Journal
SEMINARS IN CELL & DEVELOPMENTAL BIOLOGY
Volume 35, Issue -, Pages 90-97Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.semcdb.2014.06.012
Keywords
Evolution in silico; Biochemical adaptation; Immunology; Ligand discrimination; Somitogenesis; Fitness
Categories
Funding
- Natural Science and Engineering Research Council of Canada (NSERC)
- Fond de Recherche Quebecois Nature et Technologie (FRQNT)
- Human Frontier Science Program (HFSP)
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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 pre-defined 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. (C) 2014 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
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