Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints

Berlo, R. J. P. van; Ridder, Dick de; Daran, Jean‐Marc; Daran‐Lapujade, Pascale; Teusink, Bas; Reinders, Marcel J. T.

doi:10.1109/tcbb.2009.55

Cited by 57 publications

(42 citation statements)

References 27 publications

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“…Transcriptionally controlled FBA (tFBA) follows the same principles as MADE, albeit with a different formulation [27]. In this case, measurements across multiple conditions are also used, but they do not need to be from a time course.…”

Section: Introductionmentioning

confidence: 99%

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

2014

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Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

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Section: Introductionmentioning

confidence: 99%

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

2014

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“…These include constraints imposed by economy in enzyme usage 43, 51–53 , metabolite dilution 54 , and changes in transcript level 55, 56 . These FBA refinements further decrease the range of feasible reaction fluxes to obtain solutions closely resembling cellular physiology under certain growth conditions.…”

Section: A Phylogeny Of Constraint-based Methodsmentioning

confidence: 99%

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

2012

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“…In cancer cell, deregulation in these networks results in uncontrolled growth and proliferation1516, these metabolic alterations are the hallmark of cancer17.…”

Section: Discussionmentioning

confidence: 99%

Metabolite Profiling of Preneoplastic and Neoplastic Lesions of Oral Cavity Tissue Samples Revealed a Biomarker Pattern

Musharraf

Shahid

Naqvi

et al. 2016

Sci Rep

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Oral cancer is a major health challenge in the Indian subcontinent and a dreadful form of cancers worldwide. The current study is focused on the identification of distinguished metabolites of oral cancer tissue samples in comparison with precancerous and control tissue samples using gas chromatography coupled with triple quadrupole tandem mass spectrometry and chemometric analyses. Metabolites obtained were identified through National Institute of Standards and Technology (NIST) mass spectral (Wiley registry) library. Mass Profiler Professional (MPP) software was used for the alignment and for all the statistical analysis. 31 compounds out of 735 found distinguishing among oral cancer, precancerous and control group samples using p-value ≤ 0.05. Partial Least Square Discriminant Analysis (PLSDA) model was generated using statistically significant metabolites gave an overall accuracy of 90.2%. Down-regulated amino acid levels appear to be the result of enhanced energy metabolism or up-regulation of the appropriate biosynthetic pathways, and required cell proliferation in cancer tissues. These results suggest that tissue metabolic profiles have great potential in detecting oral cancer and may aid in understanding its underlying mechanisms.

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Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints

Cited by 57 publications

References 27 publications

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods

Metabolite Profiling of Preneoplastic and Neoplastic Lesions of Oral Cavity Tissue Samples Revealed a Biomarker Pattern

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