Stepwise inference of likely dynamic flux distributions from metabolic time series data.

ABSTRACT:

Motivation

Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution.

Results

We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites

SUBMITTER: Faraji M 

PROVIDER: S-EPMC5860468 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature