一种快速高效的多个过表达实验的基因网络重构方法
A fast and efficient gene-network reconstruction method from multiple over-expression experiments
摘要：
基因调控网络的逆向工程是系统生物学面临的最大挑战之一。基因调控网络通常从一组单基因过表达和/或敲除实验中推断。从稳态基因表达或从各自的时间序列检索基因之间的功能关系。我们提出了一种新的算法的基因网络重建的基础上，从过表达实验的稳态基因芯片数据。该算法是基于一个线性基因动力学方程的直接向前的解决方案，其中实验数据作为第一个预测的解决方案。我们比较了算法的性能与近红外算法，无论是在著名的大肠杆菌实验数据和硅片实验。我们表明该算法的优越性，在正确重建的链接的数量，并讨论计算时间和鲁棒性。该算法不受组合爆炸问题的限制，原则上可以用于数千个基因的大型网络。
英文摘要：
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 of thousands of genes.
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