期刊
BMC SYSTEMS BIOLOGY
卷 6, 期 -, 页码 -出版社
BIOMED CENTRAL LTD
DOI: 10.1186/1752-0509-6-84
关键词
Biochemical systems theory; Dynamic flux estimation; Metabolic pathways; Parameter estimation; Structure identification; Time series data
资金
- Molecular and Cellular Biosciences Grant from the National Science Foundation [MCB-0946595]
- National Institutes of Health [R01 GM063265]
- Georgia Research Alliance
- BioEnergy Science Center (BESC), which is a U.S. Department of Energy Bioenergy Research Center
- Office of Biological and Environmental Research in the DOE Office of Science
- Direct For Biological Sciences
- Div Of Molecular and Cellular Bioscience [958172] Funding Source: National Science Foundation
Background: Advances in modern high-throughput techniques of molecular biology have enabled top-down 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 model-free 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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