Using evolutionary computations to understand the design and evolution of gene and cell regulatory networks

作     者：Spirov, Alexander Holloway, David 

作者机构：SUNY Stony Brook Stony Brook NY 11794 USA SUNY Stony Brook CEWIT Stony Brook NY 11794 USA IM Sechenov Evolutionary Physiol & Biochem Inst St Petersburg 194223 Russia British Columbia Inst Technol Dept Math Burnaby BC Canada 

出 版 物：《METHODS》 (方法)

年 卷 期：2013年第62卷第1期

页      面：39-55页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 07[理学] 

基　　金：U.S. NIH [R01-GM072022] 

主　　题：Gene regulatory networks Evolutionary computations Insect segmentation Robustness Simulations of evolution Evolution-development 

摘      要：This paper surveys modeling approaches for studying the evolution of gene regulatory networks (GRNs). Modeling of the design or wiring of GRNs has become increasingly common in developmental and medical biology, as a means of quantifying gene-gene interactions, the response to perturbations, and the overall dynamic motifs of networks. Drawing from developments in GRN design modeling, a number of groups are now using simulations to study how GRNs evolve, both for comparative genomics and to uncover general principles of evolutionary processes. Such work can generally be termed evolution in silico. Complementary to these biologically-focused approaches, a now well-established field of computer science is Evolutionary Computations (ECs), in which highly efficient optimization techniques are inspired from evolutionary principles. In surveying biological simulation approaches, we discuss the considerations that must be taken with respect to: (a) the precision and completeness of the data (e.g. are the simulations for very close matches to anatomical data, or are they for more general exploration of evolutionary principles);(b) the level of detail to model (we proceed from coarse-grained evolution of simple gene-gene interactions to fine-grained evolution at the DNA sequence level);(c) to what degree is it important to include the genome s cellular context;and (d) the efficiency of computation. With respect to the latter, we argue that developments in computer science EC offer the means to perform more complete simulation searches, and will lead to more comprehensive biological predictions. (C) 2013 Elsevier Inc. All rights reserved.