Hypothesis Generation in Signaling Networks

Abstract

Biological signaling networks comprise the chemical processes by which cells detect and respond to changes in their environment. Such networks have been implicated in the regulation of important cellular activities, including cellular reproduction, mobility, and death. Though technological and scientific advances have facilitated the rapid accumulation of information about signaling networks, utilizing these massive information resources has become infeasible except through computational methods and computer-based tools. To date, visualization and simulation tools have received significant emphasis. In this paper, we present a graph-theoretic formalization of biological signaling network models that are in wide but informal use, and formulate two problems on the graph: the Constrained Downstream and Minimum Knockout Problems. Solutions to these problems yield qualitative tools for generating hypotheses about the networks, which can then be experimentally tested in a laboratory setting. Using established graph algorithms, we provide a solution to the Constrained Downstream Problem. We also show that the Minimum Knockout Problem is NP-Hard, propose a heuristic, and assess its performance. In tests on the Epidermal Growth Factor Receptor (EGFR) network, we find that our heuristic reports the correct solution to the problem in seconds. Source code for the implementations of both solutions is available from the authors upon request.

Network-based characterization of drug-regulated genes, drug targets, and toxicity

“…Several studies have used PPI networks to find optimal sets of targets for affecting disease processes while minimizing side effects [19,55,68]. Ruths et al [55] identified minimum sets of nodes whose removal would block downstream communication from signalling nodes, and similarly, Dasika et al [19] identified sets of drug targets for blocking a specific cellular processes while minimizing effects on other processes.…”

Functional Proteomic Analysis of Signaling Networks and Response to Targeted Therapy

“…For each of these results, we used the constrained downstream algorithm [14] to enumerate all paths between the two proteins in the model. This algorithm performs an exhaustive search of a pathway graph model for all paths connecting one set of proteins to another.…”

Integer programming-based approach to allocation of reporter genes for cell array analysis

“…This approach is reasonable because a close relationship between integer programming and the set cover is known [12]. It should be noted that our approach is significantly different from that in [11]: (i) problems and network representations are different from each other, (ii) optimality of the solution is not guaranteed in [11], whereas optimality is guaranteed in our approach.…”

“…However, these techniques require statements about the property of individual reactions in networks, details of which are often unavailable. Ruths et al recently proposed a framework for computational hypothesis testing in which signaling networks are represented as bipartite directed graphs [11]. In their framework, each network contains two types of nodes: nodes corresponding to molecules and nodes corresponding to reactions.…”