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62
Metabolic pathways in the postgenome era
 Trends in Biochem. Sci
, 2003
"... Metabolic pathways are a central paradigm in biology. Historically, they have been defined on the basis of their stepbystep discovery. However, the genomescale metabolic networks now being reconstructed from annotation of genome sequences demand new networkbased definitions of pathways to facili ..."
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Metabolic pathways are a central paradigm in biology. Historically, they have been defined on the basis of their stepbystep discovery. However, the genomescale metabolic networks now being reconstructed from annotation of genome sequences demand new networkbased definitions of pathways to facilitate analysis of their capabilities and functions, such as metabolic versatility and robustness, and optimal growth rates. This demand has led to the development of a new mathematically based analysis of complex, metabolic networks that enumerates all their unique pathways that take into account all requirements for cofactors and byproducts. Applications include the design of engineered biological systems, the generation of testable hypotheses regarding network structure and function, and the elucidation of properties that can not be described by simple descriptions of individual components (such as product yield, network robustness, correlated reactions and predictions of minimal media). Recently, these properties have also been studied in genomescale networks. Thus, networkbased pathways are emerging as an important paradigm for analysis of biological systems. The highthroughput experimental technologies that have rapidly developed within genomic science allow us to obtain comprehensive data about the molecular makeup of cells, thus enabling us to reconstruct largescale (even genomescale) reaction networks To date, traditional metabolic pathways [4] have served as conceptual frameworks for research and teaching. These pathways provide an important means of effective Most of these reactions have been grouped into 'traditional pathways' (e.g. glycolysis) that do not account for cofactors and byproducts in a way that lends itself to a mathematical description. With sequenced and annotated genomes, models can be made that account for many metabolic reactions in an organism. (c) Subsequently, networkbased, mathematically defined pathways can be analyzed that account for a complete network (note that black and gray arrows correspond to active and inactive reactions, respectively).
Characterization of metabolism in the Fe(III)Reducing organism geobacter sulfurreducens by ConstraintBased modeling
 Applied and Environmental Microbiology
, 2006
"... This article cites 82 articles, 34 of which can be accessed free ..."
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Cited by 24 (6 self)
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This article cites 82 articles, 34 of which can be accessed free
Thermodynamically feasible kinetic models of reaction networks
 Biophys J
, 2007
"... ABSTRACT The dynamics of biological reaction networks are strongly constrained by thermodynamics. An holistic understanding of their behavior and regulation requires mathematical models that observe these constraints. However, kinetic models may easily violate the constraints imposed by the principl ..."
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Cited by 23 (1 self)
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ABSTRACT The dynamics of biological reaction networks are strongly constrained by thermodynamics. An holistic understanding of their behavior and regulation requires mathematical models that observe these constraints. However, kinetic models may easily violate the constraints imposed by the principle of detailed balance, if no special care is taken. Detailed balance demands that in thermodynamic equilibrium all fluxes vanish. We introduce a thermodynamickinetic modeling (TKM) formalism that adapts the concepts of potentials and forces from irreversible thermodynamics to kinetic modeling. In the proposed formalism, the thermokinetic potential of a compound is proportional to its concentration. The proportionality factor is a compoundspecific parameter called capacity. The thermokinetic force of a reaction is a function of the potentials. Every reaction has a resistance that is the ratio of thermokinetic force and reaction rate. For massaction type kinetics, the resistances are constant. Since it relies on the thermodynamic concept of potentials and forces, the TKM formalism structurally observes detailed balance for all values of capacities and resistances. Thus, it provides an easy way to formulate physically feasible, kinetic models of biological reaction networks. The TKM formalism is useful for modeling large biological networks that are subject to many detailed balance relations.
Uniform sampling of steadystate flux spaces: means to design experiments and to interpret enzymopathies
 Biophys J
, 2004
"... ABSTRACT Reconstruction of genomescale metabolic networks is now possible using multiple different data types. Constraintbased modeling is an approach to interrogate capabilities of reconstructed networks by constraining possible cellular behavior through the imposition of physicochemical laws. As ..."
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Cited by 19 (4 self)
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ABSTRACT Reconstruction of genomescale metabolic networks is now possible using multiple different data types. Constraintbased modeling is an approach to interrogate capabilities of reconstructed networks by constraining possible cellular behavior through the imposition of physicochemical laws. As a result, a steadystate flux space is defined that contains all possible functional states of the network. Uniform random sampling of the steadystate flux space allows for the unbiased appraisal of its contents. Monte Carlo sampling of the steadystate flux space of the reconstructed human red blood cell metabolic network under simulated physiologic conditions yielded the following key results: 1), probability distributions for the values of individual metabolic fluxes showed a wide variety of shapes that could not have been inferred without computation; 2), pairwise correlation coefficients were calculated between all fluxes, determining the level of independence between the measurement of any two fluxes, and identifying highly correlated reaction sets; and 3), the networkwide effects of the change in one (or a few) variables (i.e., a simulated enzymopathy or fixing a flux range based on measurements) were computed. Mathematical models provide the most compact and informative representation of a hypothesis of how a cell works. Thus, understanding model predictions clearly is vital to driving forward the iterative modelbuilding procedure that is at the heart of systems biology. Taken together, the Monte Carlo sampling procedure provides a broadening of the constraintbased approach by allowing for the unbiased and detailed assessment of the impact of the applied physicochemical constraints on a reconstructed network.
Bayesianbased selection of metabolic objective functions
"... doi:10.1093/bioinformatics/btl619 ..."
Flux balance analysis of photoautotrophic metabolism
 Biotechnol. Prog. 2005
"... Photosynthesis is the principal process responsible for fixation of inorganic carbon dioxide into organic molecules with sunlight as the energy source. Potentially, many chemicals could be inexpensively produced by photosynthetic organisms. Mathematical modeling of photoautotrophic metabolism is the ..."
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Photosynthesis is the principal process responsible for fixation of inorganic carbon dioxide into organic molecules with sunlight as the energy source. Potentially, many chemicals could be inexpensively produced by photosynthetic organisms. Mathematical modeling of photoautotrophic metabolism is therefore important to evaluate maximum theoretical product yields and to deeply understand the interactions between biochemical energy, carbon fixation, and assimilation pathways. Flux balance analysis based on linear programming is applied to photoautotrophic metabolism. The stoichiometric network of a model photosynthetic prokaryote, Synechocystis sp. PCC 6803, has been reconstructed from genomic data and biochemical literature and coupled with a model of the photophosphorylation processes. Flux map topologies for the hetero, auto, and mixotrophic modes of metabolism under conditions of optimal growth were determined and compared. The roles of important metabolic reactions such as the glyoxylate shunt and the transhydrogenase reaction were analyzed. We also theoretically evaluated the effect of gene deletions or additions on biomass yield and metabolic flux distributions.
From annotated genomes to metabolic flux models and kinetic parameter fitting
, 2003
"... Significant advances in systemlevel modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steadystate models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic flu ..."
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Cited by 14 (1 self)
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Significant advances in systemlevel modeling of cellular behavior can be achieved based on constraints derived from genomic information and on optimality hypotheses. For steadystate models of metabolic networks, mass conservation and reaction stoichiometry impose linear constraints on metabolic fluxes. Different objectives, such as maximization of growth rate or minimization of flux distance from a reference state, can be tested in different organisms and conditions. In particular, we have suggested that the metabolic properties of mutant bacterial strains are best described by an algorithm that performs a minimization of metabolic adjustment (MOMA) upon gene deletion. The increasing availability of many annotated genomes paves the way for a systematic application of these flux balance methods to a large variety of organisms. However, such a high throughput goal crucially depends on our capacity to build metabolic flux models in a fully automated fashion. Here we describe a pipeline for generating models from annotated genomes and discuss the current obstacles to full automation. In addition, we propose a framework for the integration of flux modeling results and high throughput proteomic data, which can potentially help in the inference of wholecell kinetic parameters.
Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data
 Metabolomics
, 2006
"... Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict lar ..."
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Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networks ab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly informationrich and clearly nonrandom. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systemswide analysis of cellular metabolism. KEY WORDS: Fourier transform mass spectrometry; metabolic networks; network reconstruction; computational methods. 1.
2003. Analysis of metabolic capabilities using singular value decomposition of extre pathway matrices
 Biophysical Journal
"... ABSTRACT It is now possible to construct genomescale metabolic networks for particular microorganisms. Extreme pathway analysis is a useful method for analyzing the phenotypic capabilities of these networks. Many extreme pathways are needed to fully describe the functional capabilities of genomesc ..."
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ABSTRACT It is now possible to construct genomescale metabolic networks for particular microorganisms. Extreme pathway analysis is a useful method for analyzing the phenotypic capabilities of these networks. Many extreme pathways are needed to fully describe the functional capabilities of genomescale metabolic networks, and therefore, a need exists to develop methods to study these large sets of extreme pathways. Singular value decomposition (SVD) of matrices of extreme pathways was used to develop a conceptual framework for the interpretation of large sets of extreme pathways and the steadystate flux solution space they define. The key results of this study were: 1), convex steadystate solution cones describing the potential functions of biochemical networks can be studied using the modes generated by SVD; 2), Helicobacter pylori has a more rigid metabolic network (i.e., a lower dimensional solution space and a more dominant first singular value) than Haemophilus influenzae for the production of amino acids; and 3), SVD allows for direct comparison of different solution cones resulting from the production of different amino acids. SVD was used to identify key network branch points that may identify key control points for regulation. Therefore, SVD of matrices of extreme pathways has proved to be a useful method for analyzing the steadystate solution space of genomescale metabolic networks.