Artificial Regulatory Networks Evolution
Abstract
Genetic network inference is one of the main challenges for computer scientists in cellular biology. We propose to use in silico experimental evolution to guide the development of inference algorithm by (i) developing general knowledge about genetic networks structure (and use this knowledge to develop inference heuristics), and (ii) generate large realistic benchmarks to support validation of inference algorithms. For this purpose, we develop the RAevol model which aims at simulating the evolution of regulatory networks.
Domains
Computer Science [cs]| Origin | Files produced by the author(s) |
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