Memory effects in biochemical networks as the natural counterpart of extrinsic noise

TY - JOUR

T1 - Memory effects in biochemical networks as the natural counterpart of extrinsic noise

AU - Rubin, Katy J.

AU - Lawler, Katherine

AU - Sollich, Peter

AU - Ng, Tony

PY - 2014/9/21

Y1 - 2014/9/21

N2 - 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.

AB - 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.

KW - Subnetworks

KW - Model reduction

KW - Memory function

KW - Protein interaction networks

KW - GENE-EXPRESSION SIGNATURES

KW - GROWTH-FACTOR RECEPTOR

KW - MATHEMATICAL-MODELS

KW - CELL-LINE

KW - SYSTEMS

KW - REDUCTION

KW - CANCER

KW - SIMPLIFICATION

KW - FLUCTUATIONS

KW - PROTEOME

U2 - 10.1016/j.jtbi.2014.06.002

DO - 10.1016/j.jtbi.2014.06.002

M3 - Article

SN - 0022-5193

VL - 357

SP - 245

EP - 267

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

ER -