Department of Information Management, National Sun Yat-sen University, 70, Lienhai Road, Kaohsiung, Taiwan.
BMC Bioinformatics. 2012 May 8;13 Suppl 7(Suppl 7):S8. doi: 10.1186/1471-2105-13-S7-S8.
Reconstructing gene regulatory networks (GRNs) from expression data is one of the most important challenges in systems biology research. Many computational models and methods have been proposed to automate the process of network reconstruction. Inferring robust networks with desired behaviours remains challenging, however. This problem is related to network dynamics but has yet to be investigated using network modeling.
We propose an incremental evolution approach for inferring GRNs that takes network robustness into consideration and can deal with a large number of network parameters. Our approach includes a sensitivity analysis procedure to iteratively select the most influential network parameters, and it uses a swarm intelligence procedure to perform parameter optimization. We have conducted a series of experiments to evaluate the external behaviors and internal robustness of the networks inferred by the proposed approach. The results and analyses have verified the effectiveness of our approach.
Sensitivity analysis is crucial to identifying the most sensitive parameters that govern the network dynamics. It can further be used to derive constraints for network parameters in the network reconstruction process. The experimental results show that the proposed approach can successfully infer robust GRNs with desired system behaviors.
从表达数据中重建基因调控网络（GRNs）是系统生物学研究中最重要的挑战之一。已经提出了许多计算模型和方法来自动化网络重建的过程。然而，推断具有所需行为的稳健网络仍然具有挑战性。这个问题与网络动态有关，但尚未使用网络建模进行研究。
我们提出了一种增量进化方法来推断 GRNs，该方法考虑了网络稳健性并且可以处理大量的网络参数。我们的方法包括敏感性分析程序，用于迭代选择最有影响力的网络参数，并且使用群体智能程序进行参数优化。我们已经进行了一系列实验来评估所提出的方法推断的网络的外部行为和内部稳健性。结果和分析验证了我们方法的有效性。
敏感性分析对于确定控制网络动态的最敏感参数至关重要。它还可以进一步用于在网络重建过程中为网络参数推导约束。实验结果表明，所提出的方法可以成功推断具有所需系统行为的稳健 GRNs。
