Complex Systems Research Group, Medical University of Vienna, A-1090, Austria.
BMC Bioinformatics. 2009 Aug 17;10:253. doi: 10.1186/1471-2105-10-253.
Reverse engineering of gene regulatory networks presents one of the big challenges in systems biology. Gene regulatory networks are usually inferred from a set of single-gene over-expressions and/or knockout experiments. Functional relationships between genes are retrieved either from the steady state gene expressions or from respective time series.
We present a novel algorithm for gene network reconstruction on the basis of steady-state gene-chip data from over-expression experiments. The algorithm is based on a straight forward solution of a linear gene-dynamics equation, where experimental data is fed in as a first predictor for the solution. We compare the algorithm's performance with the NIR algorithm, both on the well known E. coli experimental data and on in-silico experiments.
We show superiority of the proposed algorithm in the number of correctly reconstructed links and discuss computational time and robustness. The proposed algorithm is not limited by combinatorial explosion problems and can be used in principle for large networks.
基因调控网络的反向工程是系统生物学面临的重大挑战之一。基因调控网络通常是从一组过表达和/或基因敲除实验中推断出来的。基因之间的功能关系是从稳态基因表达或各自的时间序列中检索到的。
我们提出了一种基于过表达实验稳态基因芯片数据的基因网络重建的新算法。该算法基于线性基因动力学方程的直接求解，其中实验数据作为求解的第一个预测器输入。我们将该算法的性能与 NIR 算法进行了比较，同时比较了它们在著名的大肠杆菌实验数据和计算机模拟实验上的性能。
我们表明，在所重建的正确连接数量方面，所提出的算法具有优越性，并讨论了计算时间和鲁棒性。所提出的算法不受组合爆炸问题的限制，原则上可以用于大型网络。
