Evolving robust gene regulatory networks.

Nasimul Noman,Taku Monjo,Hitoshi Iba,Pablo Moscato,Alberto De La Fuente

doi:10.1371/journal.pone.0116258

Nasimul Noman, Taku Monjo + Show 3 more

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https://doi.org/10.1371/journal.pone.0116258

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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.

Highlights

Robustness is a fundamental characteristic that runs throughout every form of life from unicellular bacteria to sophisticated mammals
Automated design procedure for robust biological modules will greatly contribute in synthesizing complex living systems thereby take the discipline to its level
Current research presents an in silico method for automatic design of biological systems or modules that can exhibit robust behavior

Summary

Introduction

Robustness is a fundamental characteristic that runs throughout every form of life from unicellular bacteria to sophisticated mammals. The source of perturbation could be internal such as mutation and intrinsic noises or external such as environmental changes These perturbations, whether internal or external, cause to fluctuate the biochemical parameters widely but by virtue of its robustness the system maintains the behavior [17]. Another biological attribute, very closely related to robustness, is redundancy which is the ability of a system to function reliably in spite of one or some of its component failure. Throughout this work, we adopted the former definition of robustness

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