Abstract
Developmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. Genome specifications for dynamics that can map specific gene expression patterns in early development onto specific point attractor patterns in later development are essentially impossible to discover by chance mutation alone, even for small networks. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude. These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes.
Highlights
Developmental dynamics in Boolean models of gene networks self-organize, either into point attractors or limit cycles, depending on the network interactions specified by a genome of evolvable bits
Can selection, modelled as a global optimization of the genotype by fitness maximization between lifetimes, exploit the emergence of structure in the local mapping from genotype to phenotype that occurs within lifetimes? Here we show that fitness landscapes can be modified by the intrinsic properties of dynamical network self-organization, via a simple, biologically plausible mechanism that is compatible with conventional descriptions of evolution by natural selection
The developmental dynamics of the network can be completely specified by a string comprising N = n2n binary digits
Summary
Developmental dynamics in Boolean models of gene networks self-organize, either into point attractors (stable repeating patterns of gene expression) or limit cycles (stable repeating sequences of patterns), depending on the network interactions specified by a genome of evolvable bits. We show that selection for approximate mappings, dynamically maintained in the states comprising limit cycles, can accelerate evolution by at least an order of magnitude These results suggest that self-organizing dynamics that occur within lifetimes can, in principle, guide natural selection across lifetimes. Assuming that the initial states are determined by such extrinsic factors, the problem for natural selection is to configure an N-dimensional genome such that the resulting network interactions will map a given set of initial states to a given set of point attractor states.
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