Topological augmentation to infer hidden processes in biological systems.

ABSTRACT:

Motivation

A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables-usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data.

Results

Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semi

SUBMITTER: Sunnaker M 

PROVIDER: S-EPMC3892687 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature