Evolving robust gene regulatory networks

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...

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Bibliographic Details
Published in:PloS one 2015-01, Vol.10 (1), p.e0116258-e0116258
Main Authors: Noman, Nasimul, Monjo, Taku, Moscato, Pablo, Iba, Hitoshi
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Architectural engineering
Bioinformatics
Bistability
CAD
Circadian rhythm
Complexity
Computational Biology - methods
Computer aided design
Computer engineering
Computer science
Computer simulation
E coli
Electrical engineering
Evolution
Evolution (Biology)
Evolutionary algorithms
Fitness
Gene expression
Gene Regulatory Networks
Genes
Genetic algorithms
Genomes
Mammals
Models, Genetic
Monte Carlo Method
Monte Carlo simulation
Network topologies
Reproductive fitness
Researchers
Robustness (mathematics)
Selection, Genetic
Studies
Synthetic biology
Topology
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0116258