On‐line stoichiometry and identification of metabolic state under dynamic process conditions

Herwig, Christoph; Marison, I. W.; Stockar, Urs von

doi:10.1002/bit.10058

Cited by 68 publications

(73 citation statements)

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“…The cumulative carbon balance, in Cmol per Cmol of biomass (Cmol Cmol x Ϫ1 ), compares the cumulative uptake of carbon sources with the accumulation of known carbon sinks, calculated at each sampling time point relative to time zero, when the glucose pulse was given (14). During the postpulse period, glucose (added via both the pulse and the feed) was the sole carbon source, assuming that the small amount of ethanol in the feed was not metabolized.…”

Section: Vol 72 2006 Metabolome Dynamics and Balances In S Cerevismentioning

confidence: 99%

“…Under quasi-steady-state conditions, for example, material balances of extracellular metabolites are required for metabolic flux analysis (33). Previous work has evaluated dynamic carbon balances in a time frame of tens of minutes to hours (14,26,45), based on measured extracellular fluxes and an assumed intracellular quasi-steady state. Under dynamic conditions and within the time frame of interest for in vivo kinetic analysis, setting up material balances requires comprehensive quantification of extra-and intracellular metabolite concentrations as well as the in vivo O 2 uptake rate and CO 2 evolution rate.…”

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Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Dam

Schipper

et al. 2006

Appl Environ Microbiol

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The in vivo kinetics in Saccharomyces cerevisiae CEN.PK 113-7D was evaluated during a 300-second transient period after applying a glucose pulse to an aerobic, carbon-limited chemostat culture. We quantified the responses of extracellular metabolites, intracellular intermediates in primary metabolism, intracellular free amino acids, and in vivo rates of O 2 uptake and CO 2 evolution. With these measurements, dynamic carbon, electron, and ATP balances were set up to identify major carbon, electron, and energy sinks during the postpulse period. There were three distinct metabolic phases during this time. In phase I (0 to 50 seconds after the pulse), the carbon/electron balances closed up to 85%. The accumulation of glycolytic and storage compounds accounted for 60% of the consumed glucose, caused an energy depletion, and may have led to a temporary decrease in the anabolic flux. In phase II (50 to 150 seconds), the fermentative metabolism gradually became the most important carbon/electron sink. In phase III (150 to 300 seconds), 29% of the carbon uptake was not identified in the measurements, and the ATP balance had a large surplus. These results indicate an increase in the anabolic flux, which is consistent with macroscopic balances of extracellular fluxes and the observed increase in CO 2 evolution associated with nonfermentative metabolism. The identified metabolic processes involving major carbon, electron, and energy sinks must be taken into account in in vivo kinetic models based on short-term dynamic metabolome responses.Mathematical models of in vivo enzyme kinetics in microorganisms are important for understanding metabolic control mechanisms operating on the level of the metabolome and can be used to assist the rational redesign of metabolic pathways to enhance desired functionalities of microbes (54). Kinetic parameters in this kind of model can be obtained by stimulusresponse experiments, in which cells grown in a (quasi-)steady state are perturbed by an external stimulus and the dynamic responses of intra-and extracellular metabolites are monitored. The time window of observation is usually within tens to a few hundred seconds after the application of the stimulus, and the responses are usually attributed to rapid (allosteric) enzyme-metabolite interactions (38). Kinetic parameters can be estimated from the measured responses, based on a set of (dynamic) material balances (7, 30, 49), as follows: dx/dt ϭ Sv (equation 1), where x is a vector of the metabolite concentrations, S is the stoichiometry matrix, and v is a vector of the reaction rates as a function of (yet) unknown kinetic parameters. The stimulus-response methodology is an ideal tool for obtaining kinetic information and has been applied to various microorganisms under different growth conditions (7,26,32,38,52).For Saccharomyces cerevisiae, a frequently applied perturbation is the addition of a concentrated glucose solution, i.e., a glucose pulse, to a glucose-limited chemostat culture, thereby inducing a short-term Crabtree effect. Theoba...

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Section: Vol 72 2006 Metabolome Dynamics and Balances In S Cerevismentioning

confidence: 99%

mentioning

confidence: 99%

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Dam

Schipper

et al. 2006

Appl Environ Microbiol

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“…The A-stat approach has also been used for m crit screening in a study of an enological yeast strain, Saccharomyces uvarum, used in wine production, which led to a purely oxidative fed-batch process (Albergaria et al, 2000) and in a study of S. cerevisiae for the development of a mathematical model enabling the quantitative description of transient changes in metabolism (Herwig et al, 2001). Two studies have investigated the effects of cultivation medium composition on m crit of S. cerevisiae: whilst one study concluded that supplementing glucose-based mineral medium with ethanol had no effect on m crit (Paalme et al, 1997a), the other study showed an 8 % increase of m crit on medium supplemented with oleic acid (Marc et al, 2013).…”

Section: Main Results Referencementioning

confidence: 99%

Advanced continuous cultivation methods for systems microbiology

2015

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Increasing the throughput of systems biology-based experimental characterization of in silicodesigned strains has great potential for accelerating the development of cell factories. For this, analysis of metabolism in the steady state is essential as only this enables the unequivocal definition of the physiological state of cells, which is needed for the complete description and in silico reconstruction of their phenotypes. In this review, we show that for a systems microbiology approach, high-resolution characterization of metabolism in the steady stategrowth space analysis (GSA) -can be achieved by using advanced continuous cultivation methods termed changestats. In changestats, an environmental parameter is continuously changed at a constant rate within one experiment whilst maintaining cells in the physiological steady state similar to chemostats. This increases the resolution and throughput of GSA compared with chemostats, and, moreover, enables following of the dynamics of metabolism and detection of metabolic switch-points and optimal growth conditions. We also describe the concept, challenge and necessary criteria of the systematic analysis of steady-state metabolism. Finally, we propose that such systematic characterization of the steady-state growth space of cells using changestats has value not only for fundamental studies of metabolism, but also for systems biology-based metabolic engineering of cell factories.

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“…One approach is to approximate the derivative de/dt with a simple method, such as Euler or Runge-Kutta methods, and then solve (2) (Herwig, 2001). Euler methods provide the most straightforward approximations:…”

Section: Euler Approximation and Moving Average Filtersmentioning

confidence: 99%

“…Thus, the new signal becomes smoother. This kind of filters has already been applied to the calculation of metabolic fluxes (Herwig, 2001). …”

Section: Euler Approximation and Moving Average Filtersmentioning

confidence: 99%

Interval and Possibilistic Methods for Constraint-Based Metabolic Models

Estrada¹

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In the following site the reader will find updated information regarding this thesis. This includes corrections and clarifications, connections with future works, publications, software and tools, etc.http://science.ensilicio.com/Thesis/Thesis.html AgradecimientosLa tesis doctoral es un viaje emocionante, exigente y no exento de riesgos. Por suerte, resulta mucho mejor, más sencillo y más divertido, cuando uno tiene una lista de agradecimientos tan extensa como la mía.Estoy en deuda con mi director, Jesús Picó, por darme la oportunidad de trabajar en lo que me gusta, por descubrirme un tema fascinante y por dirigirme sabiamente en el desarrollo de esta tesis. También con mis supervisores durante mis estancias en el extranjero, Georges Bastin y Vassily Hatzimanikatis, porque me permitieron aprender de su trabajo, conocer sus instituciones y explorar dos países.Le agradezco a Antonio Sala que se acercase un buen día con una propuesta para colaborar (haciendo así fructíferas las reuniones en la biblioteca); y a Marta Tortajada que fuese mi coautora en varias ocasiones y que sea una de las personas cuyas ideas más me han ayudado. He aprendido mil cosas de otras personas -profesores, compañeros, colegas, revisores, etc.-y a todos les estoy agradecido.A los compañeros que encontré camino al doctorado les agradezco, sobretodo, que hicieran mi trabajo divertido. Agradezco cada café y cada conversación. Mención especial merecen mis principales compañeros de viaje, Pepe y Sergio, por su camaradería en el trabajo y en la vida.Es probable que sin el apoyo de otras personas no hubiese terminado esta tesis... y es seguro que de haberlo hecho sería irrelevante. A mi familia -Juan, María, Juan-les agradezco que sean mi refugio, porque la existencia de ese «lugar» me permite el lujo de ser temerario. A mis amigos en la vecindad les agradezco su interés por las victorias de cada día; y tanto a ellos como al resto, les doy las gracias por creer incondicionalmente que lo que hacía, fuese lo que fuese, era digno de admirar. Mi deuda es especial con Lucia porque ella fue la recompensa en los días de trabajo y sacrificio.Por último, y por encima de todo, quiero agradecer a mis padres todo lo que hicieron y el modo preciso en que lo hicieron. Sé que eso excede el ámbito de esta tesis, pero este párrafo es un buen lugar para mostrarles mi gratitud. Gracias. AbstractThis thesis is devoted to the study and application of constraint-based metabolic models. The objective was to find simple ways to handle the difficulties that arise in practice due to uncertainty (knowledge is incomplete, there is a lack of measurable variables, and those available are imprecise). With this purpose, tools have been developed to model, analyse, estimate and predict the metabolic behaviour of cells.The document is structured in three parts. First, related literature is revised and summarised. This results in a unified perspective of several methodologies that use constraint-based representations of the cell metabolism. Three outstanding methods are discussed i...

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On‐line stoichiometry and identification of metabolic state under dynamic process conditions

Cited by 68 publications

References 26 publications

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Short-Term Metabolome Dynamics and Carbon, Electron, and ATP Balances in Chemostat-Grown Saccharomyces cerevisiae CEN.PK 113-7D following a Glucose Pulse

Advanced continuous cultivation methods for systems microbiology

Interval and Possibilistic Methods for Constraint-Based Metabolic Models

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