Computational Models of Metabolism: Stability and Regulation in Metabolic Networks

Steuer, Ralf; Junker, Björn H.

doi:10.1002/9780470475935.ch3

Cited by 53 publications

(74 citation statements)

References 313 publications

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“…In this respect, constraint-based modeling, most notably flux-balance analysis (FBA), has become a quasi-standard in the field. FBA is increasingly utilized to elucidate and characterize largescale network properties, to direct the discovery of novel or alternative pathways, to guide metabolic engineering, as well as for the conceptualization of high-throughput data (Oberhardt et al, 2009;Steuer and Junker, 2009). As one of its prime advantages, constraint-based modeling does not require knowledge of the kinetic parameters of individual metabolic reactions, making it applicable to large-scale, up to genome-scale, metabolic networks.…”

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confidence: 99%

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

et al. 2010

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and Manchester Interdisciplinary Biocentre, University of Manchester, M1 7DN Manchester, United Kingdom (R.S.) Unicellular cyanobacteria have attracted growing attention as potential host organisms for the production of valuable organic products and provide an ideal model to understand oxygenic photosynthesis and phototrophic metabolism. To obtain insight into the functional properties of phototrophic growth, we present a detailed reconstruction of the primary metabolic network of the autotrophic prokaryote Synechocystis sp. PCC 6803. The reconstruction is based on multiple data sources and extensive manual curation and significantly extends currently available repositories of cyanobacterial metabolism. A systematic functional analysis, utilizing the framework of flux-balance analysis, allows the prediction of essential metabolic pathways and reactions and allows the identification of inconsistencies in the current annotation. As a counterintuitive result, our computational model indicates that photorespiration is beneficial to achieve optimal growth rates. The reconstruction process highlights several obstacles currently encountered in the context of large-scale reconstructions of metabolic networks.Cyanobacteria are among the evolutionarily oldest organisms and are the only known prokaryotes capable of plant-like oxygenic photosynthesis. As primary producers in aquatic environments, they play an important role in global CO 2 assimilation and oxygen recycling. Recently, cyanobacteria have also attracted growing attention for economic purposes, including drug discovery and as prolific producers of natural products (Sielaff et al., 2006;Tan, 2007). In particular their ability to directly convert atmospheric CO 2 into biomass and organic compounds, driven by sunlight, offers considerable potential as a novel and renewable resource for bioenergy (Deng and Coleman, 1999;Atsumi et al., 2009;Mascarelli, 2009;Lindberg et al., 2010).Among the diverse cyanobacterial strains, Synechocystis sp. PCC 6803 is one of the most extensively studied model organisms for the analysis of photosynthetic processes. With a rich compendium of genomic, biochemical, and physiological data available, Synechocystis sp. PCC 6803, therefore, offers an ideal starting point to obtain insights into the systemic properties of phototrophic metabolism. The prerequisite for such a systemic description is a detailed reconstruction of the metabolic network of the organism: that is, a reconstruction of the comprehensive set of enzyme-catalyzed reactions required to support cellular growth and maintenance. Once a metabolic reconstruction is available, the vast array of methods developed by computational systems biology over the past decades allows us to dissect the functioning and interplay of possible metabolic routes and biochemical interconversions. In this respect, constraint-based modeling, most notably flux-balance analysis (FBA), has become a quasi-standard in the field. FBA is increasingly utilized to elucidate and characterize largescale network...

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confidence: 99%

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

et al. 2010

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“…Most of the material of this section, with the exception of the identification of different layers of representation, is standard material in the reaction networks literature. For a comprehensive treatment of the reaction network formalism, we refer to [9,34]. From now on, let M = {s 1 , .…”

Section: Reaction Networkmentioning

confidence: 99%

“…In various biochemistry-related areas such as systems biology, bioinformatics, and chemical computing, reaction networks are the mainstream language of representation [7][8][9]. Interestingly, the language of reaction networks allows for three levels of representation: relational, stoichiometric, and kinetic, respectively.…”

Section: Introductionmentioning

confidence: 99%

Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Veloz

Razeto‐Barry

2017

Systems

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Abstract:The basic processes that bring about living systems are conventionally represented in the framework of chemical reaction networks. Recently, it has been proposed that this framework can be exploited for studying various other phenomena. Reaction networks are specially suited for representing situations where different types of entities interact in contextual ways leading to the emergence of meta-structures. At an abstract level, a reaction network represents a universe whose evolution corresponds to the transformation of collections of entities into other collections of entities. Hence, we propose that systems correspond to the sub-networks that are stable enough to be observed. In this article, we discuss how to use reaction networks for representing systems. Namely, we introduce the different representational levels available (relational, stoichiometric, and kinetic), we show how to identify observable systems in the reaction network, discuss some relevant systemic notions such as context, emergence, and meta-system, and present some examples.

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“…To overcome some of the difficulties imposed by the lack of information on kinetic parameters, there has been increasing interest in heuristic and semi-quantitative methods to describe the dynamics of large-scale metabolic networks in the face of uncertain kinetic data [23,22,20]. Specifically, structural kinetic modeling (SKM) proposes to augment the constraint-based analysis by a local stability analysis utilizing an appropriate parametrization of the Jacobian matrix [21].…”

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Guaranteed and Randomized Methods for Stability Analysis of Uncertain Metabolic Networks

Koeppl

Andreozzi

Steuer

2010

Advances in the Theory of Control, Signals and Systems With Physical Modeling

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A persistent problem hampering our understanding of the dynamics of large-scale metabolic networks is the lack of experimentally determined kinetic parameters that are necessary to build computational models of biochemical processes. To overcome some of the limitations imposed by absent or incomplete kinetic data, structural kinetic modeling (SKM) was proposed recently as an intermediate approach between stoichiometric analysis and a full kinetic description. SKM extends stationary flux-balance analysis (FBA) by a local stability analysis utilizing an appropriate parametrization of the Jacobian matrix. To characterize the Jacobian, we utilize results from robust control theory to determine subintervals of the Jacobian' entries that correspond to asymptotically stable metabolic states. Furthermore, we propose an efficient sampling scheme in combination with methods from computational geometry to sketch the stability region. A glycolytic pathway model comprising 12 uncertain parameters is used to assess the feasibility of the method.

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Computational Models of Metabolism: Stability and Regulation in Metabolic Networks

Cited by 53 publications

References 313 publications

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

The Metabolic Network of Synechocystis sp. PCC 6803: Systemic Properties of Autotrophic Growth

Reaction Networks as a Language for Systemic Modeling: Fundamentals and Examples

Guaranteed and Randomized Methods for Stability Analysis of Uncertain Metabolic Networks

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