Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic

Lee, Bogju; Yen, John; Yang, Long-Hao; Liao, James C.

doi:10.1002/(sici)1097-0290(19990320)62:6<722::aid-bit11>3.0.co;2-u

Cited by 35 publications

(19 citation statements)

References 24 publications

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“…Another approach is the use of kinetic theory to solve systems of ordinary di!erential equations (Reich & Sel'kov, 1981;Shuler & Domach, 1983;Fell, 1996;Heinrich & Schuster, 1996;Stephanopoulos et al, 1998), as has been done to study E. coli growth on glucose and lactose (Wong et al, 1997). Kinetic theory may also be combined with Boolean logic as in a hybrid model of the lambda phage (McAdams & Shapiro, 1995), using fuzzy logic as has been done with E. coli core metabolism (Lee et al, 1999) or in conjunction with cybernetic principles (Guardia et al, 2000;Varner, 2000). Other approaches to the analysis of genetic regulatory circuits include the use of fractal kinetic theory (Savageau, 1998) and stochastic modeling techniques (McAdams & Arkin, 1997, 1998Carrier & Keasling, 1999).…”

Section: Introductionmentioning

confidence: 99%

Regulation of Gene Expression in Flux Balance Models of Metabolism

Covert

Schilling

Palsson

2001

Journal of Theoretical Biology

389

350

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Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with #ux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, e!ects of mutation and knock-outs, and gene expression pro"les. However, FBA leads to incorrect predictions in situations where regulatory e!ects are a dominant in#uence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simpli"ed metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic e!ects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the e!ects of transcriptional regulation on cellular metabolism at the systemic level.

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Section: Introductionmentioning

confidence: 99%

Regulation of Gene Expression in Flux Balance Models of Metabolism

Covert

Schilling

Palsson

2001

Journal of Theoretical Biology

389

350

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“…Kinetic information can be introduced by the description of enzymatic rates with mathematical equations whose parameters are estimated with experimental data (26,38). Another way of accounting for the regulatory aspects of cell metabolism consists in applying cybernetic principles (37) by using neural networks (34) or fuzzy logic-based models (13) when mechanistic details are missing.…”

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confidence: 99%

Minimization of Glycerol Production during the High-Performance Fed-Batch Ethanolic Fermentation Process in Saccharomyces cerevisiae , Using a Metabolic Model as a Prediction Tool

Bideaux

Alfenore

Cameleyre

et al. 2006

Appl Environ Microbiol

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On the basis of knowledge of the biological role of glycerol in the redox balance of Saccharomyces cerevisiae, a fermentation strategy was defined to reduce the surplus formation of NADH, responsible for glycerol synthesis. A metabolic model was used to predict the operating conditions that would reduce glycerol production during ethanol fermentation. Experimental validation of the simulation results was done by monitoring the inlet substrate feeding during fed-batch S. cerevisiae cultivation in order to maintain the respiratory quotient (RQ) (defined as the CO 2 production to O 2 consumption ratio) value between 4 and 5. Compared to previous fermentations without glucose monitoring, the final glycerol concentration was successfully decreased. Although RQ-controlled fermentation led to a lower maximum specific ethanol production rate, it was possible to reach a high level of ethanol production: 85 g · liter ؊1 with 1.7 g · liter ؊1 glycerol in 30 h. We showed here that by using a metabolic model as a tool in prediction, it was possible to reduce glycerol production in a very high-performance ethanolic fermentation process.It is well-known that glycerol is a major by-product during alcoholic fermentation in the yeast Saccharomyces cerevisiae. The role of glycerol in the cellular redox balance (5) and in osmoregulation (4, 16) have been reported in the literature. Participating in the cell redox balance, glycerol is produced by Saccharomyces cerevisiae to reoxidize surplus NADH, formed in the synthesis of biomass and secondary fermentation products, to NAD ϩ (5). Two different strategies for modulating the production of glycerol have been reported in the literature. One strategy is to change the operating conditions of the fermentation process (i.e., aeration levels, stress conditions [salts, acids, osmotic stress, heat shock . . .]) (3,4,8,11,12,19,31,40,42); another is to use genetically modified strains. Engineered strains were constructed by overexpressing and repressing components of the glycerol synthesis pathway (14,18,19,21,25).Complementing biochemical and metabolic engineering experimental approaches, mathematical models may also be useful for interpretation and prediction of biochemical processes. Process models consist in coupling mass balance equations at the reactor scale and at the cellular scale (29). Cells can be modeled at different levels: unstructured models on the population level (15, 27) or structured models on the cellular level. The latter can be a compartmental model (41) or a metabolic regulator model (28). Constraints on carbon, energy, and redox potential linked to the metabolic network can be taken into account by a metabolic model that contains a complete set of metabolic pathways involved in biomass synthesis, energy production, substrate degradation, and cometabolite production (30,35).Metabolic flux analysis is generally used to quantify intracellular fluxes in the central metabolism of microorganisms (7,9,11,22,24,43), but it has had a limited impact due to the lack of regulatory...

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“…PKI is usually a tetramer comprising four identical subunits which are subject to complex allosteric regulation, namely: potent activation by PEP and fructose-1,6-biphosphate (FBP) and inhibition by ATP and acetyl-CoA (ACCOA) [63]. There is plenty of experimental data on the activity of this enzyme especially for organisms like E. coli [64], however derivation of a mechanistic model to account for its complex behavior has remained challenging [65]. We can use GRASP to build and sample thermodynamically plausible parameterizations consistent with the reported data.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis

Saa

Nielsen

2016

Biochimica et Biophysica Acta (BBA) - General Subjects

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BackgroundAnalysis of limiting steps within enzyme-catalyzed reactions is fundamental to understand their behavior and regulation. Methods capable of unravelling control properties and exploring kinetic capabilities of enzymatic reactions would be particularly useful for protein and metabolic engineering. While single-enzyme control analysis formalism has previously been applied to well-studied enzymatic mechanisms, broader application of formalism is limited in practice by the limited amount of kinetic data and the difficulty of describing complex allosteric mechanisms. MethodsTo overcome these limitations, we present here a probabilistic framework enabling control analysis of previously unexplored mechanisms under uncertainty. By combining a thermodynamically consistent parameterization with an efficient Sequential Monte Carlosampler embedded in a Bayesian setting, this framework yields insights into the capabilities of enzyme-catalyzed reactions with modest kinetic information, provided that the catalytic mechanism and a thermodynamic reference point are defined. ResultsThe framework was used to unravel the impact of thermodynamic affinity, substrate saturation levels and effector concentrations on the flux control and response coefficients of a diverse set of enzymatic reactions. ConclusionsOur results highlight the importance of the metabolic context in the control analysis of isolated enzymes as well as the use of statistically sound methods for their interpretation. General SignificanceThis framework significantly expands our current capabilities for unravelling the control properties of general reaction kinetics with limited amount of information. This framework will be useful for both theoreticians and experimentalists in the field.

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Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic

Cited by 35 publications

References 24 publications

Regulation of Gene Expression in Flux Balance Models of Metabolism

Regulation of Gene Expression in Flux Balance Models of Metabolism

Minimization of Glycerol Production during the High-Performance Fed-Batch Ethanolic Fermentation Process in Saccharomyces cerevisiae , Using a Metabolic Model as a Prediction Tool

A probabilistic framework for the exploration of enzymatic capabilities based on feasible kinetics and control analysis

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