Bio-mimetic Evolutionary Reverse Engineering of Genetic Regulatory Networks

The effective reverse engineering of biochemical networks is one of the great challenges of systems biology. The contribution of this paper is two-fold: 1) We introduce a new method for reverse engineering genetic regulatory networks from gene expression data; 2) We demonstrate how nonlinear gene networks can be inferred from steady-state data alone. The reverse engineering method is based on an evolutionary algorithm that employs a novel representation called Analog Genetic Encoding (AGE), which is inspired from the natural encoding of genetic regulatory networks. AGE can be used with biologically plausible, nonlinear gene models where analytical approaches or local gradient based optimisation methods often fail. Recently there has been increasing interest in reverse engineering linear gene networks from steady-state data. Here we demonstrate how more accurate nonlinear dynamical models can also be inferred from steady-state data alone.


Editor(s):
Marchiori, E.
Moore, J.H.
Rajapakse, J. C.
Published in:
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings, 4447, 155-165
Presented at:
5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2007), València, 11.4.-13.4.07
Year:
2007
Publisher:
Springer-Verlag Berlin
Keywords:
Laboratories:




 Record created 2006-11-14, last modified 2018-03-17

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