title: Memory effects in biochemical networks as the natural counterpart of extrinsic noise.
abstract: We show that in the generic situation where a biological network, e.g. a protein interaction network, is in fact a subnetwork embedded in a larger "bulk" network, the presence of the bulk causes not just extrinsic noise but also memory effects. This means that the dynamics of the subnetwork will depend not only on its present state, but also its past. We use projection techniques to get explicit expressions for the memory functions that encode such memory effects, for generic protein interaction networks involving binary and unary reactions such as complex formation and phosphorylation. Remarkably, in the limit of low intrinsic copy-number noise such expressions can be obtained even for nonlinear dependences on the past. We illustrate the method with examples from a protein interaction network around epidermal growth factor receptor (EGFR), which is relevant to cancer signalling. These examples demonstrate that inclusion of memory terms is not only important conceptually but also leads to substantially higher quantitative accuracy in the predicted subnetwork dynamics.
authors.affiliation: Department of Mathematics, King׳s College London, Strand, London WC2R 2LS, UK.
authors.forename: Katy J
authors.initials: KJ
authors.lastname: Rubin
authors.affiliation: Institute for Mathematical and Molecular Biomedicine, King׳s College London, Hodgkin Building, London SE1 1UL, UK.
authors.forename: Katherine
authors.initials: K
authors.lastname: Lawler
authors.affiliation: Department of Mathematics, King׳s College London, Strand, London WC2R 2LS, UK. Electronic address: peter.sollich@kcl.ac.uk.
authors.forename: Peter
authors.initials: P
authors.lastname: Sollich
authors.affiliation: Richard Dimbleby Department of Cancer Research, Division of Cancer Studies, King׳s College London, London SE1 1UL, UK; UCL Cancer Institute, Paul O׳Gorman Building, University College London, London WC1E 6DD, UK.
authors.forename: Tony
authors.initials: T
authors.lastname: Ng
mesh_terms: D000818:Animals; D066246:ErbB Receptors / Q000378:metabolism*; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism*; D015398:Signal Transduction
chemical_list: C512478:EGFR protein, human; D066246:ErbB Receptors
keywords: Memory function; Model reduction; Protein interaction networks; Subnetworks