Signaling hypergraphs

2015

Bioinformatics

Self Cite

Motivation: Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk. Existing computational methods to discover such pathway pairs rely on simple overlap statistics. Results: We present XTALK, a path-based approach for identifying pairs of pathways that may crosstalk. XTALK computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, XTALK reports the precise interactions and mechanisms that support the identified crosstalk. We applied XTALK to signaling pathways in the KEGG and NCI-PID databases. We manually curated a gold standard set of 132 crosstalking pathway pairs and a set of 140 pairs that did not crosstalk, for which XTALK achieved an area under the receiver operator characteristic curve of 0.65, a 12% improvement over the closest competing approach. The area under the receiver operator characteristic curve varied with the pathway, suggesting that crosstalk should be evaluated on a pathway-by-pathway level. We also analyzed an extended set of 658 pathway pairs in KEGG and to a set of more than 7000 pathway pairs in NCI-PID. For the top-ranking pairs, we found substantial support in the literature (81% for KEGG and 78% for NCI-PID). We provide examples of networks computed by XTALK that accurately recovered known mechanisms of crosstalk. Availability and implementation: The XTALK software is available at

Section: Discussionsupporting

confidence: 59%

Xtalk: a path-based approach for identifying crosstalk between signaling pathways

Tegge

Sharp²,

2015

Bioinformatics

Self Cite

“…Graphs are common representations of protein networks, where nodes are proteins and edges represent pairwise interactions between two proteins. While graph representations have been useful for pathway analysis [2][3][4][5] and disease-related applications [5][6][7], the limitations of graphs for representing biochemical reactions are well recognized [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Factor graphs [26] have been used to infer pathway activity from heterogeneous data types. Hypergraphs [27,28] are generalizations of directed graphs that allow multiple inputs and outputs, and their realization as a model for signaling pathways is emerging [9,11,29]. Other models such as Petri nets [30] and logic networks [31,32] move away from structural network analysis and towards discrete dynamic modeling.…”

Section: Introductionmentioning

confidence: 99%

Connectivity Measures for Signaling Pathway Topologies

Franzese

Groce

et al. 2019

Preprint

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Characterizing cellular responses to different extrinsic signals is an active area of research, and curated pathway databases describe these complex signaling reactions. Here, we revisit a fundamental question in signaling pathway analysis: are two molecules "connected" in a network? This question is the first step towards understanding the potential influence of molecules in a pathway, and the answer depends on the choice of modeling framework. We examined the connectivity of Reactome signaling pathways using four different pathway representations. We find that Reactome is very well connected as a graph, moderately well connected as a compound graph or bipartite graph, and poorly connected as a hypergraph (which captures many-to-many relationships in reaction networks). We present a novel relaxation of hypergraph connectivity that iteratively increases connectivity from a node while preserving the hypergraph topology. This measure, B-relaxation distance, provides a parameterized transition between hypergraph connectivity and graph connectivity. B-relaxation distance is sensitive to the presence of small molecules that participate in many functionally unrelated reactions in the network. We also define a score that quantifies one pathway's downstream influence on another, which can be calculated as B-relaxation distance gradually relaxes the connectivity constraint in hypergraphs. Computing this score across all pairs of 34 Reactome pathways reveals two case studies of pathway influence, and we describe the specific reactions that contribute to the large influence score. Our method lays the groundwork for other generalizations of graph-theoretic concepts to hypergraphs in order to facilitate signaling pathway analysis.

“…We recently highlighted the potential and power of hypergraphs to address questions such as pathway reconstruction, enrichment, and crosstalk [4]. Until now, methods to solve these problems have represented pathways simply as sets of proteins or as directed or undirected graphs.…”

Section: Introductionmentioning

confidence: 99%

Pathway Analysis with Signaling Hypergraphs

Ritz

Avent

IEEE/ACM Trans. Comput. Biol. and Bioinf.

2017

Self Cite

Signaling pathways play an important role in the cell’s response to its environment. Signaling pathways are often represented as directed graphs, which are not adequate for modeling reactions such as complex assembly and dissociation, combinatorial regulation, and protein activation/inactivation. More accurate representations such as directed hypergraphs remain underutilized. In this paper, we present an extension of a directed hypergraph that we call a signaling hypergraph. We formulate a problem that asks what proteins and interactions must be involved in order to stimulate a specific response downstream of a signaling pathway. We relate this problem to computing the shortest acyclic B-hyperpath in a signaling hypergraph — an NP-hard problem — and present a mixed integer linear program to solve it. We demonstrate that the shortest hyperpaths computed in signaling hypergraphs are far more informative than shortest paths, Steiner trees, and subnetworks containing many short paths found in corresponding graph representations. Our results illustrate the potential of signaling hypergraphs as an improved representation of signaling pathways and motivate the development of novel hypergraph algorithms.