Knabe Johannes F, Wegner Katja, Nehaniv Chrystopher L, Schilstra Maria J
Biological and Neural Computation Laboratory and Adaptive Systems Research Group, STRI, University of Hertfordshire, Hatfield, Hertfordshire, UK.
Methods Mol Biol. 2010;673:297-321. doi: 10.1007/978-1-60761-842-3_19.
A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the "fitter" individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert's French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation.
遗传算法（GA）是一种模仿达尔文进化过程中出现的过程来解决计算问题的程序。遗传算法通过对问题的一组可能解（编码为“基因组”中的字符串）进行“变异”和“重组”来引入变异，并使用选择程序使这个群体进化，这些选择程序有利于基因库逐渐富集“更适应”个体的基因组。遗传算法特别适用于寻求有效系统设计或一组参数值的优化问题。在自然界中，基因调控网络（GRN）构成了基因表达水平调控的基本控制层。基因调控网络由基因与其基因产物之间的调控相互作用组成，尤其在单个受精卵细胞发育成完全成熟生物体的过程中起着基础作用。本文描述了如何应用遗传算法来寻找能够捕捉基因调控网络基本特征的模型的功能调控方案和参数值。讨论了进化计算和基因调控网络建模背后的核心思想，以及遗传算法设计和使用中的注意事项，并用一个扩展示例进行了说明。在这个示例中，基于刘易斯·沃尔珀特的用于位置指定的法国国旗模型，为一个发育系统寻找一个类似基因调控网络的控制器，在该模型中，正在生长的胚胎中的细胞分泌和检测形态发生素，以实现细胞分化的特定空间模式。
