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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Reachability Analysis in Probabilistic Biological Networks
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Reachability Analysis in Probabilistic Biological Networks

机译：概率生物学网络中的可达性分析

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摘要

Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e.,sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called (robabilistic ability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial.It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better timecomplexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.
机译：细胞外分子通过在特殊的膜受体（即来源）处引发信号来触发细胞内的反应，然后该信号通过蛋白质之间的各种相互作用链传递至报告基因（即靶标）。在研究细胞对细胞外事件的反应中，了解这种信号是否可以从膜受体到达报道分子至关重要。由于交互数据的不可靠性，这个问题变得非常复杂。在本文中，我们开发了一种新的方法，称为（增强能力），该方法可以精确地计算当潜在的信号网络不确定时，信号可以从给定的受体集合到达给定的报告基因集合的概率。没有已知的多项式时间解，这是一个非常困难的计算问题。 PReach将每个不确定的交互表示为二元多项式，将可达性问题转换为多项式乘法问题。我们介绍了新颖的多项式折叠算子，该算子将多项式项与源和目标之间的可能路径以及将源与目标分开的削减相关联。这些运算符大大减少了多项式项的数量，从而缩短了运行时间。与最近针对此问题的解决方案相比，PReach具有更好的时间复杂性。我们在真实数据集上的实验结果表明，与最新方法相比，这种改进导致运行时间减少了几个数量级。本文中使用的所有数据集，实现的软件和比对方法可从http://bioinformatics.cise.ufl.edu/PReach/获得。

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