title: Evolving robust gene regulatory networks.
abstract: Design and implementation of robust network modules is essential for construction of complex biological systems through hierarchical assembly of 'parts' and 'devices'. The robustness of gene regulatory networks (GRNs) is ascribed chiefly to the underlying topology. The automatic designing capability of GRN topology that can exhibit robust behavior can dramatically change the current practice in synthetic biology. A recent study shows that Darwinian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identifying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation approach for efficient calculation of topological robustness which is computationally very intensive. The proposed framework was verified using two classic GRN behaviors: oscillation and bistability, although the framework is generalized for evolving other types of responses. The algorithm identified robust GRN architectures which were verified using different analysis and comparison. Analysis of the results also shed light on the relationship among robustness, cooperativity and complexity. This study also shows that nature has already evolved very robust architectures for its crucial systems; hence simulation of this natural process can be very valuable for designing robust biological systems.
authors.affiliation: The Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Newcastle, New South Wales, Australia; School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, The University of Newcastle, Newcastle, New South Wales, Australia.
authors.forename: Nasimul
authors.initials: N
authors.lastname: Noman
authors.affiliation: Department of Medical Genome Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
authors.forename: Taku
authors.initials: T
authors.lastname: Monjo
authors.affiliation: The Priority Research Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Newcastle, New South Wales, Australia; School of Electrical Engineering and Computer Science, Faculty of Engineering and Built Environment, The University of Newcastle, Newcastle, New South Wales, Australia.
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
authors.affiliation: Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo, Tokyo, Japan.
authors.forename: Hitoshi
authors.initials: H
authors.lastname: Iba
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D009010:Monte Carlo Method; D012641:Selection, Genetic