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ResponseNet. revealing signaling and regulatory networks linking genetic and transcriptomic screening data. CSE891-001 2012 Fall. 2009. Overview. ResponseNet identifies high-probability signaling and regulatory paths that connect proteins to genes
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ResponseNet revealing signaling and regulatory networks linking genetic and transcriptomic screening data CSE891-001 2012 Fall
Overview • ResponseNet identifies high-probability signaling and regulatory paths that connect proteins to genes • ResponseNet proved to be particularly useful for identifying cellular response to stimuli • Given weighted lists of stimulus-related proteins and stimulus-related genes, ResponseNet searches a given interactome for a sparse, high-probability sub-network that connects these proteins to these genes through signaling and regulatory paths • The identified sub-network and its gene ontology (GO) enrichment illuminate the pathways that underlying the cellular response to the stimulus
Stimulus-related proteins intermediary proteins stimulus-related genes TFs The signaling and regulatory sub-network, by which stimulus-related proteins detected by genetic screens may lead to the measured transcriptomic response.
Challenges • Prediction of signaling and regulatory response pathways in the yeast is extremely challenging • Only the pathways of a handful of stimuli were fully characterized • Due to the vast number of known interactions, a search for all interaction paths connecting stimulus-related proteins to genes typically results in a ‘hairball’ sub-network that is very hard to interpret. • ResponseNet is designed as a network-optimization approach that uses a graphical model in which: • proteins and genes are represented as separate network nodes • a directed edge leads from a protein node to a gene node only if they correspond to a transcription factor and its target gene • each network edge is associated with a probability that reflects its likelihood • Mathematically, ResponseNetis formulated as a minimum-cost flow optimization problem
Minimum-cost Flow algorithm • Flow algorithms deliver an abstract flow from a source node (S) to a sink node (T) through the edges of a network, which are associated with a capacity that limits the flow and with a cost. • Because S and T are the two endpoints for the flow, by linking S to the stimulus-related proteins and the stimulus-related genes to T, the flow is forced to find paths that connect the stimulus-related proteins and genes through PPIs and PDIs. • Aim to maximize the flow between S and T, while minimizing the cost of the connecting paths. Hence, by setting the cost of an edge to the negative log of its probability, a sparse, high-probability connecting sub-network is obtained.
Minimum-cost Flow algorithm • The minimum-cost flow problem can be solved efficiently using linear programmingtools. • A typical optimal solution connects a subset of the stimulus related proteins to a subset of the stimulus-related genes through known interactions and intermediary proteins. • These interactions and proteins are weighted by the amount of flow they pass, thus illuminating core versus peripheral components of the response.
Linear Programming The solution F ={fij>0} defined the predicted response network
LOQO • LOQO is a system for solving smooth constrained optimization problems. The problems can be linear or nonlinear, convex or non-convex, constrained or unconstrained. • The only real restriction is that the functions defining the problem be smooth. • If the problem is convex, LOQO finds a globally optimal solution. Otherwise, it finds a locally optimal solution near to a given starting point.
Results The highly ranked part of the ResponseNet input ResponseNet
Conclusion • Both PhysicalNet and ResponseNet search for the best paths that link the input and the output. • But time-series gene expression data is difficult to use • Zif Bar-Joseph’s group developed a new model called SDREM to solve this problem
