Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks.

ABSTRACT: Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used i

SUBMITTER: Frohlich F 

PROVIDER: S-EPMC5256869 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature