title: Paradoxical results in perturbation-based signaling network reconstruction.
abstract: Mathematical models are extensively employed to understand physicochemical processes in biological systems. In the absence of detailed mechanistic knowledge, models are often based on network inference methods, which in turn rely upon perturbations to nodes by biochemical means. We have discovered a potential pitfall of the approach underpinning such methods when applied to signaling networks. We first show experimentally, and then explain mathematically, how even in the simplest signaling systems, perturbation methods may lead to paradoxical conclusions: for any given pair of two components X and Y, and depending upon the specific intervention on Y, either an activation or a repression of X could be inferred. This effect is of a different nature from incomplete network identification due to underdetermined data and is a phenomenon intrinsic to perturbations. Our experiments are performed in an in vitro minimal system, thus isolating the effect and showing that it cannot be explained by feedbacks due to unknown intermediates. Moreover, our in vitro system utilizes proteins from a pathway in mammalian (and other eukaryotic) cells that play a central role in proliferation, gene expression, differentiation, mitosis, cell survival, and apoptosis. This pathway is the perturbation target of contemporary therapies for various types of cancers. The results presented here show that the simplistic view of intracellular signaling networks being made up of activation and repression links is seriously misleading, and call for a fundamental rethinking of signaling network analysis and inference methods.
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston Massachusetts.
authors.forename: Sudhakaran
authors.initials: S
authors.lastname: Prabakaran
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston Massachusetts.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Gunawardena
authors.affiliation: Department of Mathematics & BioMaPs Institute for Quantitative Biology, Rutgers University, Piscataway, New Jersey. Electronic address: sontag@math.rutgers.edu.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Sontag
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D006801:Humans; D020935:MAP Kinase Signaling System; D013058:Mass Spectrometry; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D008954:Models, Biological; D008969:Molecular Sequence Data; D010748:Phosphopeptides / Q000737:chemistry / Q000378:metabolism; D015398:Signal Transduction; D049490:Systems Biology; D014981:Xenopus
chemical_list: D010748:Phosphopeptides; D020928:Mitogen-Activated Protein Kinases