Ren Hai-Peng, Huang Xiao-Na, Hao Jia-Xuan
IEEE/ACM Trans Comput Biol Bioinform. 2016 May-Jun;13(3):571-7. doi: 10.1109/TCBB.2015.2430321.
Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.
鲁棒适应性在基因调控网络中起着关键作用，并且被认为是生物体或细胞在波动环境中生存的重要属性。本文针对所有可能的拓扑结构，用米氏速率方程对一个简化的三节点酶网络进行建模，并使用多目标遗传算法获得了具有满意适应性的一族拓扑结构及相应的网络参数集。与耗时的穷举搜索方法相比，所提方法显著提高了计算效率。该方法为搜索可行的拓扑结构及相应参数集提供了一种系统方法，以使基因调控网络具有鲁棒适应性。所提方法由于其通用性和简单性，可用于解决生物网络中更复杂的问题。
