Estimation of dynamic flux profiles from metabolic time series data
 IChun Chou,
 Eberhard O Voit
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Abstract
Background
Advances in modern highthroughput techniques of molecular biology have enabled topdown approaches for the estimation of parameter values in metabolic systems, based on time series data. Special among them is the recent method of dynamic flux estimation (DFE), which uses such data not only for parameter estimation but also for the identification of functional forms of the processes governing a metabolic system. DFE furthermore provides diagnostic tools for the evaluation of model validity and of the quality of a model fit beyond residual errors. Unfortunately, DFE works only when the data are more or less complete and the system contains as many independent fluxes as metabolites. These drawbacks may be ameliorated with other types of estimation and information. However, such supplementations incur their own limitations. In particular, assumptions must be made regarding the functional forms of some processes and detailed kinetic information must be available, in addition to the time series data.
Results
The authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is modelfree and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae.
Conclusions
The results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.
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 Title
 Estimation of dynamic flux profiles from metabolic time series data
 Open Access
 Available under Open Access This content is freely available online to anyone, anywhere at any time.
 Journal

BMC Systems Biology
6:84
 Online Date
 July 2012
 DOI
 10.1186/17520509684
 Online ISSN
 17520509
 Publisher
 BioMed Central
 Additional Links
 Topics
 Keywords

 Biochemical systems theory
 Dynamic flux estimation
 Metabolic pathways
 Parameter estimation
 Structure identification
 Time series data
 Authors

 IChun Chou ^{(1)}
 Eberhard O Voit ^{(1)}
 Author Affiliations

 1. Integrative BioSystems Institute and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, 313 Ferst Drive, Atlanta, GA, 30332, USA