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An Algorithm for Orienting Graphs Based on CauseEffect Pairs and Its Applications to Orienting Protein Networks
"... We consider a graph orientation problem arising in the study of biological networks. Given an undirected graph and a list of ordered sourcetarget pairs, the goal is to orient the graph so that a maximum number of pairs will admit a directed path from the source to the target. We show that the prob ..."
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Cited by 15 (4 self)
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We consider a graph orientation problem arising in the study of biological networks. Given an undirected graph and a list of ordered sourcetarget pairs, the goal is to orient the graph so that a maximum number of pairs will admit a directed path from the source to the target. We show that the problem is NPhard and hard to approximate to within a constant ratio. We then study restrictions of the problem to various graph classes, and provide an O(log n) approximation algorithm for the general case. We show that this algorithm achieves very tight approximation ratios in practice and is able to infer edge directions with high accuracy on both simulated and real network data.
An integrative approach for causal gene identification and gene regulatory pathway inference
 Bioinformatics
, 2006
"... doi:10.1093/bioinformatics/btl234 ..."
SPINE: A Framework for SignalingRegulatory Pathway Inference from CauseEffect Experiments
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Approximation Algorithms for Orienting Mixed Graphs
"... Abstract. Graph orientation is a fundamental problem in graph theory that has recently arisen in the study of signalingregulatory pathways in protein networks. Given a graph and a list of ordered sourcetarget vertex pairs, it calls for assigning directions to the edges of the graph so as to maximi ..."
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Abstract. Graph orientation is a fundamental problem in graph theory that has recently arisen in the study of signalingregulatory pathways in protein networks. Given a graph and a list of ordered sourcetarget vertex pairs, it calls for assigning directions to the edges of the graph so as to maximize the number of pairs that admit a directed sourcetotarget path. When the input graph is undirected, a sublogarithmic approximation is known for the problem. However, the approximability of the biologicallyrelevant variant, in which the input graph has both directed and undirected edges, was left open. Here we give the first approximation algorithm to this problem. Our algorithm provides a sublinear guarantee in the general case, and logarithmic guarantees for structured instances. Key words: proteinprotein interaction network, mixed graph, graph orientation, approximation algorithm 1
Exploiting bounded signal flow for graph orientation based on causeeffect pairs
 In Proceedings of the 1st International ICST Conference on Theory and Practice of Algorithms in (Computer) Systems (TAPAS 2011
"... Background: We consider the following problem: Given an undirected network and a set of sender–receiver pairs, direct all edges such that the maximum number of “signal flows ” defined by the pairs can be routed respecting edge directions. This problem has applications in understanding protein intera ..."
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Cited by 8 (0 self)
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Background: We consider the following problem: Given an undirected network and a set of sender–receiver pairs, direct all edges such that the maximum number of “signal flows ” defined by the pairs can be routed respecting edge directions. This problem has applications in understanding protein interaction based cell regulation mechanisms. Since this problem is NPhard, research so far concentrated on polynomialtime approximation algorithms and tractable special cases. Results: We take the viewpoint of parameterized algorithmics and examine several parameters related to the maximum signal flow over vertices or edges. We provide several fixedparameter tractability results, and in one case a sharp complexity dichotomy between a lineartime solvable case and a slightly more general NPhard case. We examine the value of these parameters for several realworld network instances. Conclusions: Several biologically relevant special cases of the NPhard problem can be solved to optimality. In this way, parameterized analysis yields both deeper insight into the computational complexity and practical solving strategies. Background Current technologies [1] like twohybrid screening can
Optimally orienting physical networks
 In Proceedings of the 15th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2011), volume 6577 of LNCS
, 2011
"... Abstract. In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of sourcetarget 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 problem ..."
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Cited by 8 (3 self)
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Abstract. In a network orientation problem one is given a mixed graph, consisting of directed and undirected edges, and a set of sourcetarget 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 problem is NPcomplete and no approximation algorithms are known for it. It arises in the context of analyzing physical networks of proteinprotein and proteindna interactions. While the latter are naturally directed from a transcription factor to a gene, the direction of signal flow in proteinprotein 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 polynomialsize ilp formulation for this problem, which can be efficiently solved on current networks. We apply our algorithm to orient proteinprotein 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 better understanding of the structure and function of the network. Key words: network orientation, proteinprotein interaction, proteindna interaction, integer linear program, mixed graph 1
Discovering Pathways by Orienting Edges in Protein Interaction Networks Supporting Information
"... Here we present additional theoretical and experimental results, algorithm pseudocode, and details regarding both the datasets used and our implementations. Source code for our algorithms is available at ..."
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Here we present additional theoretical and experimental results, algorithm pseudocode, and details regarding both the datasets used and our implementations. Source code for our algorithms is available at
The Factor Graph Network Model for Biological Systems
 Proc. of RECOMB 2005
, 2005
"... Abstract. We introduce an extended computational framework for studying biological systems. Our approach combines formalization of existing qualitative models that are in wide but informal use today, with probabilistic modeling and integration of high throughput experimental data. Using our methods, ..."
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Abstract. We introduce an extended computational framework for studying biological systems. Our approach combines formalization of existing qualitative models that are in wide but informal use today, with probabilistic modeling and integration of high throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph and the framework accommodates partial measurements of diverse biological elements. We develop methods for inference and learning in the model. We compare the performance of standard inference algorithms and tailormade ones and show that hidden variables can be reliably inferred even in the presence of feedback loops and complex logic. We develop a formulation for the learning problem in our model which is based on deterministic hypothesis testing, and show how to derive pvalues for learned model features. We test our methodology and algorithms on both simulated and real yeast data. In particular, we use our method to study the response of S. cerevisiae to hyperosmotic shock, and explore uncharacterized logical relations between important regulators in the system. 1
Modeling the Combinatorial Functions of Multiple Transcription Factors
"... A considerable fraction of gene promoters are bound by multiple transcription factors. It is therefore important to understand how such factors interact in regulating the genes. In this paper, we propose a computational method to identify groups of coregulated genes and the corresponding regulatory ..."
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A considerable fraction of gene promoters are bound by multiple transcription factors. It is therefore important to understand how such factors interact in regulating the genes. In this paper, we propose a computational method to identify groups of coregulated genes and the corresponding regulatory programs of multiple transcription factors from proteinDNA binding and gene expression data. The key concept is to characterize a regulatory program in terms of two properties of individual transcription factors: the function of a regulator as an activator or a repressor, and its direction of effectiveness as necessary or sufficient. We apply a greedy algorithm to find the regulatory models which best explain the available data. Empirical analysis indicates that the inferred regulatory models agree with known combinatorial interactions between regulators and are robust against various parameter choices. Key words: combinatorial function, gene regulation. 1.
PT: De novo Signaling Pathway Predictions based on ProteinProtein Interaction, Targeted Therapy, and Protein Microarray Analysis
 Proceedings of the RECOMB Satellite Workshop on Systems Biology and Proteomics, Lecture Notes in Bioinformatics (LNBI #4466
"... Abstract. Mapping intracellular signaling networks is a critical step in developing an understanding of and treatments for many devastating diseases. The predominant ways of discovering pathways in these networks are knockout and pharmacological inhibition experiments. However, experimental evidenc ..."
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Abstract. Mapping intracellular signaling networks is a critical step in developing an understanding of and treatments for many devastating diseases. The predominant ways of discovering pathways in these networks are knockout and pharmacological inhibition experiments. However, experimental evidence for new pathways can be difficult to explain within existing maps of signaling networks. In this paper, we present a novel computational method that integrates pharmacological intervention experiments with protein interaction data in order to predict new signaling pathways that explain unexpected experimental results. Biologists can use these hypotheses to design experiments to further elucidate underlying signaling mechanisms or to directly augment an existing signaling network model. When applied to experimental results from human breast cancer cells targeting the epidermal growth factor receptor (EGFR) network, our method proposes several new, biologicallyviable pathways that explain the evidence for a new signaling pathway. These results demonstrate that the method has potential for aiding biologists in generating hypothetical pathways to explain experimental findings. Our method is implemented as part of the PathwayOracle toolkit and is available from the authors upon request. 1