Evolution of biological functionality


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. 

AARS

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 the manner in which genetic coding can emerge in molecular systems that start out in a completely disordered state. 

Researcher: Associate Professor Peter Wills

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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 of 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. See Dr Matthew Egbert's page for a chemotactic oil-droplets video. 

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 not 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 environmnental phenomena but, rather, to how well the organism's metabolism is operating (see Figure 1).

 

relationships



Figure 1. Schematic diagram indicating different relationships between metabolism and behaviour.The top two figures show metabolism-independent behaviour, where the actions of the organism are not influenced by the metabolic-state of the organism, but are purely responses to the organism's environment. In contrast, the latter two relationships show behaviours that are influenced in an ongoing manner by the metabolic state of the organism - processes that are tightly related to the system's viability.

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.

Researcher: Dr Matthew Egbert

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