EPSRC Network on Evolvability in Biology & Software Systems

Evolvability, Genetics & Development in Natural and Constructed Systems: Abstracts of the EPSRC Evolvability Network Symposium

Tewin Bury Farm Hotel, Hertfordshire, England, UK
26-28 August 2003


University of Hertfordshire Computer Science Technical Report 389
C. L. Nehaniv, P. J. Bentley & S. Kumar (Editors)

The Role of Genetic Regulatory Networks in Biological Evolution

The Role of Genetic Regulatory Networks in Biological Evolution

MARIA SCHILSTRA

Biocomputation Research Laboratory
Science and Technology Research Centre - University of Hertforshire
College lane - AL10 9AB Hatfield - Hertfordshire - UK

m.j.1.schilstra@herts.ac.uk


At the basis of the development of a single fertilized egg cell into a fully functional, multicellular organism are genetic regulatory networks (GRNs): networks of genes that regulate the activity of other genes. The genes at the ``output layer'' of such networks produce the building material for the machinery that is used to duplicate and split the genetic blueprint, build new cells, and provide communication lines between the cells. The regulatory genes in the middle layers control production and breakdown rates, and determine thus how many building blocks and machines are present in each cell at any one time.

For a long time it was thought that changes in the machinery itself are at the heart of biological evolution. However, most changes in the structure of a machine are likely to be detrimental to its function, which, in turn, could well be lethal to the organism. The prevailing idea these days is that evolution is driven mainly by small changes to the production and breakdown rates of the machines and building blocks. The effect of changes to regulatory elements is usually much smaller than changes to the machines themselves, but at least changes in production or breakdown rates are much less likely to be detrimental. Therefore, individuals with changes in their regulatory system are much more likely to survive, and to pass on these changes to the next generation, than individuals with changed machines.

I will briefly talk about NetBuilder, a program that we have developed to visualise the structure and predict the behaviour of biological developmental GRNs. I will then discuss the merits of various techniques that are used to simulate such networks, and, finally, put forward some ideas as to how the concepts outlined above could be exploited to create evolvable multicellular systems.