J. Comput. Biol. - Optimally orienting physical networks.

Tópicos

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Resumo

In a network orientation problem, one is given a mixed graph, consisting of directed and undirected edges, and a set of source-target vertex pairs. The goal is to orient the undirected edges so that a maximum number of pairs admit a directed path from the source to the target. This NP-complete problem arises in the context of analyzing physical networks of protein-protein and protein-DNA interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in protein-protein interactions is often unknown or cannot be measured en masse. One then tries to infer this information by using causality data on pairs of genes such that the perturbation of one gene changes the expression level of the other gene. Here we provide a first polynomial-size ILP formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient protein-protein interactions in yeast and measure our performance using edges with known orientations. We find that our algorithm achieves high accuracy and coverage in the orientation, outperforming simplified algorithmic variants that do not use information on edge directions. The obtained orientations can lead to a better understanding of the structure and function of the network.

Resumo Limpo

network orient problem one given mix graph consist direct undirect edg set sourcetarget vertex pair goal orient undirect edg maximum number pair admit direct path sourc target npcomplet problem aris context analyz physic network proteinprotein proteindna interact latter natur direct transcript factor gene direct signal flow proteinprotein interact often unknown measur en mass one tri infer inform use causal data pair gene perturb one gene chang express level gene provid first polynomials ilp formul problem can effici solv current network appli algorithm orient proteinprotein interact yeast measur perform use edg known orient find algorithm achiev high accuraci coverag orient outperform simplifi algorithm variant use inform edg direct obtain orient can lead better understand structur function network

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