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

Article · September 2012with20 Reads
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.
    • Arabidopsis can operate close to theoretical pathway optimum and that this is mediated by a fine-adjustment of metabolic flux, strongly under transcriptional control. In this light, the present work is one of the very few examples so far, which link in-vivo with in-silico flux data to a higherlevel understanding [103, 114, 115] . It seems straightforward to extend this to other plant systems and to more specific models that address specific plant tissues, which are formed during plant development.
    [Show abstract] [Hide abstract] ABSTRACT: Background During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants. Results The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response. Conclusions The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed. Electronic supplementary material The online version of this article (doi:10.1186/s12918-016-0347-3) contains supplementary material, which is available to authorized users.
    Full-text · Article · Oct 2016
    • ated by taking uncertainty into account during the simulations, however, larger parameter distributions also result in less precise predictions, reducing the power of modeling. 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. Even when in silico parameter values co
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Aug 2014
    • 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.
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Apr 2014
    • 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.
    [Show abstract] [Hide abstract] 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.
    Full-text · Article · Oct 2012
  • [Show abstract] [Hide abstract] ABSTRACT: Interests in the Atacama Desert of northern Chile until very recently were founded on its mineral resources, notably nitrate, copper, lithium and boron. Now this vast desert, the oldest and most arid on Earth, is revealing a microbial diversity that was unimagined even a decade or so ago; indeed the extreme hyper-arid core of the Desert was considered previously to be completely devoid of life. In this Perspective article we highlight pioneering research that, to the contrary, establishes the Atacama as a combination of rich microbial habitats including bacteria that influence biogeochemical transformations in the desert and others that are propitious sources of novel natural products. Many of the Atacama's habitats are especially rich in actinobacteria, not necessarily as dense populations but extensive in taxonomic diversity and capacities to synthesize novel secondary metabolites. Among the latter, compounds have been characterized that express a range of antibiotic, anti-cancer and anti- inflammatory properties to which a variety of bioinformatics and metabolic engineering tools are being applied in order to enhance potencies and productivities. Unquestionably the Atacama Desert is a living desert with regard to which future microbiology and biotechnology research presents exciting opportunities.
    Article · Apr 2013
  • [Show abstract] [Hide abstract] ABSTRACT: Relationships in biological systems are frequently represented as networks with the goal of abstracting a system's components to nodes and connections between them. While such representations allow modeling and analysis using abstract computational methods, there are certain aspects of such modeling that are particularly important for biological networks. We explore features that are deemed necessary for living and evolving organisms and reflect the evolutionary origins of biological networks. Biological networks are robust to random alterations of their nodes and connections yet may be vulnerable to attacks targeting essential genes. Biological systems are dynamic and modular, and so are their network representations. Comparisons of biological networks across species can reveal conserved and evolved regions and shed light on evolutionary events and processes. It is important to understand networks as a whole, as significant insights might emerge from the network approach that cannot be attributed to properties of the nodes alone. Network-based approaches have a potential to significantly increase our understanding of biological systems and consequently, our understanding and treatment of human diseases. © Springer Science+Business Media Dordrecht 2013. All rights are reserved.
    Article · Aug 2013 · Antonie van Leeuwenhoek
Show more