RESEARCH ARTICLE Evolving Robust Gene Regulatory Networks

Design and implementation of robust network modules is essential for construction of com-plex biological systems through hierarchical assembly of ‘parts ’ and ‘devices’. The robust-ness 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 dra-matically change the current practice in synthetic biology. A recent study shows that Dar-winian evolution can gradually develop higher topological robustness. Subsequently, this work presents an evolutionary algorithm that simulates natural evolution in silico, for identi-fying network topologies that are robust to perturbations. We present a Monte Carlo based method for quantifying topological robustness and designed a fitness approximation ap-proach for efficient calculation of topological robustness which is computationally very inten-sive. 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 analy-sis and comparison. Analysis of the results also shed light on the relationship among robust-ness, 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