Uncovering signal transduction networks from high-throughput data by integer linear programming.
Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.
Zhao
Xing-Ming
XM
ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Japan.
Wang
Rui-Sheng
RS
Chen
Luonan
L
Aihara
Kazuyuki
K
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005656:Fungal Proteins / Q000378:metabolism; D020869:Gene Expression Profiling; D020935:MAP Kinase Signaling System; D008565:Membrane Proteins / Q000378:metabolism; D008954:Models, Biological; D011382:Programming, Linear; D025941:Protein Interaction Mapping; D015398:Signal Transduction; D014157:Transcription Factors / Q000378:metabolism; D015003:Yeasts / Q000378:metabolism
D005656:Fungal Proteins; D008565:Membrane Proteins; D014157:Transcription Factors