What is Life?:How chemistry becomes biology (19 page)

Biological systems, on the other hand, are many orders of magnitude more complex, and are therefore less amenable to a detailed chemical analysis. That is why the two subjects are typically discussed at their different hierarchical levels. No matter, the recognition that natural selection has its roots within a fundamentally chemical phenomenon, one that is well understood, provides an important link connecting the two sciences of chemistry and biology.

What about that central biological term ‘fitness’? What is the chemical analogue of that term and what new insights does the translation of that central biological term offer? According to Darwin, fitness is just the capacity to survive and reproduce, and its optimization is deemed the ultimate goal of the evolutionary process. Yet that concept, conceived by Darwin in strictly qualitative terms, has become a source of endless confusion due to continuing attempts to formally quantify it. The large number of fitness types that have been proposed and discussed—absolute fitness, relative fitness, inclusive fitness, ecological fitness, to mention some key ones—clearly attest to the inherent difficulties in this venture. The problem of fitness is a highly complex one, and one that has been troubling leading biologists for the better part of the past half-
century, so a detailed discussion is well beyond the scope of this book. In the present context our goal is a more limited one: to explore how the merging of chemistry and biology can assist in clarifying at least some aspects of the troublesome ‘fitness’ issue.

In our earlier discussion of replicating systems we identified a fundamental characteristic of those systems—their dynamic kinetic stability, DKS. The ability of a replicating system to maintain itself over time reflects its stability, but a stability kind that differs from the conventional thermodynamic one. Our discussion now reveals that ‘fitness’ is actually the biological expression of that more general and fundamental chemical concept, so let us state that explicitly:

fitness = dynamic kinetic stability (DKS)

When we classify a biological entity as ‘fit’ we are really specifying that it is stable—stable in the sense of being persistent. However, as we explained previously in some detail, that stability kind only applies to a population, not to individual replicators within the population. Specifying that a population is fit (or stable) just means that the population is able to maintain itself through ongoing replication/reproduction. The immediate consequence of relating fitness and DKS is that it indicates more explicitly that fitness is best viewed as a population characteristic, not an individual one. The concept of DKS has no real meaning at the individual level. A stable population of some replicating system is the reality that comes about through individual replicators being formed and then decaying, like the water droplets turning over in a fountain. In the context of life, if you focus on the individual entity, tempting as it may be, you are missing the essence of what defines life—its dynamic nature, the continual
turnover of the individual entities that make up a particular replicating population. Bottom line: in order to understand life’s essence one should focus on life’s
population
aspect, not its
individual
aspect. Life is an evolutionary phenomenon and evolution does not operate on individuals, only on populations. Individuals are just born and then die. Focus on the individual and you will miss much of what life entails. In actual fact the difficulty in individual thinking goes deeper than the above comments might suggest. What is an individual living entity, and do they actually exist? The answer to this question is more complex than we might imagine, but I will defer this aspect of the discussion to
chapter 8
.

The fact that a population perspective is crucial for a proper understanding of replicator dynamics received considerable impetus from important theoretical work carried out in the 1970s by Manfred Eigen, the eminent Nobel prize-winning German chemist, together with Peter Schuster, the distinguished Austrian chemist, on what is termed quasispecies theory.
⁵⁸
In order to understand that theory in simplest terms we first need to describe what is meant by a ‘fitness landscape’. As already discussed in
chapter 4
, when a replicating molecule, say an RNA of some particular sequence, proceeds to replicate, occasional errors in the replication reaction will result in the formation of RNA mutants. Mutants that are faster replicators will tend to drive the slower replicating sequences to extinction. That process of sequence modification can be represented by what is termed a fitness landscape—a three-dimensional topographical map. In that three-dimensional representation, the horizontal axes represent sequence changes (that come about through mutations) while the vertical axis represents the fitness of the particular sequence. The higher the value on that vertical axis, the
greater the fitness. Accordingly, the fitness landscape resembles a three-dimensional topological map of mountain ranges and valleys in between. High points on the landscape—mountain peaks—represent RNA sequences of high fitness (fast replicators) and low points—valleys—represent RNA sequences of lower fitness (slower replicators). What that means is that some initial RNA sequence of a particular fitness, a point on that topology map, will tend to explore the fitness landscape in search of the highest point on the fitness landscape, representing the sequence of highest fitness, much like a hiker in the mountains seeking to climb to the top of the highest peak.

But here’s the important point. Manfred Eigen and Peter Schuster discovered that the population of replicating RNAs that is generated by this exploration of the fitness landscape does not consist of one single sequence, but rather a
population
of RNAs of differing sequences, centred around the most successful sequence (termed the wild type) within that population. This population of varied sequences was termed a quasispecies, and an analogy that may help capture the essence of a quasispecies would be a flock of birds as it moves in concert over the sequence landscape in search of ever higher peaks. Eigen and Schuster discovered through their computer modelling of evolutionary changes in the RNA sequences that it is not the
fittest sequence
that is selected for but the
fittest population of sequences
—the fittest quasispecies—that is selected for. In other words, evolution operates by seeking out improved fitness in a
population
sense rather than in an individual sense. In fact one can see in Eigen and Schuster’s seminal work the importance of population heterogeneity. A mutation leading to a particularly successful replicator is as likely to come from a
slower
RNA as from a
faster
one.
Counter-intuitively, the road to a fitter population may actually pass through a ‘less fit’ individual replicator within the existing population. Population heterogeneity opens up more possibilities for evolution to carry out its magic—heterogeneous populations evolve more effectively than homogeneous ones. The message is clear: the essence of stability in the world of replicators is rooted in populations, not individuals. Evolution is a process that populations undergo, not individuals. In the evolutionary scheme of things the individual is but a fleeting event, a transient water droplet in the fountain of life.

We have discussed the concept of DKS in some detail and the pertinent question now arises: can DKS be quantified? The short answer—only to a limited extent. We have identified DKS as a distinct stability kind in nature and stated that evolution operates so that DKS tends to increase over time. That fact alone suggests the term is quantifiable. Surely, if we say that evolution leads to greater DKS, that means that DKS is measurable. Yes and no. Take two RNA molecules in Sol Spiegelman’s experiment competing for building blocks during self-replication and we see one replicates faster than the other. So the relative rates at which the two RNAs replicate may be taken as a quantitative measure of the relative DKSs of the two RNA populations. Recall, it is precisely because of this rate difference that a population of one replicator drives the other population to extinction. However that attempt at quantification is only of limited value for two reasons. First, attempts at quantification are only relevant for two populations that feed off a common resource. That means it can be applied to two RNA populations competing for the same set of activated nucleotides. But asking whether an
E. Coli
bacterium or a camel is more stable is
meaningless, even in a population sense—there is no common frame of reference; it would be like comparing apples and oranges.

But the difficulty in quantifying DKS goes deeper. Despite the above comments implying that relative DKS
can
be estimated for competing replicators, another problem arises. The relative rates of replication depend on the reaction conditions. Let’s return to Sol Spiegelman’s classic RNA test-tube experiments. If some extraneous material is introduced into the test tube that inhibits the replication rates, as in fact was done by Spiegelman, then the winner of the replication race can switch. Change the reaction conditions and the evolutionary course changes as well; the winner of the Darwinian race is likely to be an entirely different set of RNA molecules. When Sol Spiegelman added a substance, ethidium bromide, to the reaction mixture, the winner of the Darwinian race turned out to be a different sequence.
⁵⁹
Why is that? Since the extraneous material that had been added affected the mechanism of replication, certain sequences that initially facilitated rapid replication were inhibited, while other RNA sequences, initially slower, were favoured. In other words DKS for populations of RNAs is
circumstantial.
Its magnitude depends on the particular materials that are present in the reaction. But that means that DKS is quite different from that other kind of stability we speak about in chemistry, thermodynamic stability. The thermodynamic stability of water is a defined quantity regardless of what else is present (though to be precise, it does depend to some extent on the physical conditions, temperature, pressure, etc.). Thermodynamic stability is an intrinsic property of any system and is measured in
closed systems.
Dynamic kinetic stability depends on rates of reaction, is highly sensitive to reaction conditions, and can only be assessed in
open systems,
in which energy and resources
are continually supplied. That makes comparisons of DKS highly problematic.

To clarify the point further, let us consider a biological example—a population of bacteria in a pool of water. Such a population may well be highly stable—billions of bacteria are busy replicating, resulting in the establishment of a dynamic population of those bacteria. But add chlorine to the pool and the bacteria simply die—their stability vanishes. The DKS of that bacterial population has dropped to zero. Same bacteria, different circumstances. The thermodynamic stability of the water molecules in that pool, however, does not depend on the environment. It is measured relative to that of a hydrogen-oxygen gas mixture (the materials from which water is formed), and that difference does not depend on the location of the water and the presence of other materials in the water (at least to any significant extent). Attempting to quantify DKS for a particular system is like preparing for an exam where the answers to the questions keep changing!

There is a moral to the above story: some characteristics of undeniable scientific interest are inherently difficult to quantify, or are even unquantifiable. Attempts to quantify the unquantifiable will be unrewarding and may only lead to confusion. The above discussion relating fitness to its underlying chemical term, DKS, makes clear why attempts to quantify fitness have proved so elusive. Not everything that counts can be counted. In addressing the concept of fitness, context is everything.

Despite the difficulties we’ve discussed in our ability to formally quantify DKS, two crude measures of DKS are actually available. These are the steady-state population number for a given replicating entity and the length of time that the replicating population has
managed to maintain itself. A large steady-state population of some life form means that it is more readily able to withstand environmental changes that may undermine its existence. By contrast, if the population size of some living form is low, then clearly that population is vulnerable and may become extinct. On that basis it is reasonable to conclude that cockroaches and mosquitoes are more stable (in a DKS sense) than pandas. Cockroaches and mosquitoes are unlikely to become extinct in the foreseeable future, while the future of pandas is far less certain. Replication is ultimately a numbers game. The time dimension can also be a useful gauge of DKS. Cyanobacteria, which have maintained a continuing presence on our planet for several billion years, would of necessity be classified as stable, remarkably so. Modern humans, by comparison, have only existed for some 150,000–200,000 years, so our long-term stability is far less assured. No matter. Appreciating that fitness is the biological expression of a particular kind of stability helps place biology in a more physical context, and assists in our goal of merging the biological and physical sciences.

Having established the connection between fitness and stability we can now seek the chemical equivalent of that quintessential biological phrase ‘survival of the fittest’, or its more modern biological expression, ‘maximizing fitness’. This will prove of importance since this connection will lead us to the profound realization that the entire evolutionary process illustrated in Fig. 6, i.e., both the emergence of life from inanimate beginnings, as well as the evolution of simple biological systems into more complex ones, may be
associated with an identifiable driving force. The fact that a driving force for that entire process might be identifiable should not be a great surprise—that, after all, is nature’s way. In nature many processes are associated with a driving force. Flowing rivers, rainfall, avalanches, falling apples, are all associated with the gravitational force, while the driving force for all chemical reactions is just the omnipresent Second Law of Thermodynamics. Of course the term ‘force’ needs to be interpreted broadly, in line with our comments on pattern recognition in
chapter 3
. Forces do not have to be visible to be identified. The existence of a force is postulated through empirical recognition of its action. In the case of replicating systems and their clear tendency to become transformed into more effective replicating systems, the driving force can now be identified as
the drive toward greater DKS.
In other words, the biological term ‘maximizing fitness’ is just the biological expression of the more fundamental and more ‘physical’ concept—maximizing DKS. So let’s equate them: