Efficient parameterization of large-scale dynamic models based on relative measurements.

ABSTRACT:

Motivation

Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.

Results

Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation mo

SUBMITTER: Schmiester L 

PROVIDER: S-EPMC9883733 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature