Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge. Results: We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different sc...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Cédric Auliac, Vincent Frouin, Xavier Gidrol, Florence d'Alché-Buc
• Keywords: Algorithms, BMCBI 2008, Evolutionary Algorithms, Gene Regulatory Networks