Statistical physics approaches to subnetwork dynamics in biochemical systems

TY - JOUR

T1 - Statistical physics approaches to subnetwork dynamics in biochemical systems

AU - Bravi, Barbara

AU - Sollich, Peter Kurt

PY - 2017/7/19

Y1 - 2017/7/19

N2 - We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally, embedded in a larger network (bulk). The key goal is to write dynamical equations reduced to the subnetwork but still retaining the effects of the bulk. As a result, the subnetwork-reduced dynamics contains a memory term and an extrinsic noise term with non-trivial temporal correlations. We first derive expressions for this memory and noise in the linearized (Gaussian) dynamics and then use a perturbative power expansion to obtain first order nonlinear corrections. For the case of vanishing intrinsic noise, our description is explicitly shown to be equivalent to projection methods up to quadratic terms, but it is applicable also in the presence of stochastic fluctuations in the original dynamics.An example from the Epidermal Growth Factor Receptor (EGFR) signalling pathway is provided to probe the increased prediction accuracy and computational efficiency of our method.

AB - We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally, embedded in a larger network (bulk). The key goal is to write dynamical equations reduced to the subnetwork but still retaining the effects of the bulk. As a result, the subnetwork-reduced dynamics contains a memory term and an extrinsic noise term with non-trivial temporal correlations. We first derive expressions for this memory and noise in the linearized (Gaussian) dynamics and then use a perturbative power expansion to obtain first order nonlinear corrections. For the case of vanishing intrinsic noise, our description is explicitly shown to be equivalent to projection methods up to quadratic terms, but it is applicable also in the presence of stochastic fluctuations in the original dynamics.An example from the Epidermal Growth Factor Receptor (EGFR) signalling pathway is provided to probe the increased prediction accuracy and computational efficiency of our method.

UR - http://www.scopus.com/inward/record.url?scp=85026481727&partnerID=8YFLogxK

U2 - 10.1088/1478-3975/aa7363

DO - 10.1088/1478-3975/aa7363

M3 - Article

SN - 1478-3967

VL - 14

JO - Physical Biology

JF - Physical Biology

IS - 4

M1 - 045010

ER -