title: Reverse engineering a signaling network using alternative inputs.
abstract: One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an "AIs-Deletions matrix" that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams.
authors.affiliation: Department of Developmental and Cell Biology, University of California Irvine, Irvine, California, USA.
authors.forename: Hiromasa
authors.initials: H
authors.lastname: Tanaka
authors.forename: Tau-Mu
authors.initials: TM
authors.lastname: Yi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods; D015966:Gene Expression Regulation, Fungal; D049770:Genes, Mating Type, Fungal; D008954:Models, Biological; D008957:Models, Genetic; D025941:Protein Interaction Mapping / Q000379:methods; D020543:Proteome / Q000378:metabolism; D015398:Signal Transduction / Q000502:physiology; D049490:Systems Biology / Q000379:methods*
chemical_list: D020543:Proteome