Evaluating Between-Pathway Models with Expression Data

Abstract

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data-microarray gene expression data from knockout experiments-allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.

Toward the dynamic interactome: it's about time

“…Such a pattern of synthetic-lethal interactions can be explained by a model in which the proteins within each of the two sets function as a pathway (or a protein complex), working together to achieve some goal, and where one set can functionally compensate for the other. Recent work [138] incorporates dynamic information from gene expression data to evaluate the functional coherence and compensation of putative BPMs, focusing attention on the strongest candidates with evident compensatory roles. By integrating temporal data with these topologically derived models, their approach provides additional functional information about the relevant gene sets and the relationships between them.…”

Sslpred: Predicting Synthetic Sickness Lethality

“…Section 4.1 describes the datasets we used for the experiments. Section 4.2 demonstrates SSLPred with another relevant method recently published by Hescott et al 7,14 …”

“…This section describes the comparison between SSLPred and the method proposed by Hescott et al 7,14 Hescott et al employs microarray expression data of single gene knockout experiments to identify BPMs. Though their method does not predict GI score, according to our knowledge, this is the only published method that integrates the concept of single gene mutants and between pathway motifs.…”

“…3,10,15 We extracted BPMs from four studies, which were also used by Hescott et al to validate their method. 14 We employed these BPMs as gold standards in this paper. Next, we describe these datasets in detail.…”

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

“…SL interactions between two genes is a condition where the loss of either gene is viable but the loss of both is lethal, and has been considered a foundation for development of targeted anticancer therapies [7,39]. Such interactions across pathways can result in cross-talk between pathways [29,45] and methods have been developed to identify such between-pathway motifs [6,23]. Many other computational approaches have also been developed to infer ME [11,3,31,26,12,8] and SL interactions [27,34,44,42,30,32].…”