An effective method of insuring such destruction is to burn the structure and stir the ashes. This is commonly employed to insure the destruction of classified documents. Under the name of “cremation” it is also employed on human beings and is sufficient to insure that death by the information theoretic criterion takes place.

More Exotic Approaches

It is not obvious that the preservation of life requires the physical repair or even the preservation of the brain[11,12]. Although the brain is made of neurons, synapses, protoplasm, DNA and the like; most modern philosophers of consciousness view these details as no more significant than hair color or clothing style. Three samples follow.

The ethicist and prolific author Robert Veatch said, in Death, Dying, and the Biological Revolution, “An `artificial brain’ is not possible at present, but a walking, talking, thinking individual who had one would certainly be considered living.”[15, page 23].

The noted philosopher of consciousness Paul Churchland said, in Matter and Consciousness, “If machines do come to simulate all of our internal cognitive activities, to the last computational detail, to deny them the status of genuine persons would be nothing but a new form of racism.”[12, page 120].

Hans Moravec, renowned roboticist and Director of the Mobile Robot Lab at Carnegie Mellon said, “Body-identity assumes that a person is defined by the stuff of which a human body is made. Only by maintaining continuity of body stuff can we preserve an individual person. Pattern-identity, conversely, defines the essence of a person, say myself, as the pattern and the process going on in my head and body, not the machinery supporting that process. If the process is preserved, I am preserved. The rest is mere jelly.”[50, page 117].

We’ll Use the Conservative Approach

Restoration of the existing structure will be more difficult than building an artificial brain (particularly if the restoration is down to the molecular level). Despite this, we will examine the technically more exacting problem of restoration because it is more generally acceptable. Most people accept the idea that restoring the brain to a healthy state in a healthy body is a desirable objective. A range of increasingly less restrictive objectives (as described) are possible. To the extent that more relaxed criteria are acceptable, the technical problems are much less demanding. By deliberately adopting such a conservative position, we lay ourselves open to the valid criticism that the methods described here are unlikely to prove necessary. Simpler techniques that relax to some degree the philosophical constraints we have imposed might well be adopted in practice. In this paper we will eschew the more exotic possibilities (without, however, adopting any position on their desirability).

Another issue is not so much philosophical as emotional. Major surgery is not a pretty sight. There are few people who can watch a surgeon cut through living tissue with equanimity. In a heart transplant, for example, surgeons cut open the chest of a dying patient to rip out their dying heart, cut open a fresh cadaver to seize its still-beating heart, and then stitch the cadaver’s heart into the dying patients chest. Despite this (which would have been condemned in the middle ages as the blackest of black magic), we cheer the patient’s return to health and are thankful that we live in an era when medicine can save lives that were formerly lost.

The mechanics of examining and repairing the human brain, possibly down to the level of individual molecules, might not be the best topic for after dinner conversation. While the details will vary depending on the specific method used, this could also be described by lurid language that failed to capture the central issue: the restoration to full health of a human being.

A final issue that should be addressed is that of changes introduced by the process of restoration itself. The exact nature and extent of these changes will vary with the specific method. Current surgical techniques, for example, result in substantial tissue changes. Scarring, permanent implants, prosthetics, etc. are among the more benign outcomes. In general, methods based on a sophisticated ability to rearrange atomic structure should result in minimal undesired alterations to the tissue.

“Minimal changes” does not mean “no changes.” A modest amount of change in molecular structure, whatever technique is used, is both unavoidable and insignificant. The molecular structure of the human brain is in a constant state of change during life — molecules are synthesized, utilized, and catabolized in a continuous cycle. Cells continuously undergo slight changes in morphology. Cells also make small errors in building their own parts. For example, ribosomes make errors when they build proteins. About one amino acid in every 10,000 added to a growing polypeptide chain by a ribosome is incorrect[14, page 383]. Changes and errors of a similar magnitude introduced by the process of restoration can reasonably be neglected.

Does the Information Theoretic Criterion Matter?

It is normally a matter of small concern whether a physician of 2190 would or would not concur with the diagnosis of “death” by a contemporary physician applied to a specific patient in 1990. A physician of today who found himself in 1790 would be able to do little for a patient whose heart had stopped, even though he knew intellectually that an intensive care unit would likely be able to save the patients life. Intensive care units were simply not available in 1790, no matter what the physician knew was possible. So, too, with the physician of today when informed that a physician 200 years hence could save the life of the patient that he has just pronounced “dead.” There is nothing he can do, for he can only apply the technologies of today — except in the case of cryonic suspension.

In this one instance, we must ask not whether the person is dead by today’s (clearly technology dependent) criteria, but whether the person is dead by all future criteria. In short, we must ask whether death by the information theoretic criterion has taken place. If it has not, then cryonic suspension is a reasonable (and indeed life saving) course of action.

Experimental Proof or Disproof of Cryonics

It is often said that “cryonics is freezing the dead.” It is more accurate to say that “cryonics is freezing the terminally ill. Whether or not they are dead remains to be seen.”

The scientifically correct experiment to verify that cryonics works (or demonstrate that it does not work) is quite easy to describe:

Select N experimental subjects.
Freeze them.
Wait 100 years.
See if the technology available 100 years from now can (or cannot) cure them.

The drawback of this experimental protocol is obvious: we can’t get the results for 100 years. This problem is fundamental. The use of future technology is an inherent part of cryonics. Criticisms of cryonics based on the observation that freezing and thawing mammals with present technology don’t work are irrelevant, for that is not what is being proposed.

This kind of problem is not entirely unique to cryonics. A new AIDS treatment might undergo clinical trials lasting a few years. The ethical dilemma posed by the terminally ill AIDS patient who might be assisted by the experimental treatment is well known. If the AIDS patient is given the treatment prior to completion of the clinical trials, it is possible that his situation could be made significantly worse. On the other hand, to deny a potentially life saving treatment to someone who will soon die anyway is ethically untenable.

In the case of cryonics this is not an interim dilemma pending the (near term) outcome of clinical trials. It is a dilemma inherent in the nature of the proposal. Clinical trials, the bulwark of modern medical practice, are useless in resolving the effectiveness of cryonics in a timely fashion.

Further, cryonics (virtually by definition) is a procedure used only when the patient has exhausted all other available options. In current practice the patient is suspended after legal death: the fear that the treatment might prove worse than the disease is absent. Of course, suspension of the terminally ill patient somewhat before legal death has significant advantages. A patient suffering from a brain tumor might view suspension following the obliteration of his brain as significantly less desirable than suspension prior to such obliteration, even if the suspension occurred at a point in time when the patient was legally “alive.”

In such a case, it is inappropriate to disregard or override the patient’s own wishes. To quote the American College of Physicians Ethics Manual, “Each patient is a free agent entitled to full explanation and full decision-making authority with regard to his medical care. John Stuart Mill expressed it as: `Over himself, his own body and mind, the individual is sovereign.’ The legal counterpart of patient autonomy is self-determination. Both principles deny legitimacy to paternalism by stating unequivocally that, in the last analysis, the patient determines what is right for him.” “If the [terminally ill] patient is a mentally competent adult, he has the legal right to accept or refuse any form of treatment, and his wishes must be recognized and honored by his physician.”[92]

If clinical trials cannot provide us with an answer, are there any other methods of evaluating the proposal? Can we do more than say that (a) cryonic suspension can do no harm (in keeping with the Hippocratic oath), and (b) it has some difficult-to-define chance of doing good?

Failure Criteria

Trying to prove something false is often the simplest method of clarifying exactly what is required to make it true. A consideration of the information theoretic criterion of death makes it clear that, from a technical point of view (ignoring various non-technical issues) there are two and only two ways in which cryonics can fail [note 10].

Cryonics will fail if:

Information theoretic death occurs prior to reaching liquid nitrogen temperature [note 11].
Repair technology that is feasible in principle is never developed and applied in practice, even after the passage of centuries.

The first failure criterion can only be considered against the background of current understanding of freezing damage, ischemic injury and mechanisms of memory and synaptic plasticity. Whether or not memory and personality are destroyed in the information theoretic sense by freezing and the ischemic injury that might precede it can only be answered by considering both the physical nature of memory and the nature of the damage to which the brain is subjected before reaching the stability provided by storage in liquid nitrogen. The following sections will therefore provide brief reviews of these subjects.

The second failure criterion is considered in the later sections on technical issues, which discuss in more detail how future technologies might be applied to the repair of frozen tissue.

As the reader will readily appreciate, the following reviews will consider only the most salient points that are of the greatest importance in determining overall feasibility. They are necessarily too short to consider the topics in anything like full detail, but should provide sufficient information to give the reader an overview of the relevant issues. References to further reading are provided throughout [note 12].

FREEZING DAMAGE

There is an extensive literature on the damage caused by both cooling and freezing to liquid nitrogen temperatures. Some reviews are[5, 6, 68, 70]. Scientific American had a recent and quite accessible article[57]. In this section, we briefly review the nature of such damage and consider whether it is likely to cause information theoretic death. Damage, per se, is not meaningful except to the extent that it obscures or obliterates the nature of the original structure.

While cooling tissue to around 0 degrees C creates a number of problems, the ability to cool mammals to this temperature or even slightly below (with no ice formation) using current methods followed by subsequent complete recovery[61, 62] shows that this problem can be controlled and is unlikely to cause information theoretic death. We will, therefore, ignore the problems caused by such cooling. This problem is discussed in [5] and elsewhere.

Further, some “freezing” damage in fact occurs upon re-warming. Current work supports this idea because the precise method used to re-warm tissue can strongly affect the success or failure of present experiments even when freezing conditions are identical[5, 6]. If we presume that future repair methods avoid the step of re-warming the tissue prior to analysis and instead analyze the tissue directly in the frozen state then this source of damage will be eliminated. Several current methods can be used to distinguish between damage that occurs during freezing and damage that occurs while thawing. At present, it seems likely that some damage occurs during each process. While significant damage does occur during slow freezing, it does not induce structural changes which obliterate the cell.

Present Day Successes

Many types of tissue including human embryos, sperm, skin, bone, red and white blood cells, bone marrow, and others [5, 6, 59] have been frozen in liquid nitrogen, thawed, and have recovered. This is not true of whole mammals [note 13]. The brain seems more resistant than most organs to freezing damage[58, 79]. Recovery of overall brain function following freezing to liquid nitrogen temperature has not been demonstrated, although recovery of unit level electrical activity following freezing to -60 degrees C has been demonstrated[79].

Fractures

Perhaps the most dramatic injury caused by freezing is macroscopic fractures[56]. Tissue becomes extremely brittle at or below the “glass transition temperature” at about 140K. Continued cooling to 77K (the temperature of liquid nitrogen) creates tensile stress in the glassy material. This is exacerbated by the skull, which inhibits shrinkage of the cranial contents. This stress causes readily evident macroscopic fractures in the tissue.

Fractures that occur below the glass transition temperature result in very little information loss. While dramatic, this damage is unlikely to cause or contribute to information theoretic death.

Ice

The damage most commonly associated with freezing is that caused by ice. Contrary to common belief, freezing does not cause cells to burst open like water pipes on a cold winter’s day. Quite the contrary, ice formation takes place outside the cells in the extracellular region. This is largely due to the presence of extracellular nucleating agents on which ice can form, and the comparative absence of intracellular nucleating agents. Consequently the intracellular liquid supercools.

Extracellular ice formation causes an increase in the concentration of the extra-cellular solute, e.g., the chemicals in the extracellular liquid are increased in concentration by the decrease in available water. The immediate effect of this increased extracellular concentration is to draw water out of the cells by osmosis. Thus, freezing dehydrates cells.

Damage can be caused by the extracellular ice, by the increased concentration of solute, or by the reduced temperature itself. All three mechanisms can play a role under appropriate conditions.

The damage caused by extracellular ice formation depends largely on the fraction of the initial liquid volume that is converted to ice[6, 57]. (The initial liquid volume might include a significant amount of cryoprotectant as well as water). When the fraction of the liquid volume converted to ice is small, damage is often reversible even by current techniques. In many cases, conversion of significantly more than 40% of the liquid volume to ice is damaging[70, page 134; 71]. The brain is more resistant to such injury: conversion of up to 60% of the liquid volume in the brain to ice is associated with recovery of neuronal function[58, 62, 66, 82]. Storey and Storey said “If the cell volume falls below a critical minimum, then the bilayer of phospholipids in the membrane becomes so greatly compressed that its structure breaks down. Membrane transport functions cannot be maintained, and breaks in the membrane spill cell contents and provide a gate for ice to propagate into the cell. Most freeze-tolerant animals reach the critical minimum cell volume when about 65 percent of total body water is sequestered as ice.”[57].

Appropriate treatment with cryoprotectants (in particular glycerol) prior to freezing will keep 40% or more of the liquid volume from being converted to ice even at liquid nitrogen temperatures.

Fahy has said “All of the postulated problems in cryobiology — cell packing [omitted reference], channel size constraints [omitted reference], optimal cooling rate differences for mixed cell populations [omitted reference], osmotically mediated injury[omitted references], and the rest — can be solved in principle by the selection of a sufficiently high concentration of cryoprotectant prior to freezing. In the extreme case, all ice formation could be suppressed completely by using a concentration of agent sufficient to ensure vitrification of the biological system in question [omitted reference]”[73]. Unfortunately, a concentration of cryoprotectant sufficiently high to protect the system from all freezing injury would itself be injurious[73]. It should be possible to trade the mechanical injury caused by ice formation for the biochemical injury caused by the cryoprotectant, which is probably advantageous. Current suspension protocols at Alcor call for the introduction of greater than 6 molar glycerol. Both venous and arterial glycerol concentrations have exceeded 6 molar in several recent suspensions. If this concentration of cryoprotectant is also reaching the tissues, it should keep over 60% of the initial liquid volume from being converted to ice at liquid nitrogen temperatures [note 14].

Concentration Effects

“Dehydration and concentration of solutes past some critical level may disrupt metabolism and denature cell proteins and macromolecular complexes”[70, page 125]. The functional losses caused by this mechanism seem unlikely to result in significant information loss. One qualification to this conclusion is that cell membranes appear to be weakened by increased solute concentration[5, page 92]. To the extent that structural elements are weakened by increased solute concentrations the vulnerability of the cell to structural damage is increased.

Denaturing

Finally, denaturing of proteins might occur at low temperature. In this process the tertiary and perhaps even secondary structure of the protein might be disrupted leading to significant loss of protein function. However, the primary structure of the protein (the linear sequence of amino acids) is still intact and so inference of the correct functional state of the protein is in principle trivial. Further, the extent of protein denaturation caused by freezing must necessarily be limited given the relatively wide range of tissues that have been successfully frozen and thawed.

Intracellular Freezing

Intracellular freezing is another damaging event which might occur[6]. If cooling is slow enough to allow the removal of most of the water from the cell’s interior by osmosis, then the high concentration of solute will prevent the small amount of remaining water from freezing. If cooling is too rapid, there will be insufficient time for the water within the cell to escape before it freezes. In the latter case, the intracellular contents are supercooled and freezing is abrupt (the cell “flashes”). While this correlates with a failure to recover function[5, 6, 68, 70] it is difficult to believe that rapid freezing results in significant loss of information.

Intracellular freezing is largely irrelevant to cryonic suspensions because of the slow freezing rates dictated by the large mass of tissue being frozen. Such freezing rates are too slow for intracellular freezing to occur except when membrane rupture allows extracellular ice to penetrate the intracellular region. If the membrane does fail, one would expect the interior of the cell to “flash.”

Loss of Information versus Loss of Function

Spontaneous recovery of function following freezing to liquid nitrogen temperatures using the best currently available techniques appears unlikely for mammalian organs, including the brain. Despite this, the level of structural preservation can be quite good. The complexity of the systems that have been successfully frozen and rewarmed is remarkable, and supports the claim that good structural preservation is often achieved. The mechanisms of damage that have been postulated in the literature are sufficiently subtle that information loss is likely to be small; that is, death by the information theoretic criterion is unlikely to have occurred. Further research aimed specifically at addressing this issue is needed.

ISCHEMIC INJURY AND PRESUSPENSION INJURY

Although modern cryonic suspensions can involve minimal delay [note 15] and future suspensions might eliminate delay entirely [note 16], delay is sometimes unavoidable [note 17]. The most significant type of damage that such delay causes is ischemic injury.

Broadly speaking, the structure of the human brain remains intact for several hours or more following the cessation of blood flow, or ischemia. The tissue changes that occur subsequent to ischemia have been well studied. There have also been studies of the “postmortem” changes that occur in tissue. Perhaps the most interesting of these studies was conducted by Kalimo et. al.[65].

“Postmortem” Changes in the Human Brain

Many researchers have examined postmortem changes in neuronal tissues. In “A Comparison of Methodologies for the Study of Functional Transmitter Neurochemistry in Human Brain” Dodd et al.[124] said

Effects of postmortem delay. Some brain functions are damaged irreversibly within minutes of the cessation of blood flow to the tissue. This led to the widespread belief that it would be impossible to isolate metabolically active and responsive preparations very long after death and use them to study neurotransmission. However, this is a misconception; many groups have successfully obtained functional preparations from normal (Table 1) [not present in this article] and pathological (Table 2) [not present in this article] human brain tissue from autopsies carried out up to 24 h or more postmortem. This is perhaps less surprising when the stability of enzymes, receptors, and nucleic acids is taken into consideration (see Hardy and Dodd, 1983 [reference 123 in this article]). With very few exceptions, the brain retains the metabolic machinery to reconstitute tissue metabolite and neurotransmitter pools. It also appears that sufficient structural integrity is retained to allow the various tissue compartments to remain relatively intact and distinct.Experiments with both animal and human brain have shown that viable preparations can be isolated routinely up to at least 24 h postmortem, a time scale within which a sufficient number of autopsies is carried out to allow extensive neurochemical studies. When the human subject has died suddenly (see below) [not in this article], such preparations exhibit the same range of characteristics as preparations made from fresh animal tissue, or from fresh human tissue obtained at biopsy or neurosurgery. Thus incubated synaptosomes and brain slices from postmortem human brain respire, accumulate tissue potassium, maintain membrane potentials, release neurotransmitters in a calcium-dependent fashion, and possess active, sodium – dependent uptake systems (see Table 1 for references [not in this article]). Electron microscopic examination of synaptosome preparations from postmortem human brain showed them to be only slightly less pure than preparations from fresh tissue, although some degree of damage is evident (Hardy et al., 1982 [not in this article]).

In order to study immediate “postmortem” changes, Kalimo et. al. perfused the brains of 5 patients with aldehydes within half an hour of “clinical death”. Subsequent examination of the preserved brain tissue with both light and electron microscopy showed the level of structural preservation. In two cases, the changes described were consistent with approximately one to two hours of ischemic injury. (Ischemic injury often begins prior to declaration of “clinical death”, hence the apparently longer ischemic period compared with the interval following declaration of death and prior to perfusion of fixative). Physical preservation of cellular structure and ultrastructure was excellent. It is difficult to avoid the conclusion that information loss was negligible in these cases. In two other cases, elevated intraparenchymal pressure prevented perfusion with the preservative, thus preventing examination of the tissue. Without such an examination, it is difficult to draw conclusions about the extent of information loss. In the final case, “…the most obvious abnormality was the replacement of approximately four-fifths of the parenchyma of the brain by a fluid-containing cavity that was lined by what seemed to be very thin remnants of the cerebral cortex.” Cryonic suspension in this last case would not be productive.

As an aside, the vascular perfusion of chemical fixatives to improve stability of tissue structures prior to perfusion with cryoprotectants and subsequent storage in liquid nitrogen would seem to offer significant advantages. The main issue that would require resolution prior to such use is the risk that fixation might obstruct circulation, thus impeding subsequent perfusion with cryoprotectants. Other than this risk, the use of chemical fixatives (such as aldehydes and in particular glutaraldehyde) would reliably improve structural preservation and would be effective at halting almost all deterioration within minutes of perfusion[67]. The utility of chemical preservation has been discussed by Drexler[1] and by Olson[90], among others.

Ischemia

The events following ischemia have been reasonably well characterized. Following experimental induction of ischemia in cats, Kalimo et. al.[74] found “The resulting cellular alterations were homogeneous and uniform throughout the entire brain: they included early chromatin clumping, gradually increasing electron lucency of the cell sap, distention of endoplasmic reticulum and Golgi cisternae, transient mitochondrial condensation followed by swelling and appearance of flocculent densities, and dispersion of ribosomal rosettes.” Energy levels within the cell drop sharply within a few minutes of cessation of blood flow. The chromatin clumping is a reversible early change. The loss of energy results fairly quickly in failure to maintain trans- membrane concentration gradients (for example the Na+K+ pump stops working, resulting in increased intracellular Na+ and increased extracellular K+). The uneven equilibration of concentration gradients results in changes in osmotic pressure with consequent flows of water. Swelling of mitochondria and other structures occurs. The appearance of “flocculent densities” in the mitochondria is thought to indicate severe internal membrane damage which is “irreversible.”[note 18]

Ischemic changes do not appear to result in any damage that would prevent repair (e.g., changes that would result in significant loss of information about structure) for at least a few hours. Temporary functional recovery has been demonstrated in optimal situations after as long as 60 minutes of total ischemia[93, 94, 95]. Hossmann, for example, reported results on 143 cats subjected to one hour of normothermic global brain ischemia[97]. “Body temperature was maintained at 36 degrees to 37 degrees C with a heating pad. … Completeness of ischemia was tested by injecting 133Xe into the innominate artery immediately before vascular occlusion and monitoring the absence of decay of radioactivity from the head during ischemia, using external scintillation detectors. … In 50% of the animals, even major spontaneous EEG activity returned after ischemia…. One cat survived for 1 yr after one hour of normothermic cerebrocirculatory arrest with no electrophysiologic deficit and with only minor neurologic and morphologic disturbances.” Functional recovery is a more stringent criterion than the more relaxed information theoretic criterion, which merely requires adequate structural preservation to allow inference about the pre-existing structure. Reliable identification of the various cellular structures is possible hours (and sometimes even days) later. Detailed descriptions of ischemia and its time course[72, page 209 et sequitur] also clearly show that cooling substantially slows the rate of deterioration. Thus, even moderate cooling “postmortem” slows deterioration significantly.

The theory that lysosomes (“suicide bags”) rupture and release digestive enzymes into the cell that result in rapid deterioration of chemical structure appears to be incorrect. More broadly, there is a body of work suggesting that structural deterioration does not take place rapidly.

Kalimo et. al.[74] said “It is noteworthy that after 120 min of complete blood deprivation we saw no evidence of membrane lysosomal breakdown, an observation which has also been reported in studies of in vitro lethal cell injury[omitted references], and in regional cerebral ischemia[omitted references].”

Hawkins et. al.[75] said “…lysosomes did not rupture for approximately 4 hours and in fact did not release the fluorescent dye until after reaching the postmortem necrotic phase of injury. … The original suicide bag mechanism of cell damage thus is apparently not operative in the systems studied. Lysosomes appear to be relatively stable organelles….”

Messenger RNA and Protein

Morrison and Griffin[76] said “We find that both rat and human cerebellar mRNAs are surprisingly stable under a variety of postmortem conditions and that biologically active, high-molecular-weight mRNAs can be isolated from postmortem tissue. … A comparison of RNA recoveries from fresh rat cerebella and cerebella exposed to different postmortem treatments showed that 83% of the total cytoplasmic RNAs present immediately postmortem was recovered when rat cerebella were left at room temperature for 16 h [hours] postmortem and that 90% was recovered when the cerebella were left at 4 degrees C for this length of time …. In neither case was RNA recovery decreased by storing the cerebella in liquid nitrogen prior to analysis. … Control studies on protein stability in postmortem rat cerebella show that the spectrum of abundant proteins is also unchanged after up to 16 h [hours] at room temperature….” Johnson et. al.[125] in “Extensive Postmortem Stability of RNA From Rat and Human Brain” found that postmortem delays of as long as 48 hours “…failed to reveal degradation of the specific rat brain mRNAs during the postmortem period.” They also said “We find no effect of postmortem delay on RNA quality in both rat and human.”

20 Million Year Survival of DNA

The ability of DNA to survive for long periods was dramatically illustrated by its recovery and sequencing from a 17 to 20 million year old magnolia leaf[81]. “Sediments and fossils seem to have accumulated in an anoxic lake bottom environment; they have remained unoxidized and water- saturated to the present day.” “Most leaves are preserved as compression fossils, commonly retaining intact cellular tissue with considerable ultrastructural preservation, including cell walls, leaf phytoliths, and intracellular organelles, as well as many organic constituents such as flavonoids and steroids[omitted references]. There is little evidence of post-depositional (diagenetic) change in many of the leaf fossils.”

ADDENDUM: A 1997 paper[130] critical of earlier work which attempted to recover DNA from ancient sources said “Whereas ancient DNA sequences from specimens younger than 100 000 years old have now been replicated independently (Hagelberg et al. 1994; Hoss et al. 1994; Taylor 1996), we have singularly failed to recover authentic ancient DNA from amber fossils.”For present purposes the distinction between 100,000 and 100,000,000 years is not critical: both are substantially longer than the time that a person might reasonably expect to stay in cryonic suspension.

Cell Cultures taken after “Death”

Gilden et. al.[77] report that “…nearly two-thirds of all tissue acquired in less than six hours after death was successfully grown, whereas only one-third of all tissue acquired more than six hours after death was successfully grown in tissue culture.” While it would be incorrect to conclude that widespread cellular survival occurred based on these findings, they do show that structural deterioration is insufficient to disrupt function in at least some cells. This supports the idea that structural deterioration in many other cells should not be extensive.

Information Loss and Ischemia

It is currently possible to initiate suspension immediately after legal death. In favorable circumstances legal death can be declared upon cessation of heartbeat in an otherwise revivable terminally ill patient who wishes to die a natural death and has refused artificial means of prolonging the dying process. In such cases, the ischemic interval can be short (two or three minutes). It is implausible that ischemic injury would cause information theoretic death in such a case.

As the ischemic interval lengthens, the level of damage increases. It is not clear exactly when information loss begins or when information theoretic death occurs. Present evidence supports but does not prove the hypothesis that information theoretic death does not occur for at least a few hours following the onset of ischemia. Quite possibly many hours of ischemia can be tolerated. Freezing of tissue within that time frame followed by long term storage in liquid nitrogen should provide adequate preservation of structure to allow repair [note 19].

MEMORY

It is essential to ask whether the important structural elements underlying “behavioral plasticity” (human memory and human personality) are likely to be preserved by cryonic suspension. Clearly, if human memory is stored in a physical form which is obliterated by freezing, then cryonic suspension won’t work. In this section we briefly consider a few major aspects of what is known about long term memory and whether known or probable mechanisms are likely to be preserved by freezing.

It appears likely that short term memory, which can be disrupted by trauma or a number of other processes, will not be preserved by cryonic suspension. Consolidation of short term memory into long term memory is a process that takes several hours. We will focus attention exclusively on long term memory, for this is far more stable. While the retention of short term memory cannot be excluded (particularly if chemical preservation is used to provide rapid initial fixation), its greater fragility renders this significantly less likely.

To see the Mona Lisa or Niagara Falls changes us, as does seeing a favorite television show or reading a good book. These changes are both figurative and literal, and it is the literal (or neuroscientific) changes that we are interested in: what are the physical alterations that underlie memory?

Briefly, the available evidence supports the idea that memory and personality are stored in identifiable physical changes in the nerve cells, and that alterations in the synapses between nerve cells play a critical role.

Shepherd in “Neurobiology”[38, page 547] said: “The concept that brain functions are mediated by cell assemblies and neuronal circuits has become widely accepted, as will be obvious to the reader of this book, and most neurobiologists believe that plastic changes at synapses are the underlying mechanisms of learning and memory.”

Kupfermann in “Principles of Neural Science”[13, page 1005] said: “Because of the enduring nature of memory, it seems reasonable to postulate that in some way the changes must be reflected in long-term alterations of the connections between neurons.”

Eric R. Kandel in “Principles of Neural Science” [13, page 1016] said: “Morphological changes seem to be a signature of the long-term process. These changes do not occur with short-term memory (Figure 65-6 [not reproduced here]). Moreover, the structural changes that occur with the long- term process are not restricted to the [sic] growth. Long- term habituation leads to the opposite change—a regression and pruning of synaptic connections. With long-term habituation, where the functional connections between the sensory neurons and motor neurons are inactivated (Figure 65- 2[not reproduced]), the number of terminals per neuron is correspondingly reduced by one-third (Figure 65-6[not reproduced]) and the proportion of terminals with active zones is reduced from 40% to 10%.”

Squire in “Memory and Brain”[109, page 10] said: “The most prevalent view has been that the specificity of stored information is determined by the location of synaptic changes in the nervous system and by the pattern of altered neuronal interactions that these changes produce. This idea is largely accepted at the present time, and will be explored further in this and succeeding chapters in the light of current evidence.”

Lynch, in “Synapses, Circuits, and the Beginnings of Memory”[34, page 3] said: “The question of which components of the neuron are responsible for storage is vital to attempts to develop generalized hypotheses about how the brain encodes and makes use of memory. Since individual neurons receive and generate thousands of connections and hence participate in what must be a vast array of potential circuits, most theorists have postulated a central role for synaptic modifications in memory storage.”

Turner and Greenough said “Two non-mutually exclusive possible mechanisms of brain information storage have remained the leading theories since their introduction by Ramon y Cajal [omitted reference] and Tanzi [omitted reference]. The first hypothesis is that new synapse formation, or selected synapse retention, yields altered brain circuitry which encodes new information. The second is that altered synaptic efficacy brings about similar change.”[22].

Greenough and Bailey in “The anatomy of a memory: convergence of results across a diversity of tests”[39] say: “More recently it has become clear that the arrangement of synaptic connections in the mature nervous system can undergo striking changes even during normal functioning. As the diversity of species and plastic processes subjected to morphological scrutiny has increased, convergence upon a set of structurally detectable phenomena has begun to emerge. Although several aspects of synaptic structure appear to change with experience, the most consistent potential substrate for memory storage during behavioral modification is an alteration in the number and/or pattern of synaptic connections.”

It seems likely, therefore, that human long term memory is encoded by detectable physical changes in cell structure and in particular in synaptic structure.

Plastic Changes in Model Systems

What, exactly, might these changes be? Very strong statements are possible in simple “model systems”. Bailey and Chen, for example, identified several specific changes in synaptic structure that encoded learned memories from sea slugs (Aplysia californica) by direct examination of the changed synapse with an electron microscope[36].

“Using horseradish peroxidase (HRP) to label the presynaptic terminals (varicosities) of sensory neurons and serial reconstruction to analyze synaptic contacts, we compared the fine structure of identified sensory neuron synapses in control and behaviorally modified animals. Our results indicate that learning can modulate long-term synaptic effectiveness by altering the number, size, and vesical complement of synaptic active zones.”

Examination by transmission electron microscopy in vacuum of sections 100 nanometers (several hundred atomic diameters) thick recovers little or no chemical information. Lateral resolution is at best a few nanometers (tens of atomic diameters), and depth information (within the 100 nanometer section) is entirely lost. Specimen preparation included removal and desheathing of the abdominal ganglion which was then bathed in seawater for 30 minutes before impalement and intrasomatic pressure injection of HRP. Two hours later the ganglia were fixed, histochemically processed, and embedded. Following this treatment, Bailey and Chen concluded that “…clear structural changes accompany behavioral modification, and those changes can be detected at the level of identified synapses that are critically involved in learning.”

The following observations about this work seem in order. First, several different types of changes were present. This provides redundant evidence of synaptic alteration. Inability to detect one type of change, or obliteration of one specific type of change, would not be sufficient to prevent recovery of the “state” of the synapse. Second, examination by electron microscopy is much cruder than the techniques considered here which literally propose to analyze every molecule in the structure. Further alterations in synaptic chemistry will be detectable when the synapse is examined in more detail at the molecular level. Third, there is no reason to believe that freezing would obliterate the structure beyond recognition.

Implications for Human Memory

Such satisfying evidence is at present confined to “model systems;” what can we conclude about more complex systems, e.g., humans? Certainly, it seems safe to say that synaptic alterations are also used in the human memory system, that synaptic changes of various types take place when the synapse “remembers” something, that the changes involve alterations in at least many thousands of molecules and probably involve mechanisms similar to those used in lower organisms (evolution is notoriously conservative).

It seems likely that knowledge of the morphology and connectivity of nerve cells along with some specific knowledge of the biochemical state of the cells and synapses would be sufficient to determine memory and personality. Perhaps, however, some fundamentally different mechanism is present in humans? Even if this were to prove true, any such system would be sharply constrained by the available evidence. It would have to persist over the lifetime of a human being, and thus would have to be quite stable. It would have to tolerate the natural conditions encountered by humans and the experimental conditions to which primates have been subjected without loss of memory and personality (presuming that the primate brain is similar to the human brain). And finally, it would almost certainly involve changes in tens of thousands of molecules to store each bit of information. Functional studies of human long term memory suggest it has a capacity of only 10⁹ bits (somewhat over 100 megabytes)[37] (though this did not consider motor memory, e.g., the information storage required when learning to ride a bicycle). Such a low memory capacity suggests that, independent of the specific mechanism, a great many molecules are required to remember each bit. It even suggests that many synapses are used to store each bit (recall there are perhaps 10¹⁵ synapses — which implies some 10⁶ synapses per bit of information stored in long term memory).

Given that future technology will allow the molecule-by- molecule analysis of the structures that store memory, and given that such structures are large on the molecular scale (involving at least tens of thousands of molecules each) then it appears unlikely that such structures will survive the lifetime of the individual only to be obliterated beyond recognition by freezing. Freezing is unlikely to cause information theoretic death.

TECHNICAL OVERVIEW

Even if information theoretic death has not occurred, a frozen brain is not a healthy structure. While repair might be feasible in principle, it would be comforting to have at least some idea about how such repairs might be done in practice. As long as we assume that the laws of physics, chemistry, and biochemistry with which we are familiar today will still form the basic framework within which repair will take place in the future, we can draw well founded conclusions about the capabilities and limits of any such repair technology.

The Nature of This Proposal

To decide whether or not to pursue cryonic suspension we must answer one question: will restoration of frozen tissue to a healthy and functional state ever prove feasible? If the answer is “yes,” then cryonics will save lives. If the answer is “no,” then it can be ignored. As discussed earlier, effectively the most that we can usefully learn about frozen tissue is the type, location and orientation of each molecule. If this information is sufficient to permit inference of the healthy state with memory and personality intact, then repair is in principle feasible. The most that future technology could offer, therefore, is the ability to restore the structure whenever such restoration was feasible in principle. We propose that just this limit will be closely approached by future advances in technology.

It is unreasonable to think that the current proposal will in fact form the basis for future repair methods for two reasons:

First, better technologies and approaches are likely to be developed. Necessarily, we must restrict ourselves to methods and techniques that can be analyzed and understood using the currently understood laws of physics and chemistry. Future scientific advances, not anticipated at this time, are likely to result in cheaper, simpler or more reliable methods. Given the history of science and technology to date, the probability of future unanticipated advances is good.

Second, this proposal was selected because of its conceptual simplicity and its obvious power to restore virtually any structure where restoration is in principle feasible. These are unlikely to be design objectives of future systems. Conceptual simplicity is advantageous when the resources available for the design process are limited. Future design capabilities can reasonably be expected to outstrip current capabilities, and the efforts of a large group can reasonably be expected to allow analysis of much more complex proposals than considered here.

Further, future systems will be designed to restore specific individuals suffering from specific types of damage, and can therefore use specific methods that are less general but which are more efficient or less costly for the particular type of damage involved. It is easier for a general-purpose proposal to rely on relatively simple and powerful methods, even if those methods are less efficient.

Why, then, discuss a powerful, general purpose method that is inefficient, fails to take advantage of the specific types of damage involved, and which will almost certainly be superseded by future technology?

The purpose of this paper is not to lay the groundwork for future systems, but to answer a question: will cryonics work? The value of cryonics is clearly and decisively based on technical capabilities that will not be developed for several decades (or longer). If some relatively simple proposal appears likely to work, then the value of cryonics is established. Whether or not that simple proposal is actually used is irrelevant. The fact that it could be used in the improbable case that all other technical progress and all other approaches fail is sufficient to let us decide today whether or not cryonic suspension is of value.

The philosophical issues involved in this type of long range technical forecasting and the methodologies appropriate to this area are addressed by work in “exploratory engineering.”[1, 85] The purpose of exploratory engineering is to provide lower bounds on future technical capabilities based on currently understood scientific principles. A successful example is Konstantin Tsiolkovsky’s forecast around the turn of the century that multi-staged rockets could go to the moon. His forecast was based on well understood principles of Newtonian mechanics. While it did not predict when such flights would take place, nor who would develop the technology, nor the details of the Saturn V booster, it did predict that the technical capability was feasible and would eventually be developed. In a similar spirit, we will discuss the technical capabilities that should be feasible and what those capabilities should make possible.

Conceptually, the approach that we will follow is simple:

Determine the coordinates and orientations of all major molecules, and store this information in a data base.
Analyze the information stored in the data base with a computer program which determines what changes in the existing structure should be made to restore it to a healthy and functional state.
Take the original molecules and move them, one at a time, back to their correct locations.

The reader will no doubt agree that this proposal is conceptually simple, but might be concerned about a number of technical issues. The major issues are addressed in the following analysis.

An obvious inefficiency of this approach is that it will take apart and then put back together again structures and whole regions that are in fact functional or only slightly damaged. Simply leaving a functional region intact, or using relatively simple special case repair methods for minor damage would be faster and less costly. Despite these obvious drawbacks, the general purpose approach demonstrates the principles involved. As long as the inefficiencies are not so extreme that they make the approach infeasible or uneconomical in the long run, then this simpler approach is easier to evaluate.

Overview of the Brain.

The brain has a volume of 1350 cubic centimeters (about one and a half quarts) and a weight of slightly more than 1400 grams (about three pounds). The smallest normal human brain weighed 1100 grams, while the largest weighed 2050 grams [30, page 24]. It is almost 80% water by weight. The remaining 20% is slightly less than 40% protein, slightly over 50% lipids, and a few percent of other material[16, page 419]. Thus, an average brain has slightly over 100 grams of protein, about 175 grams of lipids, and some 30 to 40 grams of “other stuff”.

How Many Molecules

If we are considering restoration down to the molecular level, an obvious question is: how many molecules are there? We can easily approximate the answer, starting with the proteins. An “average” protein molecule has a molecular weight of about 50,000 amu. One mole of “average” protein is 50,000 grams (by definition), so the 100 grams of protein in the brain is 100/50,000 or .002 moles. One mole is 6.02 x 10²³ molecules, so .002 moles is 1.2 x 10²¹ molecules.

We proceed in the same way for the lipids (lipids are most often used to make cell membranes) — a “typical” lipid might have a molecular weight of 500 amu, which is 100 times less than the molecular weight of a protein. This implies the brain has about 175/500 x 6.02 x 10²³ or about 2 x 10²³ lipid molecules.

Finally, water has a molecular weight of 18, so there will be about 1400 x 0.8/18 x 6.02 x 10²³ or about 4 x 10²⁵ water molecules in the brain. In many cases a substantial percentage of water will have been replaced with cryoprotectant during the process of suspension; glycerol at a concentration of 4 molar or more, for example. Both water and glycerol will be treated in bulk, and so the change from water molecules to glycerol (or other cryoprotectants) should not have a significant impact on the calculations that follow.

These numbers are fundamental. Repair of the brain down to the molecular level will require that we cope with them in some fashion.

How Much Time

Another parameter whose value we must decide is the amount of repair time per molecule. We assume that such repair time includes the time required to determine the location of the molecule in the frozen tissue and the time required to restore the molecule to its correct location, as well as the time to diagnose and repair any structural defects in the molecule. The computational power required to analyze larger-scale structural damage — e.g., this mitochondria has suffered damage to its internal membrane structure (so called “flocculent densities”) — should be less than the power required to analyze each individual molecule. An analysis at the level of sub-cellular organelles involves several orders of magnitude fewer components and will therefore require correspondingly less computational power. Analysis at the cellular level involves even fewer components. We therefore neglect the time required for these additional computational burdens. The total time required for repair is just the sum over all molecules of the time required by one repair device to repair that molecule divided by the number of repair devices. The more repair devices there are, the faster the repair will be. The more molecules there are, and the more time it takes to repair each molecule, the slower repair will be.

The time required for a ribosome to manufacture a protein molecule of 400 amino acids is about 10 seconds[14, page 393], or about 25 milliseconds to add each amino acid. DNA polymerase III can add an additional base to a replicating DNA strand in about 7 milliseconds[14, page 289]. In both cases, synthesis takes place in solution and involves significant delays while the needed components diffuse to the reactive sites. The speed of assembler-directed reactions is likely to prove faster than current biological systems. The arm of an assembler should be capable of making a complete motion and causing a single chemical transformation in about a microsecond [85]. However, we will conservatively base our computations on the speed of synthesis already demonstrated by biological systems, and in particular on the slower speed of protein synthesis.

We must do more than synthesize the required molecules — we must analyze the existing molecules, possibly repair them, and also move them from their original location to the desired final location. Existing antibodies can identify specific molecular species by selectively binding to them, so identifying individual molecules is feasible in principle. Even assuming that the actual technology employed is different it seems unlikely that such analysis will require substantially longer than the synthesis time involved, so it seems reasonable to multiply the synthesis time by a factor of a few to provide an estimate of time spent per molecule. This should, in principle, allow time for the complete disassembly and reassembly of the selected molecule using methods no faster than those employed in biological systems. While the precise size of this multiplicative factor can reasonably be debated, a factor of 10 should be sufficient. The total time required to simply move a molecule from its original location to its correct final location in the repaired structure should be smaller than the time required to disassemble and reassemble it, so we will assume that the total time required for analysis, repair and movement is 100 seconds per protein molecule.

Temperature of Analysis

Warming the tissue before determining its molecular structure creates definite problems: everything will move around. A simple solution to this problem is to keep the tissue frozen until after all the desired structural information is recovered. In this case the analysis will take place at a low temperature. Whether or not subsequent operations should be performed at the same low temperature is left open. A later section considers the various approaches that can be taken to restore the structure after it has been analyzed.

Repair or Replace?

In practice, most molecules will probably be intact — they would not have to be either disassembled or reassembled. This should greatly reduce repair time. On a more philosophical note, existing biological systems generally do not bother to repair macromolecules (a notable exception is DNA — a host of molecular mechanisms for the repair of this molecule are used in most organisms). Most molecules are generally used for a period of time and then broken down and replaced. There is a slow and steady turnover of molecular structure — the atoms in the roast beef sandwich eaten yesterday are used today to repair and replace muscles, skin, nerve cells, etc. If we adopted nature’s philosophy we would simply discard and replace any damaged molecules, greatly simplifying molecular “repair”.

Carried to its logical conclusion, we would discard and replace all the molecules in the structure. Having once determined the type, location and orientation of a molecule in the original (frozen) structure, we would simply throw that molecule out without further examination and replace it. This requires only that we be able to identify the location and type of individual molecules. It would not be necessary to determine if the molecule was damaged, nor would it be necessary to correct any damage found. By definition, the replacement molecule would be taken from a stock-pile of structurally correct molecules that had been previously synthesized, in bulk, by the simplest and most economical method available.

Discarding and replacing even a few atoms might disturb some people. This can be avoided by analyzing and repairing any damaged molecules. However, for those who view the simpler removal and replacement of damaged molecules as acceptable, the repair process can be significantly simplified. For purposes of this paper, however, we will continue to use the longer time estimate based on the premise that full repair of every molecule is required. This appears to be conservative. (Those who feel that replacing their atoms will change their identity should think carefully before eating their next meal!)

Total Repair Machine Seconds

We shall assume that the repair time for other molecules is similar per unit mass. That is, we shall assume that the repair time for the lipids (which each weigh about 500 amu, 100 times less than a protein) is about 100 times less than the repair time for a protein. The repair time for one lipid molecule is assumed to be 1 second. We will neglect water molecules in this analysis, assuming that they can be handled in bulk.

We have assumed that the time required to analyze and synthesize an individual molecule will dominate the time required to determine its present location, the time required to determine the appropriate location it should occupy in the repaired structure, and the time required to put it in this position. These assumptions are plausible but will be considered further when the methods of gaining access to and of moving molecules during the repair process are considered.

This analysis accounts for the bulk of the molecules — it seems unlikely that other molecular species will add significant additional repair time.

Based on these assumptions, we find that we require 100 seconds x 1.2 x 10²¹ protein molecules + 1 second times 2 x 10²³ lipids, or 3.2 x 10²³ repair-machine-seconds. This number is not as fundamental as the number of molecules in the brain. It is based on the (probably conservative) assumption that repair of 50,000 amu requires 100 seconds. Faster repair would imply repair could be done with fewer repair machines, or in less time.

How Many Repair Machines

If we now fix the total time required for repair, we can determine the number of repair devices that must function in parallel. We shall rather arbitrarily adopt 10⁸ seconds, which is very close to three years, as the total time in which we wish to complete repairs.

If the total repair time is 10⁸ seconds, and we require 3.2 x 10²³ repair-machine-seconds, then we require 3.2 x 10¹⁵ repair machines for complete repair of the brain. This corresponds to 3.2 x 10¹⁵ / (6.02 x 10²³) or 5.3 x 10^-9 moles, or 5.3 nanomoles of repair machines. If each repair device weighs 10⁹ to 10¹⁰ amu, then the total weight of all the repair devices is 5.3 to 53 grams: a a few ounces at most.

Thus, the weight of repair devices required to repair each and every molecule in the brain, assuming the repair devices operate no faster than current biological methods, is about 0.4% to 4% of the total mass of the brain.

By way of comparison, there are about 10¹⁴ cells[44, page 3] in the human body and each cell has about 10⁷ ribosomes[14, page 652] giving 10²¹ ribosomes. Thus, there are about six orders of magnitude more ribosomes in the human body than the number of repair machines we estimate are required to repair the human brain.

It seems unlikely that either more or larger repair devices are inherently required. However, it is comforting to know that errors in these estimates of even several orders of magnitude can be easily tolerated. A requirement for 530 kilograms of repair devices (10,000 to 100,000 times more than we calculate is needed) would have little practical impact on feasibility. Although repair scenarios that involve deployment of the repair devices within the volume of the brain could not be used if we required 530 kilograms of repair devices, a number of other repair scenarios would still work — one such approach is discussed in this paper. Given that nanotechnology is feasible, manufacturing costs for repair devices will be small. The cost of even 530 kilograms of repair devices should eventually be significantly less than a few hundred dollars. The feasibility of repair down to the molecular level is insensitive to even large errors in the projections given here.

THE REPAIR PROCESS

We now turn to the physical deployment of these repair devices. That is, although the raw number of repair devices is sufficient, we must devise an orderly method of deploying these repair devices so they can carry out the needed repairs.

Other Proposals: On-board Repair

We shall broadly divide repair scenarios into two classes: on-board and off-board. In the on-board scenarios, the repair devices are deployed within the volume of the brain. Existing structures are disassembled in place, their component molecules examined and repaired, and rebuilt on the spot. (We here class as “on-board” those scenarios in which the repair devices operate within the physical volume of the brain, even though there might be substantial off-board support. That is, there might be a very large computer outside the tissue directing the repair process, but we would still refer to the overall repair approach as “on-board”). The on-board repair scenario has been considered in some detail by Drexler[18]. We will give a brief outline of the on-board repair scenario here, but will not consider it in any depth. For various reasons, it is quite plausible that on-board repair scenarios will be developed before off-board repair scenarios.

The first advantage of on-board repair is an easier evolutionary path from partial repair systems deployed in living human beings to the total repair systems required for repair of the more extensive damage found in the person who has been cryonically suspended. That is, a simple repair device for finding and removing fatty deposits blocking the circulatory system could be developed and deployed in living humans[2], and need not deal with all the problems involved in total repair. A more complex device, developed as an incremental improvement, might then repair more complex damage (perhaps identifying and killing cancer cells) again within a living human. Once developed, there will be continued pressure for evolutionary improvements in on-board repair capabilities which should ultimately lead to repair of virtually arbitrary damage. This evolutionary path should eventually produce a device capable of repairing frozen tissue.

It is interesting to note that “At the end of this month [August 1990], MITI’s Agency of Industrial Science and Technology (AIST) will submit a budget request for ´30 million ($200,000) to launch a `microrobot’ project next year, with the aim of developing tiny robots for the internal medical treatment and repair of human beings. … MITI is planning to pour ´25,000 million ($170 million) into the microrobot project over the next ten years…”[86]. Iwao Fujimasa said their objective is a robot less than .04 inches in size that will be able to travel through veins and inside organs[17, 20]. While substantially larger than the proposals considered here, the direction of future evolutionary improvements should be clear.

A second advantage of on-board repair is emotional. In on- board repair, the original structure (you) is left intact at the macroscopic and even light microscopic level. The disassembly and reassembly of the component molecules is done at a level smaller than can be seen, and might therefore prove less troubling than other forms of repair in which the disassembly and reassembly processes are more visible. Ultimately, though, correct restoration of the structure is the overriding concern.

A third advantage of on-board repair is the ability to leave functional structures intact. That is, in on-board repair we can focus on those structures that are damaged, while leaving working structures alone. If minor damage has occurred, then an on-board repair system need make only minor repairs.

The major drawback of on-board repair is the increased complexity of the system. As discussed earlier, this is only a drawback when the design tools and the resources available for the design are limited. We can reasonably presume that future design tools and future resources will greatly exceed present efforts. Developments in computer aided design of complex systems will put the design of remarkably complex systems within easy grasp.

In on-board repair, we might first logically partition the volume of the brain into a matrix of cubes, and then deploy each repair device in its own cube. Repair devices would first get as close as possible to their assigned cube by moving through the circulatory system (we presume it would be cleared out as a first step) and would then disassemble the tissue between them and their destination. Once in position, each repair device would analyze the tissue in its assigned volume and perform any repairs required.

The Current Proposal: Off-Board Repair

The second class of repair scenarios, the off-board scenarios, allow the total volume of repair devices to greatly exceed the volume of the human brain.

The primary advantage of off-board repair is conceptual simplicity. It employees simple brute force to insure that a solution is feasible and to avoid complex design issues. As discussed earlier, these are virtues in thinking about the problem today but are unlikely to carry much weight in the future when an actual system is being designed.

The other advantages of this approach are fairly obvious. Lingering concerns about volume and heat dissipation can be eliminated. If a ton of repair devices should prove necessary, then a ton can be provided. Concerns about design complexity can be greatly reduced. Off-board repair scenarios do not require that the repair devices be mobile — simplifying communications and power distribution, and eliminating the need for locomotor capabilities and navigational abilities. The only previous paper on off-board repair scenarios was by Merkle[101].

Off-board repair scenarios can be naturally divided into three phases. In the first phase, we must analyze the structure to determine its state. The primary purpose of this phase is simply to gather information about the structure, although in the process the disassembly of the structure into its component molecules will also take place. Various methods of gaining access to and analyzing the overall structure are feasible — in this paper we shall primarily consider one approach.

We shall presume that the analysis phase takes place while the tissue is still frozen. While the exact temperature is left open, it seems preferable to perform analysis prior to warming. The thawing process itself causes damage and, once thawed, continued deterioration will proceed unchecked by the mechanisms present in healthy tissue. This cannot be tolerated during a repair time of several years. Either faster analysis or some means of blocking deterioration would have to be used if analysis were to take place after warming. We will not explore these possibilities here (although this appears worthwhile). The temperature at which other phases takes place is left open.

The second phase of off-board repair is determination of the healthy state. In this phase, the structural information derived from the analysis phase is used to determine what the healthy state of the tissue had been prior to suspension and any preceding illness. This phase involves only computation based on the information provided by the analysis phase.

The third phase is repair. In this phase, we must restore the structure in accordance with the blue-print provided by the second phase, the determination of the healthy state.

Intermediate States During Off-Board Repair

Repair methods in general start with frozen tissue, and end with healthy tissue. The nature of the intermediate states characterizes the different repair approaches. In off-board repair the tissue undergoing repair must pass through three highly characteristic states, described in the following three paragraphs.

The first state is the starting state, prior to any repair efforts. The tissue is frozen (unrepaired).

In the second state, immediately following the analysis phase, the tissue has been disassembled into its individual molecules. A detailed structural data base has been built which provides a description of the location, orientation, and type of each molecule, as discussed earlier. For those who are concerned that their identity or “self” is dependent in some fundamental way on the specific atoms which compose their molecules, the original molecules can be retained in a molecular “filing cabinet.” While keeping physical track of the original molecules is more difficult technically, it is feasible and does not alter off-board repair in any fundamental fashion.

In the third state, the tissue is restored and fully functional.

By characterizing the intermediate state which must be achieved during the repair process, we reduce the problem from “Start with frozen tissue and generate healthy tissue” to “Start with frozen tissue and generate a structural data base and a molecular filing cabinet. Take the structural data base and the molecular filing cabinet and generate healthy tissue.” It is characteristic of off-board repair that we disassemble the molecular structure into its component pieces prior to attempting repair.

As an example, suppose we wish to repair a car. Rather than try and diagnose exactly what’s wrong, we decide to take the car apart into its component pieces. Once the pieces are spread out in front of us, we can easily clean each piece, and then reassemble the car. Of course, we’ll have to keep track of where all the pieces go so we can reassemble the structure, but in exchange for this bookkeeping task we gain a conceptually simple method of insuring that we actually can get access to everything and repair it. While this is a rather extreme method of repairing a broken carburetor, it certainly is a good argument that we should be able to repair even rather badly damaged cars. So, too, with off-board repair. While it might be an extreme method of fixing any particular form of damage, it provides a good argument that damage can be repaired under a wide range of circumstances.

Off-Board Repair is the Best that can be Achieved

Regardless of the initial level of damage, regardless of the functional integrity or lack thereof of any or all of the frozen structure, regardless of whether easier and less exhaustive techniques might or might not work, we can take any frozen structure and convert it into the canonical state described above. Further, this is the best that we can do. Knowing the type, location and orientation of every molecule in the frozen structure under repair and retaining the actual physical molecules (thus avoiding any philosophical objections that replacing the original molecules might somehow diminish or negate the individuality of the person undergoing repair) is the best that we can hope to achieve. We have reached some sort of limit with this approach, a limit that will make repair feasible under circumstances which would astonish most people today.

One particular approach to off-board repair is divide-and- conquer. This method is one of the technically simplest approaches. We discuss this method in the following section.

Divide-and-Conquer

Divide-and-conquer is a general purpose problem-solving method frequently used in computer science and elsewhere. In this method, if a problem proves too difficult to solve it is first divided into sub-problems, each of which is solved in turn. Should the sub-problems prove too difficult to solve, they are in turn divided into sub-sub-problems. This process is continued until the original problem is divided into pieces that are small enough to be solved by direct methods.

If we apply divide-and-conquer to the analysis of a physical object — such as the brain — then we must be able to physically divide the object of analysis into two pieces and recursively apply the same method to the two pieces. This means that we must be able to divide a piece of frozen tissue, whether it be the entire brain or some smaller part, into roughly equal halves. Given that tissue at liquid nitrogen temperatures is already prone to fracturing, it should require only modest effort to deliberately induce a fracture that would divide such a piece into two roughly equal parts. Fractures made at low temperatures (when the material is below the glass transition temperature) are extremely clean, and result in little or no loss of structural information. Indeed, freeze fracture techniques are used for the study of synaptic structures. Hayat [40, page 398] says “Membranes split during freeze-fracturing along their central hydrophobic plane, exposing intramembranous surfaces. … The fracture plane often follows the contours of membranes and leaves bumps or depressions where it passes around vesicles and other cell organelles. … The fracturing process provides more accurate insight into the molecular architecture of membranes than any other ultrastructural method.” It seems unlikely that the fracture itself will result in any significant loss of structural information.

The freshly exposed faces can now be analyzed by various surface analysis techniques. Work with STMs supports the idea that very high resolution is feasible[46]. For example, optical absorption microscopy “…generates an absorption spectrum of the surface with a resolution of 1 nanometer [a few atomic diameters].” Kumar Wickramasinghe of IBM’s T. J. Watson Research Center said: “We should be able to record the spectrum of a single molecule” on a surface. Williams and Wickramasinghe said [51] “The ability to measure variations in chemical potential also allows the possibility of selectively identifying subunits of biological macromolecules either through a direct measurement of their chemical- potential gradients or by decorating them with different metals. This suggest a potentially simple method for sequencing DNA.” While current devices are large, the fundamental physical principles on which they rely do not require large size. Many of the devices depend primarily on the interaction between a single atom at the tip of the STM probe and the atoms on the surface of the specimen under analysis. Clearly, substantial reductions in size in such devices are feasible.

High resolution optical techniques can also be employed. Near field microscopy, employing light with a wavelength of hundreds of nanometers, has achieved a resolution of 12 nanometers (much smaller than a wavelength of light). To quote the abstract of a recent review article on the subject: “The near-field optical interaction between a sharp probe and a sample of interest can be exploited to image, spectroscopically probe, or modify surfaces at a resolution (down to ~12 nm) inaccessible by traditional far-field techniques. Many of the attractive features of conventional optics are retained, including noninvasiveness, reliability, and low cost. In addition, most optical contrast mechanisms can be extended to the near-field regime, resulting in a technique of considerable versatility. This versatility is demonstrated by several examples, such as the imaging of nanometric-scale features in mammalian tissue sections and the creation of ultrasmall, magneto-optic domains having implications for high-density data storage. Although the technique may find uses in many diverse fields, two of the most exciting possibilities are localized optical spectroscopy of semiconductors and the fluorescence imaging of living cells.”[111]. Another article said: “Our signals are currently of such magnitude that almost any application originally conceived for far-field optics can now be extended to the near-field regime, including: dynamical studies at video rates and beyond; low noise, high resolution spectroscopy (also aided by the negligible auto-fluorescence of the probe); minute differential absorption measurements; magnetooptics; and superresolution lithography.”[100] [note 20] .

How Small are the Pieces

The division into halves continues until the pieces are small enough to allow direct analysis by repair devices. If we presume that division continues until each repair device is assigned its own piece to repair, then there will be both 3.2 x 10¹⁵ repair devices and pieces. If the 1350 cubic centimeter volume of the brain is divided into this many cubes, each such cube would be about .4 microns (422 nanometers) on a side. Each cube could then be directly analyzed (disassembled into its component molecules) by a repair device during our three-year repair period.

One might view these cubes as the pieces of a three- dimensional jig-saw puzzle, the only difference being that we have cheated and carefully recorded the position of each piece. Just as the picture on a jig-saw puzzle is clearly visible despite the fractures between the pieces, so too the three-dimensional “picture” of the brain is clearly visible despite its division into pieces [note 21].

Moving Pieces

There are a great many possible methods of handling the mechanical problems involved in dividing and moving the pieces. It seems unlikely that mechanical movement of the pieces will prove an insurmountable impediment, and therefore we do not consider it in detail. However, for the sake of concreteness, we outline one possibility. Human arms are about 1 meter in length, and can easily handle objects from 1 to 10 centimeters in size (.01 to .1 times the length of the arm). It should be feasible, therefore, to construct a series of progressively shorter arms which handle pieces of progressively smaller size. If each set of arms were ten times shorter than the preceding set, then we would have devices with arms of: 1 meter, 1 decimeter, 1 centimeter, 1 millimeter, 100 microns, 10 microns, 1 micron, and finally .1 microns or 100 nanometers. (Note that an assembler has arms roughly 100 nanometers long). Thus, we would need to design 8 different sizes of manipulators. At each succeeding size the manipulators would be more numerous, and so would be able to deal with the many more pieces into which the original object was divided. Transport and mechanical manipulation of an object would be done by arms of the appropriate size. As objects were divided into smaller pieces that could no longer be handled by arms of a particular size, they would be handed to arms of a smaller size.

If it requires about three years to analyze each piece, then the time required both to divide the brain into pieces and to move each piece to an immobile repair device can reasonably be neglected. It seems unlikely that moving the pieces will take a significant fraction of three years.

Memory Requirements

The information storage requirements for a structural data- base that holds the detailed description and location of each major molecule in the brain can be met by projected storage methods. DNA has an information storage density of about 10²¹ bits/cubic centimeter. Conceptually similar but somewhat higher density molecular “tape” systems that store 10²² bits/cubic centimeter [1] should be quite feasible. If we assume that every lipid molecule is “significant” but that water molecules, simple ions and the like are not, then the number of significant molecules is roughly the same as the number of lipid molecules [note 22] (the number of protein molecules is more than two orders of magnitude smaller, so we will neglect it in this estimate). The digital description of these 2 x 10²³ significant molecules requires 10²⁵ bits (assuming that 50 bits are required to encode the location and description of each molecule). This is about 1,000 cubic centimeters (1 liter, roughly a quart) of “tape” storage. If a storage system of such capacity strikes the reader as infeasible, consider that a human being has about 10¹⁴ cells[44, page 3] and that each cell stores 10¹⁰ bits in its DNA[14]. Thus, every human that you see is a device which (among other things) has a raw storage capacity of 10²⁴ bits — and human beings are unlikely to be optimal information storage devices.

A simple method of reducing storage requirements by several orders of magnitude would be to analyze and repair only a small amount of tissue at a time. This would eliminate the need to store the entire 10²⁵ bit description at one time. A smaller memory could hold the description of the tissue actually under repair, and this smaller memory could then be cleared and re-used during repair of the next section of tissue.

Computational Requirements

The computational power required to analyze a data base with 10²⁵ bits is well within known theoretical limits[9,25,32]. It has been seriously proposed that it might be possible to increase the total computational power achievable within the universe beyond any fixed bound in the distant future[52, page 658]. More conservative lower bounds to nearer-term future computational capabilities can be derived from the reversible rod-logic molecular model of computation, which dissipates about 10^-23 joules per gate operation when operating at 100 picoseconds at room temperature[ 85]. A wide range of other possibilities exist. Likharev proposed a computational element based on Josephson junctions which operates at 4 K and in which energy dissipation per switching operation is 10^-24 joules with a switching time of 10^-9 seconds[33, 43]. Continued evolutionary reductions in the size and energy dissipation of properly designed NMOS[113] and CMOS[112, 120] circuits should eventually produce logic elements that are both very small (though significantly larger than Drexler’s mechanical proposals) and which dissipate extraordinarily small amounts of energy per logic operation. Extrapolation of current trends suggest that energy dissipations in the 10^-23 joule range will be achieved before 2030[31, fig. 1]. There is no presently known reason to expect the trend to stop or even slow down at that time[9,32].

Energy costs appear to be the limiting factor in rod logic (rather than the number of gates, or the speed of operation of the gates). Today, electric power costs about 10 cents per kilowatt hour. Future costs of power will almost certainly be much lower. Molecular manufacturing should eventually sharply reduce the cost of solar cells and increase their efficiency close to the theoretical limits. With a manufacturing cost of under 10 cents per kilogram[ 85] the cost of a one square meter solar cell will be less than a penny. As a consequence the cost of solar power will be dominated by other costs, such as the cost of the land on which the solar cell is placed. While solar cells can be placed on the roofs of existing structures or in otherwise unused areas, we will simply use existing real estate prices to estimate costs. Low cost land in the desert south western United States can be purchased for less than $1,000 per acre. (This price corresponds to about 25 cents per square meter, significantly larger than the projected future manufacturing cost of a one square meter solar cell). Land elsewhere in the world (arid regions of the Australian outback, for example) is much cheaper. For simplicity and conservatism, though, we’ll simply adopt the $1,000 per acre price for the following calculations. Renting an acre of land for a year at an annual price of 10% of the purchase price will cost $100. Incident sunlight at the earth’s surface provides a maximum of 1,353 watts per square meter, or 5.5 x 10⁶ watts per acre. Making allowances for inefficiencies in the solar cells, atmospheric losses, and losses caused by the angle of incidence of the incoming light reduces the actual average power production by perhaps a factor of 15 to about 3.5 x 10⁵ watts. Over a year, this produces 1.1 x 10¹³ joules or 3.1 x 10⁶ kilowatt hours. The land cost $100, so the cost per joule is 0.9 nanocents and the cost per kilowatt hour is 3.3 millicents. Solar power, once we can make the solar cells cheaply enough, will be over several thousand times cheaper than electric power is today. We’ll be able to buy over 10¹⁵ joules for under $10,000.

While the energy dissipation per logic operation estimated by Drexler[ 85] is about 10^-23 joules, we’ll content ourselves with the higher estimate of 10^-22 joules per logic operation. Our 10¹⁵ joules will then power 10³⁷ gate operations: 10¹² gate operations for each bit in the structural data base or 5 x 10¹³ gate operations for each of the 2 x 10²³ lipid molecules present in the brain.

It should be emphasized that in off-board repair warming of the tissue is not an issue because the overwhelming bulk of the calculations and hence almost all of the energy dissipation takes place outside the tissue. Much of the computation takes place when the original structure has been entirely disassembled into its component molecules.

How Much Is Enough?

Is this enough computational power? We can get a rough idea of how much computer power might be required if we draw an analogy from image recognition. The human retina performs about 100 “operations” per pixel, and the human brain is perhaps 1,000 to 10,000 times larger than the retina. This implies that the human image recognition system can recognize an object after devoting some 10⁵ to 10⁶ “operations” per pixel. (This number is also in keeping with informal estimates made by individuals expert in computer image analysis). Allowing for the fact that such “retinal operations” are probably more complex than a single “gate operation” by a factor of 1000 to 10,000, we arrive at 10⁸ to 10¹⁰ gate operations per pixel — which is well below our estimate of 10¹² operations per bit or 5 x 10¹³ operations per molecule.

To give a feeling for the computational power this represents, it is useful to compare it to estimates of the raw computational power of the human brain. The human brain has been variously estimated as being able to do 10¹³[50], 10¹⁵ or 10¹⁶[114] operations a second (where “operation” has been variously defined but represents some relatively simple and basic action) [note 23]. The 10³⁷ total logic operations will support 10²⁹ logic operations per second for three years, which is the raw computational power of something like 10¹³ human beings (even when we use the high end of the range for the computational power of the human brain). This is 10 trillion human beings, or some 2,000 times more people than currently exist on the earth today. By present standards, this is a large amount of computational power. Viewed another way, if we were to divide the human brain into tiny cubes that were about 5 microns on a side (less than the volume of a typical cell), each such cube could receive the full and undivided attention of a dedicated human analyst for a full three years.

The next paragraph analyzes memory costs, and can be skipped without loss of continuity.

This analysis neglects the memory required to store the complete state of these computations. Because this estimate of computational abilities and requirements depends on the capabilities of the human brain, we might require an amount of memory roughly similar to the amount of memory required by the human brain as it computes. This might require about 10¹⁶ bits (10 bits per synapse) to store the “state” of the computation. (We assume that an exact representation of each synapse will not be necessary in providing capabilities that are similar to those of the human brain. At worst, the behavior of small groups of cells could be analyzed and implemented by the most efficient method, e.g., a “center surround” operation in the retina could be implemented as efficiently as possible, and would not require detailed modeling of each neuron and synapse. In point of fact, it is likely that algorithms that are significantly different from the algorithms employed in the human brain will prove to be the most efficient for this rather specialized type of analysis, and so our use of estimates derived from low-level parts-counts from the human brain are likely to be conservative). For 10¹³ programs each equivalent in analytical skills to a single human being, this would require 10²⁹ bits. At 100 cubic nanometers per bit, this gives 10,000 cubic meters. Using the cost estimates provided by Drexler[85] this would be an uncomfortable $1,000,000. We can, however, easily reduce this cost by partitioning the computation to reduce memory requirements. Instead of having 10¹³ programs each able to “think” at about the same speed as a human being, we could have 10¹⁰ programs each able to “think” at a speed 1,000 times faster than a human being. Instead of having 10 trillion dedicated human analysts working for 3 years each, we would have 10 billion dedicated human analysts working for 3,000 virtual years each. The project would still be completed in 3 calendar years, for each computer “analyst” would be a computer program running 1,000 times faster than an equally skilled human analyst. Instead of analyzing the entire brain at once, we would instead logically divide the brain into 1,000 pieces each of about 1.4 cubic centimeters in size, and analyze each such piece fully before moving on to the next piece.

This reduces our memory requirements by a factor of 1,000 and the cost of that memory to a manageable $1,000.

It should be emphasized that the comparisons with human capabilities are used only to illustrate the immense capabilities of 10³⁷ logic operations. It should not be assumed that the software that will actually be used will have any resemblance to the behavior of the human brain.

More Computer Power

In the following paragraphs, we argue that even more computational power will in fact be available, and so our margins for error are much larger.

Energy loss in rod logic, in Likharev’s parametric quantron, in properly designed NMOS and CMOS circuits, and in many other proposals for computational devices is related to speed of operation. By slowing down the operating speed from 100 picoseconds to 100 nanoseconds or even 100 microseconds we should achieve corresponding reductions in energy dissipation per gate operation. This will allow substantial increases in computational power for a fixed amount of energy (10¹⁵ joules). We can both decrease the energy dissipated per gate operation (by operating at a slower speed) and increase the total number of gate operations (by using more gates). Because the gates are very small to start with, increasing their number by a factor of as much as 10¹⁰ (to approximately 10²⁷ gates) would still result in a total volume of 100 cubic meters (recall that each gate plus overhead is about 100 cubic nanometers). This is a cube less than 5 meters on a side. Given that manufacturing costs will eventually reflect primarily material and energy costs, such a volume of slowly operating gates should be economical and would deliver substantially more computational power per joule.

We will not pursue this approach here for two main reasons. First, published analyses use the higher 100 picosecond speed of operation and 10^-22 joules of energy dissipation[ 85]. Second, operating at 10^-22 joules at room temperature implies that most logic operations must be reversible and that less than one logic operation in 30 can be irreversible. Irreversible logic operations (which erase information) must inherently dissipate at least kT x ln(2) for fundamental thermodynamic reasons. The average thermal energy of a single atom or molecule at a temperature T (measured in degrees K) is approximately kT where k is Boltzmann’s constant. At room temperature, kT is about 4 x 10^-21 joules. Thus, each irreversible operation will dissipate almost 3 x 10^-21 joules. The number of such operations must be limited if we are to achieve an average energy dissipation of 10^-22 joules per logic operation.

While it should be feasible to perform computations in which virtually all logic operations are reversible (and hence need not dissipate any fixed amount of energy per logic operation)[9, 25, 32, 53, 112, 120], current computer architectures might require some modification before they could be adapted to this style of operation. By contrast, it should be feasible to use current computer architectures while at the same time performing a major percentage (e.g., 99% or more) of their logic operations in a reversible fashion.

Various electronic proposals show that almost all of the existing combinational logic in present computers can be replaced with reversible logic with no change in the instruction set that is executed[112, 113]. Further, while some instructions in current computers are irreversible and hence must dissipate at least kT x ln(2) joules for each bit of information erased, other instructions are reversible and need not dissipate any fixed amount of energy if implemented correctly. Optimizing compilers could then avoid using the irreversible machine instructions and favor the use of the reversible instructions. Thus, without modifying the instruction set of the computer, we can make most logic operations in the computer reversible.

Further work on reversible computation can only lower the minimum energy expenditure per basic operation and increase the percentage of reversible logic operations. Much greater reductions in energy dissipation might be feasible[105]. While it is at present unclear how far the trend towards lower energy dissipation per logic operation can go, it is clear that we have not yet reached a limit and that no particular limit is yet visible.

We can also expect further decreases in energy costs. By placing solar cells in space the total incident sunlight per square meter can be greatly increased (particularly if the solar cell is located closer to the sun) while at the same time the total mass of the solar cell can be greatly decreased. Most of the mass in earth-bound structures is required not for functional reasons but simply to insure structural integrity against the forces of gravity and the weather. In space both these problems are virtually eliminated. As a consequence a very thin solar cell of relatively modest mass can have a huge surface area and provide immense power at much lower costs than estimated here.

If we allow for the decreasing future cost of energy and the probability that future designs will have lower energy dissipation than 10^-22 joules per logic operation, it seems likely that we will have a great deal more computational power than required. Even ignoring these more than likely developments, we will have adequate computational power for repair of the brain down to the molecular level.

Chemical Energy of the Brain

Another issue is the energy involved in the complete disassembly and reassembly of every molecule in the brain. The total chemical energy stored in the proteins and lipids of the human brain is quite modest in comparison with 10¹⁵ joules. When lipids are burned, they release about 9 kilocalories per gram. (Calorie conscious dieters are actually counting “kilocalories” — so a “300 Calorie Diet Dinner” really has 300,000 calories or 1,254,000 joules). When protein is burned, it releases about 4 kilocalories per gram. Given that there are 100 grams of protein and 175 grams of lipid in the brain, this means there is almost 2,000 kilocalories of chemical energy stored in the structure of the brain, or about 8 x 10⁶ joules. This much chemical energy is over 10⁸ times less than the 10¹⁵ joules that one person can reasonably purchase in the future. It seems unlikely that the construction of the human brain must inherently require substantially more than 10⁷ joules and even more unlikely that it could require over 10¹⁵ joules. The major energy cost in repair down to the molecular level appears to be in the computations required to “think” about each major molecule in the brain and the proper relationships among those molecules.

Determining the Healthy State

In the second phase of the analysis, determination of the healthy state, we determine what the repaired (healthy) tissue should look like at the molecular level. That is, the initial structural data base produced by the analysis phase describes unhealthy (frozen) tissue. In determination of the healthy state, we must generate a revised structural data base that describes the corresponding healthy (functional) tissue. The generation of this revised data base requires a computer program that has an intimate understanding of what healthy tissue should look like, and the correspondence between unhealthy (frozen) tissue and the corresponding healthy tissue. As an example, this program would have to understand that healthy tissue does not have fractures in it, and that if any fractures are present in the initial data base (describing the frozen tissue) then the revised data base (describing the resulting healthy tissue) should be altered to remove them. Similarly, if the initial data base describes tissue with swollen or non-functional mitochondria, then the revised data base should be altered so that it describes fully functional mitochondria. If the initial data base describes tissue which is infected (viral or bacterial infestations) then the revised data base should be altered to remove the viral or bacterial components.

While the revised data base describes the healthy state of the tissue that we desire to achieve, it does not specify the method(s) to be used in restoring the healthy structure. There is in general no necessary implication that restoration will or will not be done at some specific temperature, or will or will not be done in any particular fashion. Any one of a wide variety of methods could be employed to actually restore the specified structure. Further, the actual restored structure might differ in minor details from the structure described by the revised data base.

The complexity of the program that determines the healthy state will vary with the quality of the suspension and the level of damage prior to suspension. Clearly, if cryonic suspension “almost works”, then the initial data base and the revised data base will not greatly differ. Cryonic suspension under favorable circumstances preserves the tissue with good fidelity down to the molecular level. If, however, there was significant presuspension injury then deducing the correct (healthy) structural description is more complex. However, it should be feasible to deduce the correct structural description even in the face of significant damage. Only if the structure is obliterated beyond recognition will it be infeasible to deduce the undamaged state of the structure.

ALTERNATIVES TO REPAIR

A brief philosophical aside is in order. Once we have generated an acceptable revised structural data base, we can in fact pursue either of two distinctly different possibilities. The obvious path is to continue with the repair process, eventually producing healthy tissue. An alternative path is to use the description in the revised structural data base to guide the construction of a different but “equivalent” structure (e.g., an “artificial brain”). This possibility has been much discussed[11, 50], and has recently been called “uploading” (or “downloading”)[26]. Whether or not such a process preserves what is essentially human is often hotly debated, but it has advantages wholly unrelated to personal survival. As an example, the knowledge and skills of an Einstein or Turing need not be lost: they could be preserved in a computational model. On a more commercial level, the creative skills of a Spielberg (whose movies have produced a combined revenue in the billions) could also be preserved. Whether or not the computational model was viewed as having the same essential character as the biological human after which it was patterned, it would indisputably preserve that person’s mental abilities and talents.

It seems likely that many people today will want complete physical restoration (despite the philosophical possibilities considered above) and will continue through the repair planning and repair phases.

RESTORATION

In the third phase of repair we start with an atomically precise description (the revised data base) of the structure that we wish to restore, and a filing cabinet holding the molecules that will be needed during restoration. Optionally, the molecules in the filing cabinet can be from the original structure. This deals with the concerns of those who want restoration with the original atoms. Our objective is to restore the original structure with a precision sufficient to support the original functional capabilities. Clearly, this would be achieved if we were to restore the structure with atomic precision. Before discussing this most technically exacting approach, we will briefly mention the other major approaches that might be employed.

We know it is possible to make a human brain for this has been done by traditional methods for many thousands of years. If we were to adopt a restoration method that was as close as possible to the traditional technique for building a brain, we might use a “guided growth” strategy. That is, in simple organisms the growth of every single cell and of every single synapse is determined genetically. “All the cell divisions, deaths, and migrations that generate the embryonic, then the larval, and finally the adult forms of the roundworm Caenorhabditis Elegans have now been traced.”[103]. “The embryonic lineage is highly invariant, as are the fates of the cells to which it gives rise”[102]. The appendix says: “Parts List: Caenorhabditis elegans (Bristol) Newly Hatched Larva. This index was prepared by condensing a list of all cells in the adult animal, then adding comments and references. A complete listing is available on request…” The adult organism has 959 cells in its body, 302 of which are nerve cells[104].

Restoring a specific biological structure using this approach would require that we determine the total number and precise growth patterns of all the cells involved. The human brain has roughly 10¹² nerve cells, plus perhaps ten times as many glial cells and other support cells. While simply encoding this complex a structure into the genome of a single embryo might prove to be overly complex, it would certainly be feasible to control critical cellular activities by the use of on board nanocomputers. That is, each cell would be controlled by an on-board computer, and that computer would in turn have been programmed with a detailed description of the growth pattern and connections of that particular cell. While the cell would function normally in most respects, critical cellular activities, such as replication, motility, and synapse growth, would be under the direct control of the on-board computer. Thus, as in C. Elegans but on a larger scale, the growth of the entire system would be “highly invariant.” Once the correct final configuration had been achieved, the on-board nanocomputers would terminate their activities and be flushed from the system as waste.

This approach might be criticized on the grounds that the resulting person was a “mere duplicate,” and so “self” had not been preserved. Certainly, precise atomic control of the structure would appear to be difficult to achieve using guided growth, for biological systems do not normally control the precise placement of individual molecules. While the same atoms could be used as in the original, it would seem difficult to guarantee that they would be in the same places.

Concerns of this sort lead to restoration methods that provide higher precision. In these methods, the desired structure is restored directly from molecular components by placing the molecular components in the desired locations. A problem with this approach is the stability of the structure during restoration. Molecules might drift away from their assigned locations, destroying the structure.

An approach that we might call “minimal stabilization” would involve synthesis in liquid water, with mechanical stabilization of the various lipid membranes in the system. A three-dimensional grid or scaffolding would provide a framework that would hold membrane anchors in precise locations. The membranes themselves would thus be prevented from drifting too far from their assigned locations. To prevent chemical deterioration during restoration, it would be necessary to remove all reactive compounds (e.g., oxygen).

In this scenario, once the initial membrane “framework” was in place and held in place by the scaffolding, further molecules would be brought into the structure and put in the correct locations. In many instances, such molecules could be allowed to diffuse freely within the cellular compartment into which they had been introduced. In some instances, further control would be necessary. For example, a membrane- spanning channel protein might have to be confined to a specific region of a nerve cell membrane, and prevented from diffusing freely to other regions of the membrane. One method of achieving this limited kind of control over further diffusion would be to enclose a region of the membrane by a diffusion barrier (much like the spread of oil on water can be prevented by placing a floating barrier on the water).

While it is likely that some further cases would arise where it was necessary to prevent or control diffusion, the emphasis in this method is in providing the minimal control over molecular position that is needed to restore the structure.

While this approach does not achieve atomically precise restoration of the original structure, the kinds of changes that are introduced (diffusion of a molecule within a cellular compartment, diffusion of a membrane protein within the membrane) would be very similar to the kinds of diffusion that would take place in a normal biological system. Thus, the restored result would have the same molecules with the same atoms, and the molecules would be in similar (though not exactly the same) locations they had been in prior to restoration.

To achieve even more precise control over the restored structure, we might adopt a “full stabilization” strategy. In this strategy, each major molecule would be anchored in place, either to the scaffolding or an adjacent molecule. This would require the design of a stabilizing molecule for each specific type of molecule found in the body. The stabilizing molecule would have a specific end attached to the specific molecule, and a general end attached either to the scaffolding or to another stabilizing molecule. Once restoration was complete, the stabilizing molecules would release the molecules that were being stabilized and normal function would resume. This release might be triggered by the simple diffusion of an enzyme that attacked and broke down the stabilizing molecules. This kind of approach was considered by Drexler[1].

Low Temperature Restoration

Finally, we might achieve stability of the intermediate structure by using low temperatures. If the structure were restored at a sufficiently low temperature, a molecule put in a certain place would simply not move. We might call this method “low temperature restoration.”

In this scenario, each new molecule would simply be stacked (at low temperature) in the right location. This can be roughly likened to stacking bricks to build a house. A hemoglobin molecule could simply be thrown into the middle of the half-restored red blood cell. Other molecules whose precise position was not critical could likewise be positioned rather inexactly. Lipids in the lipid bi-layer forming the cellular membrane would have to be placed more precisely (probably with an accuracy of several angstroms). An individual lipid molecule, having once been positioned more or less correctly on a lipid bi-layer under construction, would be held in place (at sufficiently low temperatures) by van der Waals forces. Membrane bound proteins could also be “stacked” in their proper locations. Because biological systems make extensive use of self- assembly it would not be necessary to achieve perfect accuracy in the restoration process. If a biological macromolecule is positioned with reasonable accuracy, it would automatically assume the correct position upon warming.

Large polymers, used either for structural or other purposes, pose special problems. The monomeric units are covalently bonded to each other, and so simple “stacking” is inadequate. If such polymers cannot be added to the structure as entirely pre-formed units, then they could be incrementally restored during assembly from their individual monomers using the techniques discussed earlier involving positional synthesis using highly reactive intermediates. Addition of monomeric units to the polymer could then be done at the most convenient point during the restoration operation.

The chemical operations required to make a polymer from its monomeric units at reduced temperatures are unlikely to use the same reaction pathways that are used by living systems. In particular, the activation energies of most reactions that take place at 310 K (98.6 degrees Fahrenheit) can not be met at 77 K: most conventional compounds don’t react at that temperature. However, as discussed earlier, assembler based synthesis techniques using highly reactive intermediates in near-perfect vacuum with mechanical force providing activation energy will continue to work quite well, even if we assume that thermal activation energy is entirely absent (e.g., that the system is close to 0 Kelvins).

An obvious problem with low temperature restoration is the need to re-warm the structure without incurring further damage. Much “freezing” injury takes place during rewarming, and this would have to be prevented. One solution is discussed in the next two paragraphs.

Generally, the revised structural data base can be further altered to make restoration easier. While certain alterations to the structural data base must be banned (anything that might damage memory, for example) many alterations would be quite safe. One set of safe alterations would be those that correspond to real-world changes that are non-damaging. For example, moving sub-cellular organelles within a cell would be safe — such motion occurs spontaneously in living tissue. Likewise, small changes in the precise physical location of cell structures that did not alter cellular topology would also be safe. Indeed, some operations that might at first appear dubious are almost certainly safe. For example, any alteration that produces damage that can be repaired by the tissue itself once it is restored to a functional state is in fact safe — though we might well seek to avoid such alterations (and they do not appear necessary). While the exact range of alterations that can be safely applied to the structural data base is unclear, it is evident that the range is fairly wide.

An obvious modification which would allow us to re-warm the structure safely would be to add cryoprotectants. Because we are restoring the frozen structure with atomic precision, we could use different concentrations and different types of cryoprotectants in different regions, thus matching the cryoprotectant requirements with exquisite accuracy to the tissue type. This is not feasible with present technology because cryoprotectants are introduced using simple diffusive techniques.

Extremely precise control over the heating rate would also be feasible, as well as very rapid heating[126]. Rapid heating would allow less time for damage to take place. Rapid heating, however, might introduce problems of stress and resulting fractures. Two approaches for the elimination of this problem are (1) modify the structure so that the coefficient of thermal expansion is very small and (2) increase the strength of the structure.

One simple method of insuring that the volume occupied before and after warming was the same (i.e., of making a material with a very small thermal expansion coefficient) would be to disperse many small regions with the opposite thermal expansion tendency throughout the material. For example, if a volume tended to expand upon warming the initial structure could include “nanovacuoles,” or regions of about a nanometer in diameter which were empty. Such regions would be stable at low temperatures but would collapse upon warming. By finely dispersing such nanovacuoles it would be possible to eliminate any tendency of even small regions to expand on heating. Most materials expand upon warming, a tendency which can be countered by the use of nanovacuoles.

Of course, ice has a smaller volume after it melts. The introduction of nanovacuoles would only exacerbate its tendency to shrink upon melting. In this case we could use vitrified H20 rather than the usual crystalline variety. H20 in the vitreous state is disordered (as in the liquid state) even at low temperatures, and has a lower volume than crystalline ice. This eliminates and even reverses its tendency to contract on warming. Vitrified water at low temperature is denser than liquid water at room temperature.

Increasing the strength of the material can be done in any of a variety of ways. A simple method would be to introduce long polymers in the frozen structure. Proteins are one class of strong polymers that could be incorporated into the structure with minimal tissue compatibility concerns. Any potential fracture plane would be criss-crossed by the newly added structural protein, and so fractures would be prevented. By also including an enzyme to degrade this artificially introduced structural protein, it would be automatically and spontaneously digested immediately after warming. Very large increases in strength could be achieved by this method.

By combining (1) rapid, highly controlled heating; (2) atomically precise introduction of cryoprotectants; (3) the controlled addition of small nanovacuoles and regions of vitrified H20 to reduce or eliminate thermal expansion and contraction; and (4) the addition of structural proteins to protect against any remaining thermally induced stresses; the damage that might otherwise occur during rewarming should be completely avoidable.

The proposal that all four methods be used in combination is open to the valid criticism that simpler approaches are likely to suffice, e.g., precise distribution of cryoprotectants coupled with relatively slow warming[131]. The proposals advanced in this paper should not be taken as predictions about what will happen or what will be necessary, but as arguments that certain capabilities will be feasible. A belt is sufficient to hold up a pair of pants. When explaining that it is possible to hold up a pair of pants to someone who has never seen or heard of clothing, it is forgiveable to point out that the combined use of both belt and suspenders should be effective in preventing the pants from falling down.

CONCLUSION

Cryonic suspension can transport a terminally ill patient to future medical technology. The damage done by current freezing methods is likely to be reversible at some point in the future. In general, for cryonics to fail, one of the following “failure criteria” must be met:

Pre-suspension and suspension injury would have to be sufficient to cause information theoretic death. In the case of the human brain, the damage would have to obliterate the structures encoding human memory and personality beyond recognition.
Repair technologies that are clearly feasible in principle based on our current understanding of physics and chemistry would have to remain undeveloped in practice, even after several centuries.

An examination of potential future technologies[ 85] supports the argument that unprecedented capabilities are likely to be developed. Restoration of the brain down to the molecular level should eventually prove technically feasible. Off- board repair utilizing divide-and-conquer is a particularly simple and powerful method which illustrates some of the principles that can be used by future technologies to restore tissue. Calculations support the idea that this method, if implemented, would be able to repair the human brain within about three years. For several reasons, better methods are likely to be developed and used in practice.

Off-board repair consists of three major steps: (1) Determine the coordinates and orientation of each major molecule. (2) Determine a set of appropriate coordinates in the repaired structure for each major molecule. (3) Move them from the former location to the latter. The various technical problems involved are likely to be met by future advances in technology. Because storage times in liquid nitrogen literally extend for several centuries, the development time of these technologies is not critical.

A broad range of technical approaches to this problem are feasible. The particular form of off-board repair that uses divide-and-conquer requires only that (1) tissue can be divided by some means (such as fracturing) which does not itself cause significant loss of structural information; (2) the pieces into which the tissue is divided can be moved to appropriate destinations (for further division or for direct analysis); (3) a sufficiently small piece of tissue can be analyzed; (4) a program capable of determining the healthy state of tissue given the unhealthy state is feasible; (5) that sufficient computational resources for execution of this program in a reasonable time frame are available; and (6) that restoration of the original structure given a detailed description of that structure is feasible.

It is impossible to conclude based on present evidence that either failure criterion is likely to be met.

Further study of cryonics by the technical community is needed. At present, there is a remarkable paucity of technical papers on the subject [note 24]. As should be evident from this paper multidisciplinary analysis is essential in evaluating its feasibility, for specialists in any single discipline have a background which is too narrow to encompass the whole. Given the life-saving nature of cryonics, it would be tragic if it were to prove feasible but was little used.

APPENDIX

Approximate values of interesting numbers. Numbers marked by * are extrapolations based on projected technical capabilities ( nanotechnology and molecular computing).

Volume of the brain:: 1350 cubic centimeters
Weight of the brain:: 1400 grams
Weight of proteins in the brain:: 100 grams
Weight of a ribosome:: 3 x 10⁶ amu
*Weight of a repair machine:: 10⁹ to 10¹⁰ amu
*Length of a repair machine arm:: 100 nanometers
Weight of water in brain:: 1100 grams
Weight of protein in brain:: 100 grams
Weight of lipids in brain:: 175 grams
Weight of “other solids”:: 35 grams
Weight of “typical” protein:: 50,000 amu
Weight of “typical” lipid:: 500 amu
Weight of water molecule:: 18 amu
Weight of carbon atom:: 12 amu
Density of carbon (diamond):: 3.51 grams/cubic centimeter
Number of proteins in brain:: 1.2 x 10²¹
Number of lipid molecules in brain:: 2 x 10²³
Number of water molecules in brain:: 4 x 10²⁵
Time to synthesize a protein:: 10 seconds
*Time to repair one protein molecule:: 100 seconds
*Time to repair one lipid molecule:: 1 second
*Time to repair all brain macromolecules:: 3.2 x 10²³ repair-machine seconds
*Number of repair machines to repair all brain molecules in three years:: 3.2 x 10¹⁵ repair machines
*Weight of that many repair devices:: 5.3 to 53 grams
Number of bits needed to store the molecular structure of the brain:: 10²⁵ bits
*Energy dissipated by a single “rod logic” (gate) operation (including a few percent of irreversible operations):: 10^-22 joules
*Speed of a single “rod logic” (gate) operation:: 100 x 10^-12 seconds
Estimated cost of 10¹⁵ joules of energy generated on earth in the future:: 10,000 dollars
*Number of gate operations 10¹⁵ joules can support:: 10³⁷ gate operations
*Size of a single “lock” (gate) plus overhead (power, etc.):: 100 cubic nanometers
*Volume of gates that can deliver 10³⁷ operations in three years (a larger volume will in fact be required to accommodate cooling requirements):: 1 cubic centimeter
Power of 10¹⁵ joules dissipated over a three year period:: 10 megawatts (100,000 light bulbs for three years)
Chemical energy stored in the structure of the brain:: 8 x 10⁶ joules (2,000 kilocalories)
Boltzmann’s constant k:: 1.38 x 10^-23 joules/Kelvin
Approximate thermal energy of one atom at room temperature (kT at 300 degrees K):: 4.14 x 10^-21 joules
One watt:: one joule per second
One kilowatt hour:: 3.6 x 10⁶ joules
Avogadro’s number (the number of atoms in one mole):: 6.0221367 x 10²³
One mole of a substance:: that quantity of the substance that weighs (in grams) the same as its molecular weight
amu (atomic mass units):: By definition, one atom of carbon 12 weighs 12 amu
Joules per (dietary) Calorie:: 4,186

ACKNOWLEDGMENTS

It is the authors pleasant duty to acknowledge the many people who have commented on or encouraged the work on this paper as it evolved. The reviewers were not selected because of their opinions about cryonics: some support it, some don’t, and some reserve final judgment. While the quality of the result could not have been achieved without their help, the author must accept responsibility for any errors in the final version. The author would like to thank: Dave Biegelsen, Arthur C. Clarke, Mike Darwin, Thomas Donaldson, Eric Drexler, Greg Fahy, Steve Harris, Leonard Hayflick, Hugh Hixon, Peter Mazur, Mark Miller, David Pegg, Chris Peterson, Ed Regis, Paul Segall, Len Shar, Irwin Sobel, Jim Southard, Jim Stevens and Leonard Zubkoff.

REFERENCES

1. “Engines of Creation” by K. Eric Drexler, Anchor Press, 1986.
2. “Nanotechnology: wherein molecular computers control tiny circulatory submarines”, by A. K. Dewdney, Scientific American, January 1988, pages 100 to 103.
3. “Foresight Update”, a publication of the Foresight Institute, Box 61058, Palo Alto, CA 94306.
4. “There’s Plenty of Room at the Bottom” a talk by Richard Feynman (awarded the Nobel Prize in Physics in 1965) at an annual meeting of the American Physical Society given on December 29, 1959. Reprinted in “Miniaturization”, edited by H. D. Gilbert (Reinhold, New York, 1961) pages 282-296.
5. “Principles of Tissue Preservation” by David Pegg, chapter 4, pages 69 to 105 from “Progress in Transplantation”, Volume Two, edited by Peter J. Morris and Nicholas L. Tilney.
6. “Freezing of living cells: mechanisms and implications”, by Peter Mazur, American Journal of Physiology, Vol 247 (Cell Physiology 16): C125-C142, 1984.
7. “Scanning Tunneling Microscopy and Atomic Force Microscopy: Application to Biology and Technology” by P. K. Hansma, V. B. Elings, O. Marti, and C. E. Bracker. Science, October 14 1988, page 209-216.
8. “Molecular manipulation using a tunnelling microscope,” by J. S. Foster, J. E. Frommer and P. C. Arnett. Nature, Vol. 331 28 January 1988, pages 324-326.
9. “The fundamental physical limits of computation” by Charles H. Bennet and Rolf Landauer, Scientific American Vol. 253, July 1985, pages 48-56.
10. “Molecular Engineering: An Approach to the Development of General Capabilities for Molecular Manipulation,” by K. Eric Drexler, Proceedings of the National Academy of Sciences (USA), Vol 78, pp 5275-78, 1981.
11. “The Mind’s I”, by Douglas R. Hofstadter and Daniel C. Dennett. Bantam, 1982.
12. “Matter and Consciousness: A Contemporary Introduction to the Philosophy of Mind”, by Paul Churchland, second edition, 1988, the MIT press.
13. “Principles of Neural Science”, third edition, by Eric R. Kandel, James H. Schwartz and Thomas M. Jessel. Elsevier, 1991.
14. “Molecular Biology of the Gene”, fourth edition, by James D. Watson, Nancy H. Hopkins, Jeffrey W. Roberts, Joan Argetsinger Steitz, and Alan M. Weiner. Benjamin Cummings, 1987. It can now be purchased as a single large volume.
15. “Death, Dying, and the Biological Revolution”, revised edition, by Robert M. Veatch, Yale University Press 1989.
16. “Human Biochemistry” by James M. Orten, Tenth Edition, Mosby 1982.
17. “Tiny surgical robot being developed”, San Jose Mercury News, Feb. 18, 1989, page 26A
18. Eric Drexler, private communication.
19. “Rod Logic and Thermal Noise in the Mechanical Nanocomputer”, by K. Eric Drexler, Proceedings of the Third International Symposium on Molecular Electronic Devices, F. Carter ed., Elsevier 1988.
20. “Submarines small enough to cruise the bloodstream”, in Business Week, March 27 1989, page 64.
21. “Calcium and Ischemic Injury”, by Joseph Y. Cheung et. al., The New England Journal of Medicine, Vol. 314 No. 26 page 1670.
22. “Differential Rearing Effects on Rat Visual Cortex Synapses. I. Synaptic and Neuronal Density and Synapses per Neuron” by Anita M. Turner and William T. Greenough, Brain Research, 329 (1985) pages 195-203.
23. “Cryonics: reaching for tomorrow”, by Brian Wowk and Michael Darwin, Alcor, 1991, 104 pages. Available from the Alcor Life Extension Foundation, Scottsdale AZ, , 800-367-2228.
24. “Many are cold, but few are frozen: A Humanist looks at Cryonics”, by Steven B. Harris, M.D., Free Inquiry, Vol. 9 No. 2, Spring 1989, pages 19-24.
25. “Conservative Logic”, by Edward Fredkin and Tommaso Toffoli, International Journal of Theoretical Physics, Vol. 21 Nos. 3/4, 1982, pages 219-253.
26. “The Tomorrow Makers”, Grant Fjermedal, MacMillan 1986.
27. “Computer analysis of neuronal structures” by Robert D. Lindsay, Plenum 1977.
28. “The microcomputer in cell and neurobiology research” by R. Ranney Mize, Elsevier 1985.
29. “Intracellular control of axial shape in non-uniform neurites: a serial electron microscopic analysis of organelles and microtubules in AI and AII retinal amacrine neurites”, by Sharon E. Sasaki-Sherrington, J. Roger Jacobs, John K. Stevens, Journal of Cell Biology 98, April 1984, 1279-1290
30. “Guinness Book of World Records,” Donald McFarlan et. al., Bantam 1989.
31. “Dissipation and noise immunity in computation and communication” by Rolf Landauer, Nature, Vol. 335, October 27 1988, page 779.
32. “Notes on the History of Reversible Computation” by Charles H. Bennett, IBM Journal of Research and Development, Vol. 32, No. 1, January 1988.
33. “Classical and Quantum Limitations on Energy Consumption in Computation” by K. K. Likharev, International Journal of Theoretical Physics, Vol. 21, Nos. 3/4, 1982.
34. “Synapses, Circuits, and the Beginnings of Memory,” by Gary Lynch, MIT press 1986.
35. “Memory Storage and Neural Systems,” by Daniel L. Alkon, Scientific American, July 1989, pages 42-50.
36. “Morphological basis of long-term habituation and sensitization in Aplysia” by Craig H. Bailey and Mary Chen, Science 220, April 1, 1983, pages 91-93
37. “How Much Do People Remember? Some Estimates of the Quantity of Learned Information in Long-term Memory,” by Thomas K. Landauer, in Cognitive Science 10, 477-493, 1986
38. “Neurobiology,” by Gordon M. Shepherd, Oxford 1983.
39. “The anatomy of a memory: convergence of results across a diversity of tests,” by William T. Greenough and Craig H. Bailey, Trends in Neuroscience, 1988, Vol. 11, No. 4, pages 142-147.
40. “Principles and Techniques of Electron Microscopy: Biological Applications,” Third edition, by M. A. Hayat, CRC Press, 1989.
41. “Machines of Inner Space” by K. Eric Drexler, 1990 Yearbook of Science and the Future, pages 160-177, published by Encyclopedia Britannica, Chicago 1989.
42. “Stopping Biological Time: The Freezing of Living Cells” by Peter Mazur, Ann. N.Y. Acad. Sci. 541: 514-531, 1988.
43. “Reversible Conveyer Computation in Array of Parametric Quantrons” by K. K. Likharev, S. V. Rylov, and V. K. Semenov, IEEE Transactions on Magnetics, Vol. 21 No. 2, March 1985, pages 947-950
44. “Basic Human Physiology: Normal Function and Mechanisms of Disease” by Arthur Guyton, M.D., Saunders 1971.
45. “Theory of Self Reproducing Automata” by John Von Neumann, edited by Arthur W. Burks, University of Illinois Press, 1966.
46. “The Children of the STM” by Robert Pool, Science, Feb. 9, 1990, pages 634-636.
47. “A Small Revolution Gets Under Way,” by Robert Pool, Science, Jan. 5 1990.
48. “Advanced Automation for Space Missions“, Proceedings of the 1980 NASA/ASEE Summer Study, edited by Robert A. Freitas, Jr. and William P. Gilbreath. Available from NTIS, U.S. Department of Commerce, National Technical Information Service, Springfield, VA 22161; telephone 703-487-4650, order no. N83-15348
49. “Positioning Single Atoms with a Scanning Tunnelling Microscope,” by D. M. Eigler and E. K. Schweizer, Nature Vol 344, April 5 1990, page 524-526.
50. “Mind Children” by Hans Moravec, Harvard University Press, 1988.
51. “Microscopy of Chemical-Potential Variations on an Atomic Scale” by C.C. Williams and H.K. Wickramasinghe, Nature, Vol 344, March 22 1990, pages 317-319.
52. “The Anthropic Cosmological Principle” by John D. Barrow and Frank J. Tipler, Oxford University Press, 1988.
53. “Time/Space Trade-Offs for Reversible Computation” by Charles H. Bennett, SIAM J. Computing, Vol. 18, No. 4, pages 766-776, August 1989.
. “Large Scale Analysis of Neural Structures,” by Ralph C. Merkle, Xerox Technical Report CSL-89-10, November 1989. Available from: Xerox Corporation, Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA 94304.
55. “Cerebral Hypothermia and Circulatory Arrest,” by Robert J. White, Mayo Clinic Proceedings, Vol. 53, July 1978, page 452.
56. “Postmortem examination of three cryonic suspension patients,” by Michael Darwin, Hugh Hixon, and Jerry Leaf. Available from Alcor.
57. “Frozen and Alive”, by Kenneth B. Storey and Janet M. Storey, Scientific American, Vol. 263 No. 6, Dec. 1990, pages 92-97.
58. “Viability of Long Term Frozen Cat Brain In Vitro” by Isamu Suda, K. Kito and C. Adachi, Nature Vol. 212, October 15, 1966 page 268.
59. “Analysis of “solution effects” injury: rabbit renal cortex frozen in the presence of dimethyl sulfoxide” by Gregory M. Fahy, Cryobiology 17:371-388, 1980.
60. “The Etiology, Concept, and Prophylaxis of Childbed Fever” by Ignaz Semmelweis, translated by K. Codell Carter, University of Wisconsin Press, 1983.
61. Alcor has chilled and re-warmed over ten dogs to 0 degrees to 4 degrees C with full recovery in almost all cases.
62. “Studies on golden hamsters during cooling to and rewarming from body temperatures below 0 degrees C. III. Biophysical aspects and general discussion,” by J. E. Lovelock and A. U. Smith, Proc. Royal Society, Biology (Lond) Series B 145:427-442, 1956.
64. “Frozen Dreams: A Matter of Life and Death” by Cynthia Gorney, Washington Post, May 1, 1990.
65. “Cellular and Subcellular Alterations of Human CNS: Studies Utilizing in Situ Perfusion Fixation at Immediate Autopsy” by Hannu Kalimo, Julio H. Garcia, Yoshinari Kamijyo, Junichi Tanaka, Jesus E. Viloria, Jon M. Valigorsky, Raymond T. Jones, Kook M. Kim, Wolfgang J. Mergner, Robert E Pendergrass, Benjamin F. Trump. Arch. Pathol. Vol 97, June 1974, pages 352-359.
66. “Revival of Mammals from body temperatures below zero,” by A. U. Smith. In: Biological Effects of Freezing and Supercooling by A. U. Smith. Edward Arnold, London, 1961, pages 304-368.
67. “Fixation for Electron Microscopy” by M. A. Hayat, Academic Press, 1981.
68. “The Biophysics of Organ Cryopreservation” by David E. Pegg and Armand M. Karow, Jr., Plenum, 1987.
69. “Calcium blockers given after CPR may save brains denied blood up to an hour,” Medical World News, January 18, 1982, pages 11-13.
70. “Metabolic Arrest and the Control of Biological Time,” by Peter W. Hochachka and Michael Guppy, Harvard 1987.
71. “Vitrification: a New Approach to Organ Crypreservation,” by Gregory M. Fahy, pages 305-335, in “Transplantation: Approaches to Graft Rejection”, edited by H.T. Meryman, 1986.
72. “Cell Death in Biology and Pathology” edited by I. D. Bowen and R. A. Lockshin, Chapman and Hall, 1981.
73. “The Relevance of Cryoprotectant `Toxicity’ to Cryobiology” by Gregory M. Fahy, Cryobiology 23, 1-13, 1986.
74. “The Ultrastructure of `Brain Death’, II. Electron Microscopy of Feline Cortex after Complete Ischemia,” by Hannu Kalimo, Julio H. Garcia, Yoshinari Kamijyo, Junichi Tanaka, and Benjamin F. Trump, Virchows Archiv B Cell Path. 25, 207-220 (1977).
75. “Lysosome and Phagosome Stability in Lethal Cell Injury” by Hal K. Hawkins, Jan L. E. Ericsson, Peter Biberfeld, and Benjamin F. Trump. Am J. Pathol. 68:255-288, 1972.
76. “The isolation and in Vitro translation of Undegraded Messenger RNAs from Human Postmortem Brain,” by Marcelle R. Morrison and W. Sue T. Griffin, Analytical Biochemistry 113, 318-324 (1981).
77. “Human Brain in Tissue Culture: Acquisition, Initial Processing, and Establishment of Brain Cell Cultures” by Donald H. Gilden et. al., J. Comp. Neur., 161: 295-306.
78. “Brain States: Death, Vegetation, and Life” by Julius Korein, pages 293-351, chapter 15, from “Anesthesia and Neurosurgery”, 2nd Edition, Cottrell and Turndorf, 1986.
79. “Bioelectric discharges of isolated cat brain after revival from years of frozen storage,” by I. Suda, K. Kito, and C. Adachi. Brain Research 70:527-531, 1974.
80. “The Prospect of Immortality,” by Robert C. W. Ettinger, Sidgwick and Jackson, 1965.
81. “Chloroplast DNA sequence from a Miocene Magnolia species,” by Edward M. Golenberg, David E. Giannasi, Michael T. Clegg, Charles J. Smiley, Mary Durbin, David Henderson, and Gerard Zurawski, Nature, Vol 344, April 12 1990, page 656.
82. “The Cryobiological Case for Cryonics,” from Cryonics, March 1988. Reprints available from The Alcor Life Extension Foundation, Scottsdale AZ, , 800-367-2228.
83. “Nonexistent technology gets a hearing,” by I. Amato, Science News, Vol. 136, November 4, 1989, page 295.
84. “The Invisible Factory,” The Economist, December 9, 1989, page 91.
85. “Nanosystems: Molecular Machinery, Manufacturing and Computation,” by K. Eric Drexler, John Wiley 1992.
86. “MITI heads for inner space” by David Swinbanks, Nature, Vol 346, August 23 1990, page 688-689.
87. “Cerebral Death” by A. Earl Walker, Second Edition, Urban and Schwarzenberg 1981.
88. “Fundamentals of Physics,” Third Edition Extended, by David Halliday and Robert Resnick, Wiley 1988.
89. “General Chemistry” Second Edition, by Donald A. McQuarrie and Peter A. Rock, Freeman 1987.
90. “A Possible Cure for Death” by Charles B. Olson, Medical Hypotheses 1988 Vol 26, pages 77-84.
92. “American College of Physicians Ethics Manual. Part II: Research, Other Ethical Issues. Recommended Reading.” by the Ad Hoc Committee on Medical Ethics, American College of Physicians; Annals of Internal Medicine, July 1984; Vol. 101 No. 2, pages 263-267.
93. “Neurochemical Determinants of Ischemic Cell Damage” by Carl-Henrik Nordstrom, M.D. and Bo K. Siesjo, M.D., pages 49- 66; from “Protection of the Brain from Ischemia” edited by Philip R. Weinstein and Alan A. Faden, Williams and Wilkens 1990.
94. “Hemodynamics of Postischemic Reperfusion of the Brain,” by K.-A. Hossmann, M.D., Ph.D., pages 21-36; from “Protection of the Brain from Ischemia” edited by Philip R. Weinstein and Alan A. Faden, Williams and Wilkens 1990.
95. “Post-ischemic resuscitation of the brain: selective vulnerability versus global resistance” by K.-A. Hossman; from Progress in Brain Research, Vol. 63, edited by K. Kogure, K.-A. Hossman, B. K. Siesjo and F. A. Welsh, 1985, pages 3-17.
96. Leonard Hayflick, personal communication in January of 1991.
97. “Resuscitation potentials after prolonged global cerebral ischemia in cats” by Konstantin-Alexander Hossmann, MD, Ph.D., Critical Care Medicine, Vol 16, No. 10, 1988, page 964-971.
98. “Charles Babbage On the Principles and Development of the Calculator and Other Seminal Writings” by Charles Babbage and others. Dover, New York, 1961.
99. “Molecular Mechanics” by U. Burkert and N. L. Allinger, American Chemical Society Monograph 177 (1982).
100. “Breaking the Diffraction Barrier: Optical Microscopy on a Nanometric Scale” by E. Betzig, J. K. Trautman, T.D. Harris, J.S. Weiner, and R.L. Kostelak, Science Vol. 251, March 22 1991, page 1468.
101. “Molecular Repair of the Brain” by Ralph C. Merkle, Cryonics Vol. 10 No. 10, October 1989, pages 21-44. This is a much earlier version of the current paper.
102. “The embryonic cell lineage of the nematode Caenorhabditis elegans,” by J.E. Sulston, E. Schierenberg, J.G. White, J.N. Thomson, Developmental Biology, Vol. 100, 1983 pages 64-119.
103. “Caenorhabditis elegans: Getting to Know You,” by Jean L. Marx, Science, Vol. 225, July 6 1984, pages 40-42.
104. “Why is Development So Illogical?” by Roger Lewin, Science Vol. 224, June 22 1984, pages 1327-1329.
105. ” Two Types of Mechanical Reversible Logic,” by Ralph C. Merkle, Nanotechnology 4 (1993) pages 114-131.
106. J.A. Armstrong, Creativity, Vol 10 No. 2, June 1991.
107. “Atom by Atom, Scientists build `Invisible’ Machines of the Future,” Andrew Pollack, The New York Times, Science section, Tuesday November 26, 1991, page B7.
108. “Theoretical analysis of a site-specific hydrogen abstraction tool,” by Charles Musgrave, Jason Perry, Ralph C. Merkle and William A. Goddard III, Nanotechnology, Vol 2 No. 3/4 1991, pages 187-195.
109. “Memory and Brain” by Larry R. Squire, Oxford University Press, 1987.
110. “Physics and Life Prolongation” by Gerald Feinberg, Physics Today, November 1966, page 45.
111. “Near-Field Optics: Microscopy, Spectroscopy, and Surface Modifications Beyond the Diffraction Limit” by Eric Betzig and Jay K. Trautman, Science, Vol. 257, July 10 1992, pages 189-195.
. ” Reversible Electronic Logic using Switches,” by Ralph C. Merkle, Nanotechnology 4 (1993) pages 21-40.
113. “Hot-Clock nMOS” by Charles Seitz, et. al 1985 Chapel Hill Conference on VLSI, Computer Science Press, pages 1-17.>
114. “Energy Limits to the Computational Power of the Human Brain” by Ralph C. Merkle, Foresight Update No. 6 page 1.
115. “The Bounded Brain: Toward Quantitative Neuroanatomy,” by Christopher Cherniak, Journal of Cognitive Neuroscience, Volume 2, No. 1, pages 58-68.
116. “Nanotechnology: Research and Perspectives” edited by B.C. Crandall and James Lewis, MIT press 1992.
. ” Self Replicating Systems and Molecular Manufacturing” by Ralph C. Merkle, Journal of the British Interplanetary Society, Vol. 45, pages 407-413, 1992.
. ” Computational Nanotechnology” by Ralph C. Merkle, Nanotechnology 1991; 2 (3 & 4): 134-141.
119. “NASA and Self Replicating Systems,” by Ralph C. Merkle, Foresight Update No. 9.
120. “Towards Practical Reversible Logic ,” by Ralph C. Merkle, in Workshop on Physics and Computation, PhysComp ’92, October, Dallas Texas; IEEE press 1992.
121. Nanotechnology 1991; 2 (3 & 4), special issue on the Second Foresight Conference on Molecular Nanotechnology.
122. Journal of the British Interplanetary Society, Vol. 45, 1992, special issue on molecular manufacturing.
123. “Metabolic and Functional Studies on Post-mortem Human Brain,” by J.A. Hardy and P. R. Dodd, Neurochem. Int. 5, 253-266, 1983.
124. “A Comparison of Methodologies for the Study of Functional Transmitter Neurochemistry in Human Brain,” by Peter R. Dodd, John W. Hambley, Richard F. Cowburn and John A. Hardy; Journal of Neurochemistry, 1988, pages 1133-1345.
125. “Extensive Postmortem Stability of RNA From Rat and Human Brain,” by S. A. Johnson, D. G. Morgan, and C. E. Finch; Journal of Neuroscience Research Volume 16, pages 267-280, 1986.
126. “Cold Start,” by Ralph C. Merkle, Cryonics 11(11), November 1990, page 11.
127. “Efficiently coded messages can transmit the information content of a human across interstellar space,” by William A. Reupke, Acta Astronautica, Vol. 26, No. 3/4, pages 273-276, 1992.
128. “The information content and error rate of living things,” by Sidney M. Dancoff and Henry Quastler, pages 263-273 of “Essays on the use of information theory in biology,” edited by H. Quastler, University of Illinois Press (1953).
129. “The next frontier: invisible,” by Charles Siebert, The New York Times Magazine, September 29, 1996, pages 137-140.
130. “Problems of reproducibility — does geologically ancient DNA survive in amber-preserved insects?” by Jeremy J. Austin, Andrew J. Ross, Andrew B. Smith, Richard A. Fortey and Richard H. Thomas; Proceedings of the Royal Society, London B (1997) 264, 467-474.
131. Greg Fahy, private communication.

NOTES

1) Peter Mazur, a well known cryobiologist and critic of cryonics, has said: “Cryobiologists are often asked how long cells can remain viable at -196 degrees C, the temperature of boiling liquid nitrogen (which is the usual cryogenic fluid). The answer is clear — more than 1000 years. The reason is that direct ionizations from background radiation are the only source of damage at such temperatures. Ordinary chemical reactions cannot occur. The pertinent question then is not storage stability, it is how can one get cells down to -196 degrees C and back without killing them.”[42] The record for storage is held by Leonard Hayflick, who has kept normal fibroblasts from embryonic human lungs in liquid nitrogen for 28 years (as of June 1990) without noticeable deterioration[96].

2) There is no implication here that the most powerful repair method either will (or will not) be used or be necessary. The fact that we can kill a gnat with a double-barreled shotgun does not imply that a fly-swatter won’t work just as well. If we aren’t certain whether we face a gnat or a tiger, we’d rather be holding the shotgun than the fly- swatter. The shotgun will work in either case, but the fly-swatter can’t deal with the tiger. In a similar vein, we will consider the most powerful methods that should be feasible rather than the minimal methods that might be sufficient. While this approach can reasonably be criticized on the grounds that simpler methods are likely to work, it avoids the complexities and problems that must be dealt with in trying to determine exactly what those simpler methods might be in any particular case and provides additional margin for error.

3) An atomic mass unit is the same as a Dalton. Different authors in different fields have different preferences for the name used to describe this unit, and so no single abbreviation will satisfy everyone. The use in this paper of the atomic mass unit, abbreviated as amu, was a compromise intended to be most easily understood by the widest audience.

4) A wide variety of mechanical computer designs are feasible. Perhaps the most famous proposal for a mechanical computer was made by Charles Babbage[98] in the early to mid 1800’s. Mechanical systems can be scaled down to the molecular size range and still function, although the analysis of such molecular mechanical systems requires the use of (appropriately enough) molecular mechanics: a thriving field which models molecular behavior by the use of force fields to describe the forces acting on the individual nuclei[99]. The time evolution of the locations of the nuclei can be followed using relatively straightforward computational methods.

5) To fully specify the state of each atom would, strictly speaking, require that we specify the states of all its electrons. For the most part, however, these states are known or can be readily inferred once the type of atom is given. For example, a sodium atom in solution will normally be the ion, Na+. Likewise, the bonding structure of two carbon atoms separated by a certain distance can normally be inferred from the distance. The state of magnetization, while relevant for computers (the state of magnetization of a floppy disk is obviously of importance) is of negligible importance in biological systems. People are routinely exposed to magnetic fields of several Tesla to make diagnostic images, and appear none the worse for the experience. While coordinate information should be sufficient in almost all cases, we can always add a few bits of additional information if there is some ambiguity. This won’t increase our estimate of 100 bits per atom by very much, and because 100 bits is a conveniently round number we’ll continue to use it.

6) Because proteins are always produced as a linear chain, they must of necessity be able to adopt an appropriate three dimensional configuration by themselves. Usually, the correct configuration is unique. If it isn’t, it is usually the case that the molecule will spontaneously cycle through appropriate configurations by itself, e.g., an ion channel will open and close at appropriate times regardless of whether it was initially started in the “open” or “closed” configuration. If any remaining cases should prove to be a problem, a few additional bits can be used to describe the specific configuration desired.

7) “For many years, it was thought that irreversible cellular damage unavoidably occurs after only a few minutes of complete cerebral ischemia. This opinion has been modified during the past decade [omitted reference]. Provided that the conditions for recovery are optimal, short-term restoration of brain functions may be achieved after periods of ischemia lasting as long as 60 minutes…”[93]. “Most clinical and experimental studies suggest that the normothermic brain is not able to withstand complete ischemia of >8 to 10 min. There is, however, firm experimental evidence of functional and biochemical recovery of a substantial part of the brain after complete cerebrocirculatory arrest of one hour [omitted references].”[97]. “It turned out in fact that appropriate treatment of post-ischemic recirculation disturbances led to recovery of energy metabolism and neuronal excitability after complete cerebro-circulatory arrest of as long as 1 hour at normal body temperature [omitted reference]”[95].

8) Definitions that are similar or identical to the one given here are well known in the cryonics literature[23].

9) This issue is of great concern to computer users. A variety of tools and techniques exist for recovering information from damaged or otherwise inoperative disk drives, with the intent of recovering the memory and “personality” of the computer so that the user will not suffer a (sometimes traumatic) loss.

10) Cryonics will also fail if a person is prematurely thawed. This failure mode, however, is not an argument against cryonics, rather it is an argument for reliable refrigerators. A person injured in a car crash might die if their ambulance was struck by a train. This is not an argument that we should cremate accident victims rather than use an ambulance to transport them to a hospital!

11) There is fairly general agreement that death by the information theoretic criterion will not occur during storage of tissue at the temperature of liquid nitrogen, confer note 1. For this reason we neglect the possibility that significant information loss occurs during storage even though this might be viewed as theoretically possible.

12) Criticisms of cryonics are not supported by the extant literature. Interestingly (and somewhat to the author’s surprise) there are no published technical articles on cryonics that claim it won’t work. As one might suspect, there are also no articles in the neuroscience literature that address the issue of erasure of memory in the information theoretic sense, and there are no articles in the cryobiological literature that address the impact of freezing on the retention of long term memory in the information theoretic sense. There is an almost absolute conceptual failure to either understand or consider the implications of the information theoretic criterion of death. This conceptual failure is a severe impediment to research in this area. Even worse, the Society for Cryobiology has gone so far as to adopt by- laws calling for the expulsion of members who support cryonics. Members in good standing who support cryonics have been threatened with firing if they discuss their views publicly. Open discussion and review has proven to be a remarkably effective engine for driving scientific advance. The suppression of open discussion by a scientific society runs counter to one of the most central principles of scientific research and seriously impedes progress.

13 Many non-mammalian animals can be frozen to temperatures as low as -50 degrees C and survive[57].

14) Unpublished work by Darwin, Leaf and Hixon suggests that penetration of glycerol into the axonal regions of myelinated nerve cells is poor, and that increased damage to the axon results. This is consistent with the observation that penetration of glycerol through the many layers of the myelin sheath would presumably be slowed. However, myelinated axons are relatively large and serve a relatively well defined function: the transport of information. Even significant damage to the axon would not obliterate the fact of its existence or the path over which it carried its signal. As a consequence, this damage is unlikely to result in information theoretic death.

15) Suspension usually begins immediately upon pronouncement of legal “death.” If legal death is pronounced upon cessation of heartbeat in a terminally ill patient who is being continuously monitored, then the ischemic interval can be kept under 5 minutes.

16) There are at least two likely ways in which cryonic suspensions conducted prior to legal death could be legalized. First, polls support the creation of a “right to die” for the terminally ill patient and an active movement is seeking to translate this support into some form of law. Second, terminally ill patients have sought and will presumably continue to seek the legal right to be cryonically suspended before (rather than after) a deteriorative disease (brain cancer, for example[64]) has destroyed their brain. It is difficult to argue that such individuals should be forced to suffer an agonizing death, knowing that this agony is also destroying their brain and hence any chance for a future life.

17) There are various reasons for delay when a person is cryonically suspended, ranging from purely pragmatic issues such as delay following abrupt and unexpected accidents to legal and social forces that mandate that suspension not be started until after a legal declaration of “death.” Whatever the cause, the effect is to increase the level of damage that takes place prior to suspension.

18) It should be clear that the claim of “irreversibility” is unsupported. Mitochondrial function is well understood: they provide energy for the cell. Even the complete absence of mitochondria would not cause death by the information theoretic criteria.

19) Much current work advances the (correct) claim that cellular, organ, and body function is lost under certain conditions. This loss of function is incorrectly and misleadingly labeled “death,” “irreversible injury,” etc. This work forms the backdrop against which tissue damage to cryonically suspended patients is measured by most biologists, cryobiologists, doctors and other health care workers. Clearly, this work predisposes such workers to dismiss cryonics because, by these criteria, much “irreversible” damage has occurred in most cryonically suspended patients. The implications of adopting the information theoretic criterion of death have simply not been considered, and we can reasonably expect a delay of several years to a few decades before they are. This would be consistent with historical data concerning the slow acceptance of new ideas. Ignaz Semmelweis demonstrated in 1848 that washing your hands in chlorinated lime after leaving the autopsy room and before entering the maternity ward reduced maternal deaths from childbed fever from as high as 25% to about 1%. Despite this, his proposal was widely ridiculed and little practiced for several more decades[60]. Interestingly, few of even the most severe critics of cryonics claim that death by the information theoretic criterion is likely to have occurred when the question is posed to them directly.

20) It is interesting to note that current research into the three- dimensional structure of neurons often embeds neural tissue in plastic, and then produces a series of thin sections (typically 50 to 100 nanometers thick in electron microscopic reconstruction work) by using an ultramicrotome. The serial sections are then examined by a person (typically a graduate student) and the structures of interest in each section are outlined on a digitizing tablet and entered into a computer. The resulting data-base is used to build a three-dimensional image of the neuron[54]. This work has been quite successful at determining the three-dimensional structure of small volumes (small enough for a graduate student to examine in a few weeks or months) despite the adverse effects of tissue preparation and sectioning. Sections vary in thickness. They also buckle, fold, and tear. Despite these difficulties, the human visual system can reconstruct the original shape of the object in three dimensions. Current electron microscopic reconstructions are quite capable of analyzing even the finest dendrites and thinnest axons, as well as determining the location and size of synapses[27,28], and even finer detail[29]. It seems reasonable that the less damaging method of inducing a fracture at low temperature, and the more informative and less damaging analysis possible with nanotechnology (as opposed to destructive analysis of thin sections by a high energy electron beam) will produce more information about the structure being analyzed.

21) Under favorable circumstances, we might be able to terminate the division process sooner. That is, it might be that a relatively large piece of tissue (several tens of microns or larger) was relatively intact, and required little if any repair. Devising methods to take advantage of the minimal damage that might occur under favorable circumstances is beyond the scope of this paper.

22) For those concerned about the omission of water molecules and the like, we could just as easily store the coordinates of every molecule. This would increase the storage requirement, but would still be entirely feasible.

23) Despite the notorious difficulty in obtaining accurate information about specific aspects of brain “hardware,” as discussed by Cherniak[115], it is still the case that rather rough bounds can be usefully derived.

24) A literature search on cryonics along with personal inquiries has not produced a single technical paper on the subject that claims that cryonics is infeasible or even unlikely. On the other hand, technical papers and analyses of cryonics that speak favorably of its eventual success have been published. It is unreasonable, given the extant literature, to conclude that cryonics is unlikely to work. Such unsupported negative claims require further analysis and careful critical evaluation before they can be taken seriously.