The Genome-Scale Metabolic Extreme Pathway Structure in Haemophilus influenzae Shows Significant Network Redundancy

Papin, Jason A.; Price, Nathan D.; Edwards, Jeremy S.; Palsson, Bernhard Ø.

doi:10.1006/jtbi.2001.2499

Cited by 107 publications

(71 citation statements)

References 19 publications

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“…9 Earlier studies on mutational robustness have concentrated primarily on individual genes, or on a set of genes that act in the same functional pathway. 9,[18][19][20][21] Even though further studies have addressed the role of redundancy 9,12,22,23 in various molecular interaction networks, including protein regulatory networks, very few studies have investigated the collective properties of real biological networks, which are required to evaluate the significance of concepts like distributed robustness. In this context, the technological advances in high-throughput genomics and proteomics place us in a unique position to reconstruct the interaction network of most genes, and to study them as an integrated system rather than in isolation or as a small group of interacting proteins.…”

Section: Introductionmentioning

confidence: 99%

Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Balaji

Iyer

Aravind

et al. 2006

Journal of Molecular Biology

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Section: Introductionmentioning

confidence: 99%

Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Balaji

Iyer

Aravind

et al. 2006

Journal of Molecular Biology

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“…Using this approach, a variety of methods including flux balance analysis (11, 12), extreme pathway analysis (13,14), and mixed integer linear programming (15) have been developed to characterize the steady-state solution space and search for physiologically relevant metabolic flux distributions. In this paper, optimal flux distributions, alternate optima, extreme points, and optimal solutions are used interchangeably.…”

mentioning

confidence: 99%

Reconstruction and Functional Characterization of the Human Mitochondrial Metabolic Network Based on Proteomic and Biochemical Data

Greenberg

Palsson

2004

Journal of Biological Chemistry

140

125

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Diverse datasets including genomic, proteomic, isotopomer, and DNA sequence variation are becoming available for human mitochondria. Thus there is a need to integrate these data within an in silico modeling framework where mitochondrial biology and related disorders can be studied and analyzed. This paper reports a reconstruction and characterization of the human mitochondrial metabolic network based on proteomic and biochemical data. The 189 reactions included in this reconstruction are both elementally and charge-balanced and are assigned to their respective cellular compartments (mitochondrial, cytosol, or extracellular). The capabilities of the reconstructed network to fulfill three metabolic functions (ATP production, heme synthesis, and mixed phospholipid synthesis) were determined. Network-based analysis of the mitochondrial energy conversion process showed that the overall ATP yield per glucose is 31.5. Network flexibility, characterized by allowable variation in reaction fluxes, was evaluated using flux variability analysis and analysis of all of the possible optimal flux distributions. Results showed that the network has high flexibility for the biosynthesis of heme and phospholipids but modest flexibility for maximal ATP production. A subset of all of the optimal network flux distributions, computed with respect to the three metabolic functions individually, was found to be highly correlated, suggesting that this set may contain physiological meaningful fluxes. Examinations of optimal flux distributions also identified correlated reaction sets that form functional modules in the network.In recent years there has been increasing interest in the mitochondrion as numerous important physiological functions and disorders have been linked to this organelle (1-3). Malfunctioning mitochondrial metabolism is known to play a role not only in rare childhood diseases but also in many leading causes of death including heart disease, diabetes, and Parkinson's disease. The human mitochondrial genome was one of the first genomes to be sequenced (4), and more recently, the proteome of the human cardiac mitochondrion has been identified in two separate studies (5, 6). The analyses of mitochondrial proteomic and isotopomer data have proven to be useful in detecting the onset of cancer and studying glucose homeostasis in diabetes (7,8). With the increasing availability of these independent datasets, there is a growing need for incorporating and reconciling such information in a genetically and biochemically consistent format. We have developed a constraint-based model of the human cardiac mitochondrion to meet this need.The constraint-based approach for analyzing reconstructed networks involves the application of a series of constraints arising from reaction stoichiometry, thermodynamics, enzymatic capacities, and regulatory and kinetic constraints when they are available (9, 10). Using this approach, a variety of methods including flux balance analysis (11, 12), extreme pathway analysis (13,14), and mixed integer linear...

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“…The third step is the determination of the possible solutions in this space that correspond to physiologically meaningful states. This constraint-based modeling procedure has been successfully utilized to study phenotypes in various model [15][16][17][18] and infectious [19,20] microorganisms. Recently, this approach has passed a significant milestone, namely the reconstruction of the human metabolic network [1].…”

Section: Biochemical Reaction Networkmentioning

confidence: 99%

Biochemical and statistical network models for systems biology

Price

Shmulevich

2007

Current Opinion in Biotechnology

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IntroductionThe creation of models of the integrated functions of genes and proteins in cells is of fundamental and immediate importance to the emerging field of computational systems biology. Some of the most successful attempts at cell-scale modeling to date have been based on piecing together networks that represent hundreds of experimentally-determined biochemical interactions, while others have been very successful at inferring statistical networks from large amounts of high-throughput data. These networks (metabolic, regulatory, or signaling) can be analyzed, and predictions about cellular behavior made and tested. Many types of models have been built and applied to study cellular behavior and in this review we focus on two broad types: biochemical network models and statistical inference models. Through iterative model prediction, experimentation, and network refinement, the molecular circuitry and functions of biological networks can be elucidated. The construction of genomescale models that integrate the myriad components that produce cellular behavior is a fundamental goal of systems biology today. Biochemical Reaction NetworksBiochemical reaction networks represent the underlying chemistry of the system, and thus at a minimum represent stoichiometric relationships between inter-converted biomolecules. The stoichiometry of biochemical reaction networks can now be reconstructed at the genome-scale, and at smaller scale with sufficient detail to generate kinetic models. These biochemical reaction networks represent many years of accumulated experimental data and can be interrogated in silico to determine their functional states. Genome-scale models based on biochemical networks provide a comprehensive, yet concise, description of cellular functions.For metabolism the reconstruction of the biochemical reaction network is a well-established procedure [1][2][3][4][5][6][7], while methods for the reconstruction of the associated regulatory [8,9] and signaling networks [10][11][12] with stoichiometric detail are being developed. The typically used formalism is to reconstruct the stoichiometric matrix, where each row represents a molecular compound and each column represents a reaction. For metabolism, these networks are often focused on just the metabolites, where the existence of a protein that catalyzes this reaction is used to allow that reaction to be present in the network. It is also possible (and truer to the realities in the system) to represent the proteins themselves as compounds in the network, which enables the integration of proteomics, metabolomics, and flux data (Figure 1). For regulatory and signaling networks, the inclusion of the proteins as compounds is essential. This process * To whom correspondence should be addressed: ishmulevich@sytemsbiology.org. + Present Address: Department of Chemical and Biomolecular Engineering University of Illinois, Urbana-Champaign Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our c...

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The Genome-Scale Metabolic Extreme Pathway Structure in Haemophilus influenzae Shows Significant Network Redundancy

Cited by 107 publications

References 19 publications

Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks

Reconstruction and Functional Characterization of the Human Mitochondrial Metabolic Network Based on Proteomic and Biochemical Data

Biochemical and statistical network models for systems biology

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