A method for the determination of flux in elementary modes, and its application to <i>Lactobacillus rhamnosus</i>

Poolman, Mark G.; Venkatesh, K. V.; Pidcock, Michael; Fell, David A.

doi:10.1002/bit.20273

Cited by 62 publications

(69 citation statements)

References 17 publications

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“…Once such biological information has been amassed, a mathematical approach can be used to predict metabolic behavior to the extent determined by the nature of the modeling framework. Thus flux balance analysis (FBA), [1][2][3] metabolic flux analysis (MFA), [4][5][6] and other flux-based approaches such as elementary flux modes, 7,8 which are based on steady-state assumptions, concentrate on predicting all metabolic fluxes of interest (for example, excretion rates of end-products, growth rate of biomass, exchange rate of cofactors, and so on). Dynamic approaches, on the other hand, will require dynamic measurements of various extra and intracellular variables for parameter identification.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

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Section: Introductionmentioning

confidence: 99%

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

Kim

Varner

Ramkrishna

2008

Biotechnology Progress

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“…As demonstrated by [38], metabolic networks exhibit unique prop-erties that are most effectively understood by the combined analysis of both network structure (topology) and pathway function. This insight encompasses the major mode of analysis in several recent studies that utilize a combined FBA and pathway analysis approach [71, 72,59,48,24]. We may envision the integration of the OCCI algorithm with such pathway-based methods, wherein the global network effect of an identified capacity constraint can be assessed by investigating the capacity constraint as part of an extreme pathway [60], or an elementary flux mode [62].…”

Section: Incorporation Of Flux Coupling Characteristics Improves Occimentioning

confidence: 99%

A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks

Yang

Mahadevan

Cluett

2008

Computers & Chemical Engineering

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Constraint-based models of metabolism seldom incorporate capacity constraints on intracellular fluxes due to the lack of experimental data. This can sometimes lead to inaccurate growth phenotype predictions. Meanwhile, other forms of data such as fitness profiling data from growth competition experiments have been demonstrated to contain valuable information for elucidating key aspects of the underlying metabolic network.Hence, the optimal capacity constraint identification (OCCI) algorithm is developed to reconcile constraint-based models of metabolism with fitness profiling data by identifying a set of flux capacity constraints that optimally fits a wide array of strains. OCCI is able to identify capacity constraints with considerable accuracy by matching 1,155 in silico-generated growth rates using a simplified model of Escherichia coli central carbon metabolism. Capacity constraints identified using experimental fitness profiles with OCCI generated novel hypotheses, while integrating thermodynamics-based metabolic flux analysis allowed prediction of metabolite concentrations.ii

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“…• Poolman et al (2004) used the same assumption, and although the calculation procedure was different, very similar results were obtained.…”

Section: Particular Translation Methodsmentioning

confidence: 71%

“…Nookaew et al have proposed to get estimates based on the assumption that cells are likely to use as many pathways as possible to maintain robustness and redundancy (2007). Related hypotheses have been formulated using the concept of elementary modes (Poolman, 2004;Schwartz, 2006). The FBA assumption of optimal cell behaviour could be also invoked.…”

Section: Limitations Of Mfamentioning

confidence: 99%

“…It will be shown that in most cases there are multiple valid translations, that is, that a given a flux state can be represented with different patterns of pathway activities. Two approaches are usually followed: chose one pattern based on a reasonably assumption (Poolman, 2004;Schwartz, 2006), or deal with the whole space of possible patterns. The second approach relies on the so-called α-spectrum, the ranges of possible activities for each pathway (Wiback, 2003).…”

Section: Introductionmentioning

confidence: 99%

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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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A method for the determination of flux in elementary modes, and its application to Lactobacillus rhamnosus

Cited by 62 publications

References 17 publications

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

A hybrid model of anaerobic E. coli GJT001: Combination of elementary flux modes and cybernetic variables

A bilevel optimization algorithm to identify enzymatic capacity constraints in metabolic networks

Interval and Possibilistic Methods for Constraint-Based Metabolic Models

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