title: Studying Cellular Signal Transduction with OMIC Technologies.
abstract: In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.
authors.affiliation: Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
authors.forename: Benjamin D
authors.initials: BD
authors.lastname: Landry
authors.affiliation: Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC, V5A 1S6 Canada.
authors.forename: David C
authors.initials: DC
authors.lastname: Clarke
authors.affiliation: Program in Systems Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA; Program in Molecular Medicine, Department of Molecular, Cell, and Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA. Electronic address: michael.lee@umassmed.edu.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Lee
mesh_terms: D000818:Animals; D023281:Genomics / Q000379:methods*; D057166:High-Throughput Screening Assays / Q000379:methods; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods; D011506:Proteins / Q000032:analysis / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
chemical_list: D011506:Proteins
keywords: OMIC technologies; data-driven modeling; signal transduction; systems biology