Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Yizhak, Keren; Benyamini, Tomer; Liebermeister, Wolfram; Ruppin, Eytan; Shlomi, Tomer

doi:10.1093/bioinformatics/btq183

Cited by 235 publications

(186 citation statements)

References 28 publications

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“…GIM 3 E); (2) identification of turnover rates and limiting metabolites; (3) prediction of fluxes compatible with a particular kinetic law and thermodynamic principles (Hoppe et al, 2007;Yizhak et al, 2010;Tepper et al, 2013); and (4) prediction of timeresolved flux distributions. The integration of metabolite levels in constraint-based methods and their applications in plant research have already been systematically reviewed elsewhere (Töpfer et al, 2015).…”

Section: Linking Fluxes To Metabolitesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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In this Update, we cover the basic principles of the estimation and prediction of the rates of the many interconnected biochemical reactions that constitute plant metabolic networks. This includes metabolic flux analysis approaches that utilize the rates or patterns of redistribution of stable isotopes of carbon and other atoms to estimate fluxes, as well as constraints-based optimization approaches such as flux balance analysis. Some of the major insights that have been gained from analysis of fluxes in plants are discussed, including the functioning of metabolic pathways in a network context, the robustness of the metabolic phenotype, the importance of cell maintenance costs, and the mechanisms that enable energy and redox balancing at steady state. We also discuss methodologies to exploit 'omic data sets for the construction of tissue-specific metabolic network models and to constrain the range of permissible fluxes in such models. Finally, we consider the future directions and challenges faced by the field of metabolic network flux phenotyping.The metabolic systems of plants utilize a continual energy stream (i.e. light in the case of photosynthetic tissues and chemical energy in the case of heterotrophic tissues) to drive a complex network of hundreds of chemical reactions away from equilibrium. Ultimately, this leads to the catalyzed biosynthesis of the ordered polymeric biomolecules that make up the biomass of the cells in each tissue and underpins the growth and development of the plant (Smith and Stitt, 2007). To operate on a time scale relevant for life, the system is dependent upon the acceleration of its chemistry by enzymes. Additionally, because of the highly compartmented nature of plant cells, transporter proteins are required to permit the movement of metabolites (i.e. the substrates and products of biochemical reactions) between subcellular compartments. Transporter proteins are also required to bring substrates into cells and to allow the excretion of waste products and other metabolites such as defense compounds. The sum total of expressed genes encoding enzymes and metabolite transporters determines the metabolic capabilities of a cell. In a growing tissue, the net output of this metabolism is anabolic (i.e. leading to the biosynthesis of biomass constituents such as starch, fructans, cell wall, lipid, and protein), but catabolic metabolism is also required. Most importantly, catabolism generates universal energy currencies such as ATP and NAD(P)H, whose turnover is used to provide the energetic driving force for anabolism. Catabolism is also important to allow the turnover of cellular components for regulatory and repair purposes (Linster et al., 2013;Ishihara et al., 2015). The turnover and resynthesis of cellular components, along with the maintenance of electrochemical potentials across membranes, are important facets of metabolism that need to be considered alongside the biosynthesis of macromolecules for growth (Stitt, 2013;Sweetlove et al., 2013).Metabolism, then, is the entirety of c...

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Section: Linking Fluxes To Metabolitesmentioning

confidence: 99%

Inference and prediction of metabolic network fluxes

Nikoloski

Perez-Storey

2015

Plant Physiol.

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“…However, the former are rather scarce, small-scale, and are taken mostly from cell lines. The latter require inferring the effects metabolite concentrations have on enzyme activity by incorporating the measurements in kinetic rate equations or by accounting for thermodynamic principles (31). Transcriptomics and proteomics, which are becoming increasingly more accurate and accessible, can also provide important insights into the regulation of metabolic flux.…”

Section: Genome-scale Metabolic Modelingmentioning

confidence: 99%

Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling

Jerby

Ruppin

2012

Clinical Cancer Research

105

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The metabolism of cancer cells is reprogrammed in various ways to support their growth and survival. Studying these phenomena to develop noninvasive diagnostic tools and selective treatments is a promising avenue. Metabolic modeling has recently emerged as a new way to study human metabolism in a systematic, genome-scale manner by using pertinent high-throughput omics data. This method has been shown in various studies to provide fairly accurate estimates of the metabolic phenotype and its modifications following genetic and environmental perturbations. Here, we provide an overview of genome-scale metabolic modeling and its current use to model human metabolism in health and disease. We then describe the initial steps made using it to study cancer metabolism and how it may be harnessed to enhance ongoing experimental efforts to identify drug targets and biomarkers for cancer in a rationale-based manner.

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“…Although the initial goal of the in silico model development was to fundamentally account for intracellular and environmental exchange reactions to more accurately predict TEAP conversion rates, the advent of mass spectrometry-based protein identification technology now provides an experimental way to assess the detailed intracellular reactions. Further advancements in the measurement of subsurface in situ processes via environmental proteomics now offers the potential to track metabolic functions to specific bacterial species (9, 61).Previous studies have suggested that protein abundances inferred from proteomic data can be assumed to be proportional to the metabolic flux through a specific reaction (14,32,67), making possible the assessment of genome-scale models. Colijin et al (14) …”

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confidence: 99%

mentioning

confidence: 99%

“…Previous studies have suggested that protein abundances inferred from proteomic data can be assumed to be proportional to the metabolic flux through a specific reaction (14,32,67), making possible the assessment of genome-scale models. Colijin et al (14) used whole-cell measurement of gene expression data to model the maximum flux through individual metabolic reactions using a genome-scale model of Mycobacterium tuberculosis, the pathogen that causes tuberculosis.…”

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Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

Fang

Wilkins

Yabusaki

et al. 2012

Appl Environ Microbiol

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bAccurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzymecoding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment. Microbial environments have been engineered for enhanced oil recovery (7,23,29,33,42) and remediation of soil and groundwater contaminated by chlorinated hydrocarbons, metals, and radionuclides (4,24,30,40,47,51,63) in the past 30 years and have recently been studied to manage carbon dioxide (46). Mathematical models of microbial processes have been used for experimental interpretation, design, and prediction in recent decades (3,10,20,22,39,48,49,53,56). These processes were very complex to model because there was no way to measure the detailed metabolisms inside the system. They were usually represented by specific terminal electron acceptor process (TEAP) reactions with fixed stoichiometry throughout the simulation and were traditionally modeled by Monod kinetics. These models are sufficient under nonlimiting conditions of nutrients. However, the kinetic parameters of these models, which were calibrated but not directly measurable, cannot reflect the sophisticated mechanisms developed by microorganisms to adapt to the changing environment through the regulation of metabolic pathways, such as their ability to respond to nutrient gradients and environmental stress (5, 6).With the ad...

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Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model

Cited by 235 publications

References 28 publications

Inference and prediction of metabolic network fluxes

Inference and prediction of metabolic network fluxes

Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens by Using Proteomic Data from a Field Biostimulation Experiment

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