title: Evolving phenotypic networks in silico.
abstract: Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype.
authors: Ernest Rutherford Physics Building, McGill University, 3600 rue University, H3A2T8 Montreal, QC, Canada. Electronic address: paulf@physics.mcgill.ca.
authors: Paul
authors: P
authors: François
mesh_terms: D000222:Adaptation, Physiological / Q000235:genetics; D000465:Algorithms; D000818:Animals; D019521:Body Patterning / Q000235:genetics*; D003198:Computer Simulation; D019143:Evolution, Molecular; D053263:Gene Regulatory Networks / Q000235:genetics*; D008957:Models, Genetic; D012641:Selection, Genetic; D014714:Vertebrates / Q000196:embryology / Q000235:genetics / Q000254:growth & development
keywords: Biochemical adaptation; Evolution in silico; Fitness; Immunology; Ligand discrimination; Somitogenesis