Centre for Complexity Science, University of Warwick, Coventry, West Midlands, United Kingdom.
PLoS One. 2012;7(5):e36010. doi: 10.1371/journal.pone.0036010. Epub 2012 May 7.
We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman's Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. In silico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks.
我们研究了布尔网络（BNs）对突变的敏感性。我们对布尔网络作为基因调控网络（GRNs）的模型很感兴趣。我们采用了 Ribeiro 和 Kauffman 的遍历集，并利用它来研究 BN 的长期动力学。我们将 BN 的敏感性定义为在所有可能的交互作用缺失突变下，其遍历集结构的平均变化。通过计算机模拟实验，我们选择性地进化 BN 以提高对交互作用缺失的敏感性。我们发现，最大敏感性通常是可以实现的，并且导致 BN 拓扑上达到平衡，即它们朝着具有相似数量的抑制性和兴奋性相互作用的网络结构进化。就动力学而言，进化出的主要敏感性策略是构建具有由单个长极限环主导的遍历集的 BN，这种极限环很容易被突变所破坏。我们在干细胞分化的背景下讨论了我们发现的相关性，并提出了多能干细胞与我们进化出的敏感网络之间的关系。
