Paper Info
Title
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 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.
Year
DOI
Venue
2017
10.1093/bioinformatics/btx126
BIOINFORMATICS
Field
DocType
Volume
Flux distribution,Statistical physics,Data mining,Time series,MATLAB,Matrix (mathematics),Computer science,Source code,Inference,Software,Flux,Statistics
Journal
33
Issue
ISSN
Citations 
14
1367-4803
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Mojdeh Faraji100.34
Eberhard O. Voit201.35