Design centering enables robustness screening of pattern formation models.

ABSTRACT:

Motivation

Access to unprecedented amounts of quantitative biological data allows us to build and test biochemically accurate reaction-diffusion models of intracellular processes. However, any increase in model complexity increases the number of unknown parameters and, thus, the computational cost of model analysis. To efficiently characterize the behavior and robustness of models with many unknown parameters remains, therefore, a key challenge in systems biology.

Results

We propose a novel computational framework for efficient high-dimensional parameter space characterization of reaction-diffusion models in systems biology. The method leverages the Lp-Adaptation algorithm, an adaptive-proposal statistical method for approximate design centering and robustness estimation. Ou

SUBMITTER: Solomatina A 

PROVIDER: S-EPMC9486588 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature