{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Faraji M"],"funding":["NIAID NIH HHS","NIEHS NIH HHS","National Institutes of Health","NIH HHS","National Science Foundation"],"pagination":["2165-2172"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5860468"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["33(14)"],"pubmed_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 "],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["Stepwise inference of likely dynamic flux distributions from metabolic time series data."],"pmcid":["PMC5860468"],"funding_grant_id":["MCB-0958172, MCB-0946595 and MCB-1517588","DEB-1241046","HHSN272201200031C","P51 OD011132","MCB 1411672","P30 ES019776"],"pubmed_authors":["Voit EO","Faraji M"],"additional_accession":[]},"is_claimable":false,"name":"Stepwise inference of likely dynamic flux distributions from metabolic time series data.","description":"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 ","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Jul","modification":"2025-04-05T15:11:20.619Z","creation":"2019-03-26T23:46:22Z"},"accession":"S-EPMC5860468","cross_references":{"pubmed":["28334199"],"doi":["10.1093/bioinformatics/btx126"]}}