Continuous modeling of metabolic networks with gene regulation in yeast and in vivo determination of rate parameters

Centre for Biochemical Engineering and Biotechnology, Institute for Cell Dynamics and Biotechnology: A Centre for Systems Biology, University of Chile, Santiago, Chile.
Biotechnology and Bioengineering (Impact Factor: 4.13). 09/2012; 109(9):2325-39. DOI: 10.1002/bit.24503
Source: PubMed


A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass-action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions.

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    • "Taking uncertainty into account, however, does not address the adaptation and regulation that can occur upon genetic manipulation, which is central in metabolic engineering. Moisset et al. (2012) generated a promising model of glucose repression in yeast, where the expression of genes was dependent on the level of glucose, and this model was able to reproduce metabolic fluxes during exponential growth on glucose or ethanol. Such an approach could be used to model other types of regulation. "
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    ABSTRACT: Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology, often, the aim is to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression, however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally non-existent, but well characterized components into a living organism. To describe these systems kinetic models are often used, and to integrate these systems with the standard metabolic engineering approach it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.This article is protected by copyright. All rights reserved.
    FEMS Yeast Research 08/2014; DOI:10.1111/1567-1364.12199 · 2.82 Impact Factor
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    • "Although models with simpler structures have proven useful in many cases, models of production processes and cell cultivation with increasing mechanistic details of intracellular reactions are now starting to appear (Bettenbrock et al., 2006; Shinto et al., 2007; Oshiro et al., 2009; Kadir et al., 2010; Li et al., 2011; Nolan and Lee, 2011). In fact, kinetic models of substantial complexity, which have been successful in describing how the metabolic state of cells varies with the external conditions, have recently been presented for both E. coli (Kotte et al., 2010; Usuda et al., 2010) and S. cerevisiae (Moisset et al., 2012). In addition to detailed representations of primary metabolic reaction networks, these models include genetic regulation of enzyme concentrations. "
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    ABSTRACT: An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed.
    Metabolic Engineering 04/2014; 24. DOI:10.1016/j.ymben.2014.03.007 · 6.77 Impact Factor
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    • "If, during the cultivation process, drastic changes occur in the extracellular conditions, which are known to trigger extensive remodelling of the hierarchical regulation of the chassis’ metabolism, this regulation must be included in the model. For example, changes in the concentration of glucose and/or uptake trigger reorganization of the metabolism of S. cerevisiae, which was recently modelled by Moisset et al. (2012) [70]. They introduced glucose-dependent hierarchical regulation into a dynamic model of S. cerevisiae. "
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    ABSTRACT: Cell factories are commonly microbial organisms utilized for bioconversion of renewable resources to bulk or high value chemicals. Introduction of novel production pathways in chassis strains is the core of the development of cell factories by synthetic biology. Synthetic biology aims to create novel biological functions and systems not found in nature by combining biology with engineering. The workflow of the development of novel cell factories with synthetic biology is ideally linear which will be attainable with the quantitative engineering approach, high-quality predictive models, and libraries of well-characterized parts. Different types of metabolic models, mathematical representations of metabolism and its components, enzymes and metabolites, are useful in particular phases of the synthetic biology workflow. In this minireview, the role of metabolic modelling in synthetic biology will be discussed with a review of current status of compatible methods and models for the in silico design and quantitative evaluation of a cell factory.
    Computational and Structural Biotechnology Journal 10/2012; 3(4):e201210009. DOI:10.5936/csbj.201210009
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