Evolution of biological functionality research

The research in this area looks at how life evolved, and what are the attributes of intelligent life.

Genetic coding

What is it about the chemistry of our universe that provides for the possibility of genetic coding and how did it get going in the first place? Answers can be found in the chemistry of the amino acyl-tRNA (aaRS) enzymes and their cooperative mode of operation.

It looks highly likely that the most elementary system of genetic information processing was a binary “one-bit” code that was executed by two enzymes encoded in complementary strands of a short double helix. We have identified about 100 amino acid residues that make up the core structures of both Class I and II aaRSs from extant species taken from every branch of the tree of life. From this information we are computing the family tree of the aaRSs during the period, more than 3.5 billion years ago, before the last universal common ancestor of all the organisms that have ever existed.

We are also searching for genetic sequences that separately encode Class I and II aaRS activities on complementary double strands. Our simulations of Gene-Replicase-Translatase (GRT) systems are demonstrating how genetic coding can emerge in molecular systems that start in a completely disordered state.

About the researcher
Associate Professor Peter Wills
Department of Physics

Behaviour and the origins of life

Which came first, genes or behaviour? The common view is that evolution must have preceded these structures and for this reason, it may seem counterintuitive to consider how behaviour could have played a role in the origins of life.

However, many examples can be found in non-biological systems that demonstrate life-like behaviour. One of the most compelling examples is that of motile oil-droplets. These are simple systems, in some cases involving less than five common and easily synthesized chemicals, such as olive-oil and soap, but their behaviour is impressive.

These behaviours can be described as viability-based behaviours. In each case, there is an inherently unstable dissipative structure that persists only when there is sufficient, accessible free-energy to maintain its ordered state. The behaviours of these systems tend to increase the likelihood of there being sufficient energy available, and this is no mere coincidence. In each case, the behaviour is not an arbitrary response to the environment but is, instead, a response to how the environment affects the self-maintenance of the dissipative structure. This “viability-based” behaviour is reminiscent of the “metabolism-based” behaviour that is observed in a variety of natural organisms, such as the metabolism-based behaviour of bacteria like Escherichia coli and Azospirillum brasilense, where certain behaviours are not responses to environmental phenomena but, rather, to how well the organism’s metabolism is operating.

Theoretical work has shown that viability-based behaviours can provide a variety of adaptive and evolutionary advantages.

The next step in this research is to investigate the extent to which viability-based behaviour can bootstrap open-ended, complexity-increasing evolution in the following ways:

    • Investigate the extent to which viability-based behaviour improves evolvability in a computational model. In this project, we will use a computational model (perhaps similar to AVIDA) to compare the evolution of passively-stable systems (cf RNA-polymers) with the evolution of inherently unstable, self-maintaining systems that perform a form of viability-based behaviour.
    • Investigate the limitations of viability-based behaviour in a model that fully captures emergent viability-limits. Most models of adaptive behaviour simply assume certain viability limits. In this project, we will simulate a self-maintaining system with emergent viability limits and investigate how various forms of viability-based behaviour are (or are not) capable of responding to viability-affecting environmental changes.

We use a combination of mathematical and computational models in collaboration with chemists, biologists, physicists, philosophers and psychologists to investigate these topics.

About the researcher
Dr Matthew Egbert
School of Computer Science