Streamlining the construction of large-scale dynamic models using generic kinetic equations

Adiamah, Delali A.; Handl, Julia; Schwartz, Jean-Marc

doi:10.1093/bioinformatics/btq136

Cited by 17 publications

(22 citation statements)

References 34 publications

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“…Furthermore, the increasing adjusted R-square values indicate that the "explained" variance substantially rises with the growing number of variables in the regression model, although in a somewhat non-linear proportion, due to a more pronounced contribution of the few "strongest" [22] of models e model of Hynne et al [18] f model of Teusink et al [5] g generic model [19] h stoichiometric model [20] AA frequencies ( Figure 3). Therefore, 5 to 7 variables are sufficient enough to form statistically robust multiple linear regression models linking the values of metabolic fluxes predicted by different (Appendix 1) kinetic or stoichiometric models with the AA composition (AAC) of corresponding sequences ( Table 1).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, the same variables were selected for the Teusink′s model [5] and the generic GRaPre model [19] with slight differences in selected variables (Q instead of W) and regression coefficients ( Table 1, models II, and III, respectively) though the latter additionally includes the reaction of triosephosphate isomerase and changing trehalose and glycogen fluxes.…”

Section: Discussionmentioning

confidence: 99%

“…Metabolic fluxes within the yeast Saccharomyces cerevisiae glycolysis pathway were determined using the kinetic models of Hynne [18] and Teusink [5] from the BioModels Database (http://www.ebi.ac.uk/biomodels/) -BIOMD0000000061 and BIOMD0000000064, respectively. Simulation experiments were performed for both models using the COmplex PAthway Simulator tool (Copasi 4.7 Build 34, http://www.copasi.org) at perturbed initial concentrations of external glucose (25 mM, 50 mM, and 100 mM (or 10 mM, 50 mM, and 100 mM for the generic model [19])). The dataset was also supplemented by the data series representing the metabolic fluxes of a generic kinetic model developed by GRaPre tool [19] and a stoichiometric model [20] of the yeast Saccharomyces cerevisiae glycolysis pathway.…”

Section: Methodsmentioning

confidence: 99%

“…Simulation experiments were performed for both models using the COmplex PAthway Simulator tool (Copasi 4.7 Build 34, http://www.copasi.org) at perturbed initial concentrations of external glucose (25 mM, 50 mM, and 100 mM (or 10 mM, 50 mM, and 100 mM for the generic model [19])). The dataset was also supplemented by the data series representing the metabolic fluxes of a generic kinetic model developed by GRaPre tool [19] and a stoichiometric model [20] of the yeast Saccharomyces cerevisiae glycolysis pathway. The UniProtKB accession numbers of enzyme sequences together with the enzyme representation in the kinetic/stoichiometric models used in the study [5,[18][19][20] are summarized in Appendix 1.…”

Section: Methodsmentioning

confidence: 99%

“…The dataset was also supplemented by the data series representing the metabolic fluxes of a generic kinetic model developed by GRaPre tool [19] and a stoichiometric model [20] of the yeast Saccharomyces cerevisiae glycolysis pathway. The UniProtKB accession numbers of enzyme sequences together with the enzyme representation in the kinetic/stoichiometric models used in the study [5,[18][19][20] are summarized in Appendix 1. The AA composition (frequencies of AA occurrence) of sequences (AAC) and codon usage indices were computed using ExPASy/ProtParam (http://web.expasy.…”

Section: Methodsmentioning

confidence: 99%

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Relationships between metabolic fluxes and enzyme amino acid composition

Kampenusa

Zikmanis

2013

Open Life Sciences

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Relationships between metabolic fluxes and enzyme amino acid composition Abbreviations AA -amino acid; AAC -amino acid composition; LOOCV -leave-one-out cross-validation; VIF -variance inflation factor. IntroductionCell metabolism is comprised of enzyme-catalyzed biochemical reactions and carrier-mediated transport processes. Taken as a whole, these reactions form interrelated metabolic pathways which are combined into a cellular metabolic network. Metabolic fluxes describe the amount of material chemically converted or transported per time unit and are considered as the key parameters of any metabolic pathway and hence, as the fundamental determinants of cell physiology [1][2][3][4]. Changes in metabolic fluxes in response to various types of genetic and environmental perturbations are critical for the metabolic flux control which is a key objective of metabolic engineering [5,6]. On the other hand, enzyme activity is one of the major factors influencing the magnitude of metabolic fluxes in any cell [6]. According to concepts of systems biology, metabolic fluxes are net sums of underlying enzymatic reaction rates represented by integral outputs of three biological quantities which interact at the level of enzyme kinetics: kinetic parameters, enzyme and reactant concentrations [7]. An integrated view on enzymes suggests them as dynamic assemblies whose variable structures are closely related to catalytic functions [8,9]. It is therefore important to extend our knowledge of enzyme sequence, structure, and function relationships [10], as well as to explore coherencies between enzyme activity profiles and metabolic flux distributions in order to understand the physiological dynamics within a cell [2,11]. Amino acid (AA) composition (AAC) is a simplest attribute of proteins among so-called global sequence Keywords: Saccharomyces cerevisiae • Metabolic fluxes • Glycolytic enzymes • Amino acid composition • Multivariate relationshipsAbstract: Metabolic fluxes are a key parameter of metabolic pathways being closely related to the kinetic properties of enzymes and could be conditional on their sequence characteristics. This study examines possible relationships between the metabolic fluxes and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolysis pathway. Metabolic fluxes were quantified by the COPASI tool using the kinetic models of Hynne and Teusink at 25 mM, 50 mM, and 100 mM of external glucose or employing literature data for cognate kinetic or stoichiometric models. The enzyme sequences were taken from the UniProtKB, and the AAC computed by the ExPASy/ProtParam tool. Multiple linear regressions (89.07% < R2 adjusted < 91.82%; P<0.00001) were found between the values of metabolic fluxes and the selected sets of AA frequencies (5 to 7 for each model). Selected AA differed from the rest by their physicochemical and structural propensities, thus suggesting a distinctive contribution to the properties of enzymes, and hence the metabolic fluxes. The results provide evidenc...

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