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- Title
Stepwise inference of likely dynamic flux distributions from metabolic time series data.
- Authors
Faraji, Mojdeh; Voit, Eberhard O.
- 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 underdeterminedness 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 and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method.
- Subjects
METABOLISM; METABOLITES; TIME series analysis; MATHEMATICAL models; DEGREES of freedom
- Publication
Bioinformatics, 2017, Vol 33, Issue 14, p2165
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btx126