title: Inference of S-system models of gene regulatory networks using immune algorithm.
abstract: The S-system model is one of the nonlinear differential equation models of gene regulatory networks, and it can describe various dynamics of the relationships among genes. If we successfully infer rigorous S-system model parameters that describe a target gene regulatory network, we can simulate gene expressions mathematically. However, the problem of finding an optimal S-system model parameter is too complex to be solved analytically. Thus, some heuristic search methods that offer approximate solutions are needed for reducing the computational time. In previous studies, several heuristic search methods such as Genetic Algorithms (GAs) have been applied to the parameter search of the S-system model. However, they have not achieved enough estimation accuracy. One of the conceivable reasons is that the mechanisms to escape local optima. We applied an Immune Algorithm (IA) to search for the S-system parameters. IA is also a heuristic search method, which is inspired by the biological mechanism of acquired immunity. Compared to GA, IA is able to search large solution space, thereby avoiding local optima, and have multiple candidates of the solutions. These features work well for searching the S-system model. Actually, our algorithm showed higher performance than GA for both simulation and real data analyses.
authors.affiliation: Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan.
authors.forename: Tomoyoshi
authors.initials: T
authors.lastname: Nakayama
authors.forename: Shigeto
authors.initials: S
authors.lastname: Seno
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Takenaka
authors.forename: Hideo
authors.initials: H
authors.lastname: Matsuda
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks / Q000276:immunology*