In vivo analysis of metabolic dynamics inSaccharomyces cerevisiae: II. Mathematical model

Rizzi, Manfred; Baltes, Michael; Theobald, Uwe; Reuß, Matthias

doi:10.1002/(sici)1097-0290(19970820)55:4<592::aid-bit2>3.0.co;2-c

Cited by 277 publications

(60 citation statements)

References 47 publications

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“…A tremendous amount of experimental data has been and continues to be generated for S. cerevisiae (17)(18)(19). A number of databases have been dedicated to store and update yeast experimental data sets, and several mathematical models are available that use kinetic information to capture cell behaviors (20)(21)(22)(23). Limited availability of kinetic information restricts such models to a subset of the whole cell.…”

mentioning

confidence: 99%

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili

Förster

Nielsen

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

336

244

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Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for Ϸ70 -80% of the conditions considered. S ystems biology is commonly viewed as a four-step procedure (1-3): (i) the enumeration of the biological components that make up the biological process of interest, (ii) the reconstruction of the network of interactions among these components, (iii) the in silico simulation of the network function, and (iv) the comparison of computed network properties with actual phenotypic observations. A wealth of available biological data for Saccharomyces cerevisiae (4-7) led to the establishment of the first genome-scale reconstruction of the metabolic network in a eukaryotic cell (8). The initial completion of the first two steps of the systems biology procedure for yeast metabolism has been achieved. Steps iii and iv have not yet been carried out for a eukaryotic organism.Integrated functions of reconstructed metabolic networks can be determined in silico by using a number of analytical approaches (9-11). The relatively young constraint-based approach differs fundamentally from the more traditional kinetic theory-based approaches in that it is not aimed at finding the solution or behavior of the network under certain conditions but rather at eliminating solutions (behaviors) that the network cannot exhibit (Fig. 1). By using this approach, a network of interactions is successively constrained by defining the stoichiometry of the interacting components, the direction of network reactions, and the maximum allowable throughput. In this way, candidate solutions to the network equations are systematically eliminated by the successive application of the governing constraints, i.e., stoichiometry, thermodynamics, and maximal enzymatic rates (12). One thus can define the range of capabilities of the reconstructed network and then, through the use of optimization procedur...

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confidence: 99%

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili

Förster

Nielsen

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

336

244

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“…Unstructured modelling approaches, based on the Monod equation and its descendants with the assumption of balanced growth and product formation 27 , are often reproached with the fact that they inadequately describe the response of a microorganism population to environmental disturbances. According to Sweere et al 28 available structured models can be distinguished in different types from approaches based on a structured biochemical pathway 8,26,29 to more segregated modelling approaches regarding different cell ages 2,11 . These approaches consider balanced growth, or are specified to describe particular biochemical pathways inside structured models and therefore represent the basis for the modelling approach introduced in this work.…”

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confidence: 99%

A Model Based Simulation of Brewing Yeast Propagation

Kurz

Mieleitner

Becker

et al. 2002

Journal of the Institute of Brewing

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A comprehensive kinetic model of yeast propagation in breweries is introduced. It represents the basis for a control strategy aimed at the provision of optimal inoculum at the starting time of subsequent industrial beer fermentations. In the metabolic modelling approach presented, respiratory metabolism on sugars and ethanol and fermentative metabolism respectively, are included. Occurring limitations, due to specific nutritional data of the growth medium, were taken into account for sugar, nitrogen, ethanol and oxygen. Correspondingly, inhibitions of the metabolism by ethanol and high sugar concentrations were formulated. The model especially represents the Crabtree-effect. For model validation, literature data were used and selected experiments within the relevant range of manipulated variables (temperature, dissolved oxygen) were conducted. Model based simulations matched these data with a deviation of 5.6% and with standard deviations of 5.6% after an adaptation of three variable parameters. A relationship between the temperature and the variable parameters could be extracted, which allowed predictive simulations of the yeast propagation process using non-isothermal trajectories. It was shown, that with a precise adjustment of trajectories of both, temperature and dissolved oxygen, the crop time of the inoculum could be varied within a period of 7 to 50 h to maintain high fermentation activity for the subsequent anaerobic fermentation.

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“…Mathematical models are useful tools for optimizing and controlling microbial product formation and microbial metabolism, encompassing the modeling of cultivation techniques, the design of single cell metabolic models, or modelling of whole cell populations [26][27][28][29] . The segregated nature of biological systems and the complexity of cell reactions are cumbersome for mathematical treatment of processes in the bioengineering field.…”

Section: Introductionmentioning

confidence: 99%

“…This approach is far from being an easy endeavour. For example, Rizzi et al 29 applied 22 ma-terial balances for metabolites and 23 enzyme rate equations to predict the intracellular and extracellular metabolite levels in transient phase caused by glucose pulse in a continuous culture of S. cerevisiae. This model was successful only in a timescale of 120 seconds, but enzyme synthesis/degradation of pivotal importance in larger timescales have not been included.…”

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Mathematical Modelling as a Tool for Optimized PHA Production

Novák

Koller

Braunegg

et al. 2015

Chem.Biochem.Eng.Q.

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c ARENA -Association for resource efficient and sustainable technologies, Inffeldgasse 21b, 8010 Graz, AustriaThe potential of poly(hydroxyalkanoates) (PHAs) to replace conventional plastic materials justifies the increasing attention they have drawn both at lab-scale and in industrial biotechnology.The improvement of large-scale productivity and biochemical/genetic properties of producing strains requires mathematical modeling and process/strain optimization procedures. Current models dealing with structurally diversified PHAs, both structured and unstructured, can be divided into formal kinetic, low-structured, dynamic, metabolic (high-structured), cybernetic, neural networks and hybrid models; these attempts are summarized in this review. Characteristic properties of specific groups of models are stressed in light of their benefit to the better understanding of PHA biosynthesis, and their applicability for enhanced productivity. Unfortunately, there is no single type of mathematical model that expresses exactly all the characteristics of producing strains and/or features of industrial-scale plants; in addition, the different requirements for modelling of PHA production by pure cultures or mixed microbial consortia have to be addressed. Therefore, it is crucial to sophisticatedly adapt and fine-tune the modelling approach accordingly to actual processes, as the case arises. For "standard microbial cultivations and everyday practices", formal kinetic models (for simple cases) and "low-structured" models will be appropriate and of great benefit. They are relatively simple and of low computational demand.To overcome the specific weaknesses of different established model types, some authors use hybrid models. Here, satisfying compromises can be achieved by combining mechanistic, cybernetic, and neural and computational fluid dynamics (CFD) models. Therefore, this hybrid modelling approach appears to constitute the most promising solution to generate a holistic picture of the entire PHA production process, encompassing all the benefits of the original modelling strategies. Complex growth media require a higher degree of model structuring. For scientific purposes and advanced development of industrial equipment in the future, real systems will be modelled by highly organized hybrid models.All solutions related to modelling PHA production are discussed in this review.

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In vivo analysis of metabolic dynamics inSaccharomyces cerevisiae: II. Mathematical model

Cited by 277 publications

References 47 publications

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

A Model Based Simulation of Brewing Yeast Propagation

Mathematical Modelling as a Tool for Optimized PHA Production

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