title: Inferring gene regulatory networks using differential evolution with local search heuristics.
abstract: We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time series data of gene expression using the decoupled Ssystem formalism. We propose an Information Criteria based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE) based fitness evaluation. A hill-climbing local-search method has been incorporated in our evolutionary algorithm for efficiently attaining the skeletal architecture which is most frequently observed in biological networks. The suitability of the method is tested in gene circuit reconstruction experiments, varying the network dimension and/or characteristics, the amount of gene expression data used for inference and the noise level present in expression profiles. The reconstruction method inferred the network topology and the regulatory parameters with high accuracy. Nevertheless, the performance is limited to the amount of expression data used and the noise level present in the data. The proposed fitness function has been found more suitable for identifying correct network topology and for estimating the accurate parameter values compared to the existing ones. Finally, we applied the methodology for analyzing the cell-cycle gene expression data of budding yeast and reconstructed the network of some key regulators.
authors.affiliation: Iba Laboratory, Graduate School of Frontier Sceinces, University of Tokyo, Tokyo, Japan. noman@iba.k.u-tokyo.ac.jp
authors.forename: Nasimul
authors.initials: N
authors.lastname: Noman
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Iba
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001842:Bone and Bones / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D014157:Transcription Factors