System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Inference of gene regulatory networks using s-system and differential evolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The Regulatory Network Computational Device
Genetic Programming and Evolvable Machines
Genetic programming with genetic regulatory networks: genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.