Biochemical Systems Analysis of Genome-wide Expression Data
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Motivation: Modern methods of genomics have produced an unprecedented amount of raw data. The interpretation and explanation of these data constitute a major, well-recognized challenge.
Results: Biochemical Systems Theory (BST) is the mathematical basis of a well-established methodological framework for analyzing networks of biochemical reactions. An existing BST model of yeast glycolysis is used here to explain and interpret the glycolytic gene expression pattern of heat shocked yeast. Our analysis demonstrates that the observed gene expression profile satisfies the primary goals of increased ATP, trehalose, and NADPH production, while maintaining intermediate metabolites at reasonable levels. Based on a systematic exploration of alternative, hypothetical expression profiles, we show that the observed profile outperforms other profiles.
Conclusion: BST is a useful framework for combining DNA microarray data with enzymatic process information to yield new insights into metabolic pathway regulation.
Availability: All analyses were executed with the software PLAS(Copyright), which is freely available at http://correio.cc.fc.ul.pt/~aenf/plas.html for academic use.
Contact: VoitEO@MUSC.edu
The best models of metabolism.
Voit E Wiley Interdiscip Rev Syst Biol Med. 2017; 9(6).
PMID: 28544810 PMC: 5643013. DOI: 10.1002/wsbm.1391.
Nonparametric dynamic modeling.
Faraji M, Voit E Math Biosci. 2016; 287:130-146.
PMID: 27590775 PMC: 5706552. DOI: 10.1016/j.mbs.2016.08.004.
Stochastic S-system modeling of gene regulatory network.
Chowdhury A, Chetty M, Evans R Cogn Neurodyn. 2015; 9(5):535-47.
PMID: 26379803 PMC: 4567998. DOI: 10.1007/s11571-015-9346-0.
Canonical modeling of the multi-scale regulation of the heat stress response in yeast.
Fonseca L, Chen P, Voit E Metabolites. 2014; 2(1):221-41.
PMID: 24957376 PMC: 3901190. DOI: 10.3390/metabo2010221.
Flux imbalance analysis and the sensitivity of cellular growth to changes in metabolite pools.
Reznik E, Mehta P, Segre D PLoS Comput Biol. 2013; 9(8):e1003195.
PMID: 24009492 PMC: 3757068. DOI: 10.1371/journal.pcbi.1003195.