COMPLEXITY OF THE SEARCH SPACE IN A MODEL OF ARTIFICIAL EVOLUTION OF GENE REGULATORY NETWORKS CONTROLLING 3D MULTICELLULAR MORPHOGENESIS
The question of what properties of biological systems allow for efficient evolutionary search in complex fitness landscapes (evolvability) is one of the central interests both for the research in the field of evolutionary biology and artificial life. Here, we attempt to address this issue by using a model of 3D multicellular development in which cell fate is determined by differential gene expression in each cell. In our model, cells can vary in size and can move freely in 3D space, affected by forces of adhesion and repulsion. The development relies on an indirect mapping between the genotype and the morphology (the phenotype). Cell differentiation is allowed by positional information provided by diffusible factors. The state of the gene regulatory network (GRN) coded by the genome determines the cell fate (such as division, death, growth). The genetic elements in our systems define points in N-dimensional space. The connectivity in the GRN is determined by the proximity of these points; one can imagine the evolutionary process as their movement in space. Changing the number of dimensions of this space allows to ask directly the questions about the effect of the complexity of the search space on the efficiency of the evolutionary search. Higher dimensionality results in a larger search space, but in our model this search space can still be explored thanks to the action of genetic operators that allow for duplications of genetic elements, a mutational mechanism that allows for regulatory innovations in the network.
Artificial embryogeny, gene regulatory network, evo-devo, complexity of the fitness landscape, neutrality, evolvability
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