The Mystery of Life's Origin:

Reassessing Current Theories




CHAPTER 8

Thermodynamics and the Origin of Life


Peter Molton has defined life as "regions of order which use energy to maintain their organization against the disruptive force of entropy."1 In Chapter 7 it has been shown that energy and/or mass flow through a system can constrain it far from equilibrium, resulting in an increase in order. Thus, it is thermodynamically possible to develop complex living forms, assuming the energy flow through the system can somehow be effective in organizing the simple chemicals into the complex arrangements associated with life.

In existing living systems, the coupling of the energy flow to the organizing "work" occurs through the metabolic motor of DNA, enzymes, etc. This is analogous to an automobile converting the chemical energy in gasoline into mechanical torque on the wheels. We can give a thermodynamic account of how life's metabolic motor works. The origin of the metabolic motor (DNA, enzymes, etc.) itself, however, is more difficult to explain thermodynamically, since a mechanism of coupling the energy flow to the organizing work is unknown for prebiological systems. Nicolis and Prigogine summarize the problem in this way:
Needless to say, these simple remarks cannot suffice to solve the problem of biological order. One would like not only to establish that the second law (dSi 0) is compatible with a decrease in overall entropy (dS < 0), but also to indicate the mechanisms responsible for the emergence and maintenance of coherent states.2
Without a doubt, the atoms and molecules which comprise living cells individually obey the laws of chemistry and physics, including the laws of thermodynamics. The enigma is the origin of so unlikely an organization of these atoms and molecules. The electronic computer provides a striking analogy to the living cell. Each component in a computer obeys the laws of electronics and mechanics. The key to the computer's marvel lies, however, in the highly unlikely organization of the parts which harness the laws of electronics and mechanics. In the computer, this organization was specially arranged by the designers and builders and continues to operate (with occasional frustrating lapses) through the periodic maintenance of service engineers.

Living systems have even greater organization. The problem then, that molecular biologists and theoretical physicists are addressing, is how the organization of living systems could have arisen spontaneously. Prigogine et al., have noted:
All these features bring the scientist a wealth of new problems. In the first place, one has systems that have evolved spontaneously to extremely organized and complex forms. Coherent behavior is really the characteristic feature of biological systems.3
In this chapter we will consider only the problem of the origin of living systems. Specifically, we will discuss the arduous task of using simple biomonomers to construct complex polymers such as DNA and protein by means of thermal, electrical, chemical, or solar energy. We will first specify the nature and magnitude of the "work" to be done in building DNA and enzymes.
[NOTE: Work in physics normally refers to force times displacement. In this chapter it refers in a more general way to the change in Gibbs free energy of the system that accompanies the polymerization of monomers into polymers].
In Chapter 9 we will describe the various theoretical models which attempt to explain how the undirected flow of energy through simple chemicals can accomplish the work necessary to produce complex polymers. Then we will review the experimental studies that have been conducted to test these models. Finally we will summarize the current understanding of this subject.

How can we specify in a more precise way the work to be done by energy flow through the system to synthesize DNA and protein from simple biomonomers? While the origin of living systems involves more than the genesis of enzymes and DNA, these components are essential to any system if replication is to occur. It is generally agreed that natural selection can act only on systems capable of replication. This being the case, the formation of a DNA/enzyme system by processes other than natural selection is a necessary (though not sufficient) part of a naturalistic explanation for the origin of life.
[NOTE: A sufficient explanation for the origin of life would also require a model for the formation of other critical cellular components, including membranes, and their assembly].

Order vs. Complexity in the Question of Information

Only recently has it been appreciated that the distinguishing feature of living systems is complexity rather than order.4 This distinction has come from the observation that the essential ingredients for a replicating system---enzymes and nucleic acids---are all information-bearing molecules. In contrast, consider crystals. They are very orderly, spatially periodic arrangements of atoms (or molecules) but they carry very little information. Nylon is another example of an orderly, periodic polymer (a polyamide) which carries little information. Nucleic acids and protein are aperiodic polymers, and this aperiodicity is what makes them able to carry much more information. By definition then, a periodic structure has order. An aperiodic structure has complexity. In terms of information, periodic polymers (like nylon) and crystals are analogous to a book in which the same sentence is repeated throughout. The arrangement of "letters" in the book is highly ordered, but the book contains little information since the information presented---the single word or sentence---is highly redundant.

It should be noted that aperiodic polypeptides or polynucleotides do not necessarily represent meaningful information or biologically useful functions. A random arrangement of letters in a book is aperiodic but contains little if any useful information since it is devoid of meaning.
[NOTE: H.P. Yockey, personal communication, 9/29/82. Meaning is extraneous to the sequence, arbitrary, and depends on some symbol convention. For example, the word "gift," which in English means a present and in German poison, in French is meaningless].
Only certain sequences of letters correspond to sentences, and only certain sequences of sentences correspond to paragraphs, etc. In the same way only certain sequences of amino acids in polypeptides and bases along polynucleotide chains correspond to useful biological functions. Thus, informational macro-molecules may be described as being and in a specified sequence.5 Orgel notes:
Living organisms are distinguished by their specified complexity. Crystals fail to qualify as living because they lack complexity; mixtures of random polymers fail to qualify because they lack specificity.6
Three sets of letter arrangements show nicely the difference between order and complexity in relation to information:
1. An ordered (periodic) and therefore specified arrangement:

THE END THE END THE END THE END

Example: Nylon, or a crystal.

[NOTE: Here we use "THE END" even though there is no reason to suspect that nylon or a crystal would carry even this much information. Our point, of course, is that even if they did, the bit of information would be drowned in a sea of redundancy].

2. A complex (aperiodic) unspecified arrangement:

AGDCBFE GBCAFED ACEDFBG

Example: Random polymers (polypeptides).

3. A complex (aperiodic) specified arrangement:

THIS SEQUENCE OF LETTERS CONTAINS A MESSAGE!

Example: DNA, protein.

Yockey7 and Wickens5 develop the same distinction, that "order" is a statistical concept referring to regularity such as could might characterize a series of digits in a number, or the ions of an inorganic crystal. On the other hand, "organization" refers to physical systems and the specific set of spatio-temporal and functional relationships among their parts. Yockey and Wickens note that informational macromolecules have a low degree of order but a high degree of specified complexity. In short, the redundant order of crystals cannot give rise to specified complexity of the kind or magnitude found in biological organization; attempts to relate the two have little future.

Information and Entropy

There is a general relationship between information and entropy. This is fortunate because it allows an analysis to be developed in the formalism of classical thermodynamics, giving us a powerful tool for calculating the work to be done by energy flow through the system to synthesize protein and DNA (if indeed energy flow is capable of producing information). The information content in a given sequence of units, be they digits in a number, letters in a sentence, or amino acids in a polypeptide or protein, depends on the minimum number of instructions needed to specify or describe the structure. Many instructions are needed to specify a complex, information-bearing structure such as DNA. Only a few instructions are needed to specify an ordered structure such as a crystal. In this case we have a description of the initial sequence or unit arrangement which is then repeated ad infinitum according to the packing instructions.

Orgel9 illustrates the concept in the following way. To describe a crystal, one would need only to specify the substance to be used and the way in which the molecules were to be packed together. A couple of sentences would suffice, followed by the instructions "and keep on doing the same," since the packing sequence in a crystal is regular. The description would be about as brief as specifying a DNA-like polynucleotide with a random sequence. Here one would need only to specify the proportions of the four nucleotides in the final product, along with instructions to assemble them randomly. The chemist could then make the polymer with the proper composition but with a random sequence.

It would be quite impossible to produce a correspondingly simple set of instructions that would enable a chemist to synthesize the DNA of an E. coli bacterium. In this case the sequence matters. Only by specifying the sequence letter-by-letter (about 4,000,000 instructions) could we tell a chemist what to make. Our instructions would occupy not a few short sentences, but a large book instead!

Brillouin,10 Schrodinger,11 and others12 have developed both qualitative and quantitative relationships between information and entropy. Brillouin,13 states that the entropy of a system is given by

S = k ln (8-1)

where S is the entropy of the system, k is Boltzmann's constant, and corresponds to the number of ways the energy and mass in a system may be arranged.

We will use Sth and Sc to refer to the thermal and configurational entropies, respectively. Thermal entropy, Sth, is associated with the distribution of energy in the system. Configurational entropy Sc is concerned only with the arrangement of mass in the system, and, for our purposes, we shall be especially interested in the sequencing of amino acids in polypeptides (or proteins) or of nucleotides in polynucleotides (e.g., DNA). The symbols th and c refer to the number of ways energy and mass, respectively, may be arranged in a system.

Thus we may be more precise by writing

S = k lnth c = k lnth + k lnc = Sth + Sc (8-2A)

where

Sth = k lnth (8-2b)

and

Sc = k lnc (8-2c)

Determining Information: From a Random Polymer to an Informed Polymer

If we want to convert a random polymer into an informational molecule, we can determine the increase in information (as defined by Brillouin) by finding the difference between the negatives of the entropy states for the initial random polymer and the informational molecule:

I = - (Scm - Scr) (8-3A),

I = Scr - Scm (8-3b),

= k lncr - k lncm (8-3c)

In this equation, I is a measure of the information content of an aperiodic (complex) polymer with a specified sequence, Scm represents the configurational "coding" entropy of this polymer informed with a given message, and Scr represents the configurational entropy of the same polymer for an unspecified or random sequence.

[NOTE: Yockey and Wickens define information slightly differently than Brilloum, whose definition we use in our analysis. The difference is unimportant insofar as our analysis here is concerned].
Note that the information in a sequence-specified polymer is maximized when the mass in the molecule could be arranged in many different ways, only one of which communicates the intended message. (There is a large Scr from eq. 8-2c since cr is large, yet Scm = 0 from eq. 8-2c since cm = 1.) The information carried in a crystal is small because Sc is small (eq. 8-2c) for a crystal. There simply is very little potential for information in a crystal because its matter can be distributed in so few ways. The random polymer provides an even starker contrast. It bears no information because Scr, although large, is equal to Scm (see eq. 8-3b).

In summary, equations 8-2c and 8-3c quantify the notion that only specified, aperiodic macromolecules are capable of carrying the large amounts of information characteristic of living systems. Later we will calculate "c" for both random and specified polymers so that the configurational entropy change required to go from a random to a specified polymer can be determined. In the next section we will consider the various components of the total work required in the formation of macromolecules such as DNA and protein.

DNA and Protein Formation:

Defining the Work

There are three distinct components of work to be done in assembling simple biomonomers into a complex (or aperiodic) linear polymer with a specified sequence as we find in DNA or protein. The change in the Gibbs free energy, G, of the system during polymerization defines the total work that must be accomplished by energy flow through the system. The change in Gibbs free energy has previously been shown to be

G = E + P V - T S (8-4a)

or

G = H - T S (8-4b)

where a decrease in Gibbs free energy for a given chemical reaction near equilibrium guarantees an increase in the entropy of the universe as demanded by the second law of thermodynamics.

Now consider the components of the Gibbs free energy (eq. 8-4b) where the change in enthalpy (H) is principally the result of changes in the total bonding energy (E), with the (P V) term assumed to be negligible. We will refer to this enthalpy component (H) as the chemical work. A further distinction will be helpful. The change in the entropy (S) that accompanies the polymerization reaction may be divided into two distinct components which correspond to the changes in the thermal energy distribution (Sth) and the mass distribution (Sc), eq. 8-2. So we can rewrite eq. 8-4b as

G = H - TSth - T Sc (8-5)

that is,

(Gibbs free energy) = (Chemical work) - (Thermal entropy work) - (Configurational entropy work)

It will be shown that polymerization of macromolecules results in a decrease in the thermal and configurational entropies (Sth 0, Sc 0). These terms effectively increase G, and thus represent additional components of work to be done beyond the chemical work.

Consider the case of the formation of protein or DNA from biomonomers in a chemical soup. For computational purposes it may be thought of as requiring two steps: (1) polymerization to form a chain molecule with an aperiodic but near-random sequence, and (2) rearrangement to an aperiodic, specified information-bearing sequence.

[NOTE: Some intersymbol influence arising from differential atomic bonding properties makes the distribution of matter not quite random. (H.P. Yockey, 1981. J. Theoret. Biol. 91,13)].
The entropy change (S) associated with the first step is essentially all thermal entropy change (Sth), as discussed above. The entropy change of the second step is essentially all configurational entropy reducing change (Sc). In fact, as previously noted, the change in configurational entropy (Sc) = Sc "coding" as one goes from a random arrangement (Scr) to a specified sequence (Scm) in a macromolecule is numerically equal to the negative of the information content of the molecule as defined by Brillouin (see eq. 8-3a).

In summary, the formation of complex biological polymers such as DNA and protein involves changes in the chemical energy, H, the thermal entropy, Sth, and the configurational entropy, Sc, of the system. Determining the magnitudes of these individual changes using experimental data and a few calculations will allow us to quantify the magnitude of the required work potentially to be done by energy flow through the system in synthesizing macromolecules such as DNA and protein.

Quantifying the Various Components of Work

1. Chemical Work


The polymerization of amino acids to polypeptides (protein) or of nucleotides to polynucleotides (DNA) occurs through condensation reactions. One may calculate the enthalpy change in the formation of a dipeptide from amino acids to be 5-8 kcal/mole for a variety of amino acids, using data compiled by Hutchens.14 Thus, chemical work must be done on the system to get polymerization to occur. Morowitz15 has estimated more generally that the chemical work, or average increase in enthalpy, for macromolecule formation in living systems is 16.4 cal/gm. Elsewhere in the same book he says that the average increase in bonding energy in going from simple compounds to an E. coli bacterium is 0.27 ev/atom. One can easily see that chemical work must be done on the biomonomers to bring about the formation of macromolecules like those that are essential to living systems. By contrast, amino acid formation from simple reducing atmosphere gases (methane, ammonia, water) has an associated enthalpy change (H) of -50 kcal/mole to -250 kcal/ mole,16 which means energy is released rather than consumed. This explains why amino acids form with relative ease in prebiotic simulation experiments. On the other hand, forming amino acids from less-reducing conditions (i.e., carbon dioxide, nitrogen, and water) is known to be far more difficult experimentally. This is because the enthalpy change (H) is positive, meaning energy is required to drive the energetically unfavorable chemical reaction forward.

2. Thermal Entropy Work

Wickens17 has noted that polymerization reactions will reduce the number of ways the translational energy may be distributed, while generally increasing the possibilities for vibrational and rotational energy. A net decrease results in the number of ways the thermal energy may be distributed, giving a decrease in the thermal entropy according to eq. 8-2b (i.e., Sth 0). Quantifying the magnitude of this decrease in thermal entropy (Sth ) associated with the formation of a polypeptide or a polynucleotide is best accomplished using experimental results.

Morowitz18 has estimated that the average decrease in thermal entropy that occurs during the formation of macromolecules of living systems in 0.218 cal/deg-gm or 65 cal/gm at 298oK. Recent work by Armstrong et al.,19 for nucleotide oligomerization of up to a pentamer indicates H and -T Sth values of 11.8 kcal/mole and 15.6 kcal/mole respectively, at 294K. Thus the decrease in thermal entropy during the polymerization of the macromolecules of life increases the Gibbs free energy and the work required to make these molecules, i.e., -T Sth > 0.

3. Configurational Entropy Work

Finally, we need to quantify the configurational entropy change (Sc) that accompanies the formation of DNA and protein. Here we will not get much help from standard experiments in which the equilibrium constants are determined for a polymerization reaction at various temperatures. Such experiments do not consider whether a specific sequence is achieved in the resultant polymers, but only the concentrations of randomly sequenced polymers (i.e., polypeptides) formed. Consequently, they do not measure the configurational entropy (Sc) contribution to the total entropy change (S). However, the magnitude of the configurational entropy change associated with sequencing the polymers can be calculated.

Using the definition for configurational "coding" entropy given in eq. 8-2c, it is quite straightforward to calculate the configurational entropy change for a given polymer. The number of ways the mass of the linear system may be arranged (c) can be calculated using statistics. Brillouin20 has shown that the number of distinct sequences one can make using N different symbols and Fermi-Dirac statistics is given by

= N! (8-6)

If some of these symbols are redundant (or identical), then the number of unique or distinguishable sequences that can be made is reduced to

c = N! / n1!n2!n2!...ni! (8-7)

where n1 + n2 + ... + ni = N and i defines the number of distinct symbols. For a protein, it is i =20, since a subset of twenty distinctive types of amino acids is found in living things, while in DNA it is i = 4 for the subset of four distinctive nucleotides. A typical protein would have 100 to 300 amino acids in a specific sequence, or N = 100 to 300. For DNA of the bacterium E. coli, N = 4,000,000. In Appendix 1, alternative approaches to calculating c are considered and eq. 8-7 is shown to be a lower bound to the actual value.

For a random polypeptide of 100 amino acids, the configurational entropy, Scr, may be calculated using eq. 8-2c and eq. 8-7 as follows:

Scr = k lncr

since cr = N! / n1!n2!...n20! = 100! / 5!5!....5! = 100! / (5!)20

= 1.28 x 10115 (8-8)

The calculation of equation 8-8 assumes that an equal number of each type of amino acid, namely 5, are contained in the polypeptide. Since k, or Boltzmann's constant, equals 1.38 x 10-16 erg/deg, and ln [1.28 x 10115] = 265,

Scr = 1.38 x 10-16 x 265 = 3.66 x 10-14 erg/deg-polypeptide

If only one specific sequence of amino acids could give the proper function, then the configurational entropy for the protein or specified, aperiodic polypeptide would be given by

Scm = k lncm
= k ln 1
= 0
(8-9)

Determining scin Going from a Random Polymer to an Informed Polymer

The change in configurational entropy, Sc, as one goes from a random polypeptide of 100 amino acids with an equal number of each amino acid type to a polypeptide with a specific message or sequence is:

Sc = Scm - Scr

= 0 - 3.66 x 10-14 erg/deg-polypeptide
= -3.66 x 10-14 erg/deg-polypeptide (8-10)

The configurational entropy work (-T Sc) at ambient temperatures is given by

-T Sc = - (298oK) x (-3.66 x 10-14) erg/deg-polypeptide
= 1.1 x 10-11 erg/polypeptide
= 1.1 x 10-11 erg/polypeptide x [6.023 x 1023 molecules/mole] / [10,000 gms/mole] x [1 cal] / 4.184 x 107 ergs

= 15.8 cal/gm (8-11)

where the protein mass of 10,000 amu was estimated by assuming an average amino acid weight of 100 amu after the removal of the water molecule. Determination of the configurational entropy work for a protein containing 300 amino acids equally divided among the twenty types gives a similar result of 16.8 cal/gm.

In like manner the configurational entropy work for a DNA molecule such as for E. coli bacterium may be calculated assuming 4 x 106 nucleotides in the chain with 1 x 106 each of the four distinctive nucleotides, each distinguished by the type of base attached, and each nucleotide assumed to have an average mass of 339 amu. At 298oK:

-T Sc = -T (Scm - Scr)

= T ( Scr - Scm)

= kT ln (cr - lncm)

= kT ln [(4 x 106)! / (106)!(106)!(106)!(106)!] - kT ln 1

= 2.26 x 10-7 erg/polynucleotide

= 2.39 cal/gm 8-12

It is interesting to note that, while the work to code the DNA molecule with 4 million nucleotides is much greater than the work required to code a protein of 100 amino acids (2.26 x 10-7 erg/DNA vs. 1.10 x 10-11 erg/protein), the work per gram to code such molecules is actually less in DNA. There are two reasons for this perhaps unexpected result: first, the nucleotide is more massive than the amino acid (339 amu vs. 100 amu); and second, the alphabet is more limited, with only four useful nucleotide "letters" as compared to twenty useful amino acid letters. Nevertheless, it is the total work that is important, which means that synthesizing DNA is much more difficult than synthesizing protein.

It should be emphasized that these estimates of the magnitude of the configurational entropy work required are conservatively small. As a practical matter, our calculations have ignored the configurational entropy work involved in the selection of monomers. Thus, we have assumed that only the proper subset of 20 biologically significant amino acids was available in a prebiotic oceanic soup to form a biofunctional protein. The same is true of DNA. We have assumed that in the soup only the proper subset of 4 nucleotides was present and that these nucleotides do not interact with amino acids or other soup ingredients. As we discussed in Chapter 4, many varieties of amino acids and nucleotides would have been present in a real ocean---varieties which have been ignored in our calculations of configurational entropy work. In addition, the soup would have contained many other kinds of molecules which could have reacted with amino acids and nucleotides. The problem of using only the appropriate optical isomer has also been ignored. A random chemical soup would have contained a 50-50 mixture of D- and L-amino acids, from which a true protein could incorporate only the Lenantiomer. Similarly, DNA uses exclusively the optically active sugar D-deoxyribose. Finally, we have ignored the problem of forming unnatural links, assuming for the calculations that only CL-links occurred between amino acids in making polypeptides, and that only correct linking at the 3', 5'-position of sugar occurred in forming polynucleotides. A quantification of these problems of specificity has recently been made by Yockey.21

The dual problem of selecting the proper composition of matter and then coding or rearranging it into the proper sequence is analogous to writing a story using letters drawn from a pot containing many duplicates of each of the 22 Hebrew consonants and 24 Greek and 26 English letters all mixed together. To write in English the message,

HOW DID I GET HERE?

we must first draw from the pot 2 Hs, 2 Is, 3 Es, 2 Ds, and one each of the letters W, 0, G, T, and R. Drawing or selecting this specific set of letters would be a most unlikely event itself. The work of selecting just these 14 letters would certainly be far greater than arranging them in the correct sequence. Our calculations only considered the easier step of coding while ignoring the greater problem of selecting the correct set of letters to be coded. We thereby greatly underestimate the actual configurational entropy work to be done.

In Chapter 6 we developed a scale showing degrees of investigator interference in prebiotic simulation experiments. In discussing this scale it was noted that very often in reported experiments the experimenter has actually played a crucial but illegitimate role in the success of the experiment. It becomes clear at this point that one illegitimate role of the investigator is that of providing a portion of the configurational entropy work, i.e., the "selecting" work portion of the total -T Sc work.

It is sometimes argued that the type of amino acid that is present in a protein is critical only at certain positions---active sites---along the chain, but not at every position. If this is so, it means the same message (i.e., function) can be produced with more than one sequence of amino acids.

This would reduce the coding work by making the number of permissible arrangements cm in eqs. 8-9 and 8-10 for Scm greater than 1. The effect of overlooking this in our calculations, however, would be negligible compared to the effect of overlooking the "selecting" work and only considering the "coding" work, as previously discussed. So we are led to the conclusion that our estimate for Sc is very conservatively low.

Calculating the Total Work: Polymerization of Biomacromolecules

It is now possible to estimate the total work required to combine biomonomers into the appropriate polymers essential to living systems. This calculation using eq. 8-5 might be thought of as occurring in two steps. First, amino acids polymerize into a polypeptide, with the chemical and thermal entropy work being accomplished (H -T Sth). Next, the random polymer is rearranged into a specific sequence which constitutes doing configurational entropy work (-T Sc). For example, the total work as expressed by the change in Gibbs free energy to make a specified sequence is

G = H - T Sth - T Sc (8-13)

where H - T Sth may be assumed to be 300 kcal/mole to form a random polypeptide of 101 amino acids (100 links). The work to code this random polypeptide into a useful sequence so that it may function as a protein involves the additional component of T Sc "coding" work, which has been estimated previously to be 15.9 cal/gm, or approximately 159 kcal/mole for our protein of 100 links with an estimated mass of 10,000 amu per mole. Thus, the total work (neglecting the "sorting and selecting" work) is approximately

G = (300 + 159) kcal/mole = 459 kcal/mole (8-14)

with the coding work representing 159/459 or 35% of the total work.

In a similar way, the polymerization of 4 x 106 nucleotides into a random polynucleotide would require approximately 27 x 106 kcal/mole. The coding of this random polynucleotide into the specified, aperiodic sequence of a DNA molecule would require an additional 3.2 x 106 kcal/mole of work. Thus, the fraction of the total work that is required to code the polymerized DNA is seen to be 8.5%, again neglecting the "sorting and selecting" work.

The Impossibility of Protein Formation under Equilibrium Conditions

It was noted in Chapter 7 that because macromolecule formation (such as amino acids polymerizing to form protein) goes uphill energetically, work must be done on the system via energy flow through the system. We can readily see the difficulty in getting polymerization reactions to occur under equilibrium conditions, i.e., in the absence of such an energy flow.

Under equilibrium conditions the concentration of protein one would obtain from a solution of 1 M concentration in each amino acid is given by:

K= [protein] x [H2 0] / [glycine] [alanine]... (8-15)

where K is the equilibrium constant and is calculated by

K = exp [ - G / RT ] (8-16)

An equivalent form is

G = -RT ln K (8-17)

We noted earlier that G = 459 kcal/mole for our protein of 101 amino acids. The gas constant R = 1.9872 cal/deg-mole and T is assumed to be 298oK. Substituting these values into eqs. 8-15 and 8-16 gives

protein concentration = 10-338 M (8-18)

This trivial yield emphasizes the futility of protein formation under equilibrium conditions. In the next chapter we will consider various theoretical models attempting to show how energy flow through the system can be useful in doing the work quantified in this chapter for the polymerization of DNA and protein. Finally, we will examine experimental efforts to accomplish biomacromolecule synthesis.


References

1. Peter M. Molton, 1978. J. Brit. Interplanet. Soc. 31, 147.

2. G. Nicolis and I. Prigogine, 1977. Self Organization in Nonequilibrium Systems. New York: John Wiley, p.25.

3. I. Prigogine, G. Nicolis, and A. Babloyantz, 1972. Physics Today, p.23.

4. L.E. Orgel, 1973. The Origins of Life. New York: John Wiley, p. 189ff; M. Polanyi, 1968. Science 160, 1308; Huberi P. Yockey, 1977. J. Theoret. Biol 67, 377; Jeffrey Wickens, 1978. J. Theoret Biol. 72, 191.

5. Yockey, J. Theoret. Biol, p.383.

6. Orgel, The Origins of Life, p.189.

7. Yockey, J. Theoret. Biol, p.579.

8. Wickens, J. Theoret. Biol., p.191.

9. Orgel, The Origins of Life, p.190.

10. L. Brillouin, 1951. J. Appi. Phys. 22, 334; 1951. J. Appl Phys. 22, 338; 1950. Amer. Sci. 38, 5941949. Amer. Sci. 37, 554.

11. E. Schrodinger, 1945. What is Life? London: Cambridge University Press, and New York: Macmillan.

12. W. Ehrenberg, 1967. Sci. Amer. 217,108; Myron Tribus and Edward C. McIrvine, 1971. Sci. Amer. 225, 197.

13. Brillouin, J. AppL Phys. 22, 885.

14. John 0. Hutchens, 1976. Handbook of Biochemistry and Molecular Biology, 3rd ed., Physical and Chemical Data, Gerald D. Fasman. Cleveland: CRC Press.

15. H. Morowitz, 1968. Energy Flow in Biology. New York: Academic Press, p.79.

16. H. Borsook and H.M. Huffman, 1944. Chemistry of Amino Acids and Proteins, ed. C.L.A. Schmidt. Springfield, Mass.: Charles C. Thomas Co., p.822.

17. Wickens, J. Theoret. Biol, p.191.

18. Morowitz, Energy Flow in Biology, p.79.

19. D.W. Armstrong, F. Nome, J.H. Fendler, and J. Nagyvary, 1977. J. Mol. Evol. 9, 218.

20. Brilllouin, J. AppL Phys. 22, 338.

21. H.P. Yockey, 1981. J. Theoret. Biol 91, 13.


Chapter 9

Chapter 7

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