Regulation of Gene Expression in Flux Balance Models of Metabolism

Covert, Markus W.; Schilling, Christophe H.; Palsson, Bernhard Ø.

doi:10.1006/jtbi.2001.2405

Cited by 389 publications

(357 citation statements)

References 48 publications

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“…The molecular mechanisms underlying the Crabtree effect in S. cerevisiae are not known. The regulatory features of the Crabtree effect (36), however, can be included in the in silico model as an experimentally determined (37) growth-rate-dependent maximum oxygen uptake rate (38). With this additional constraint, the in silico model makes quantitative predictions about the respiratory quotient, glucose uptake, ethanol, CO 2 , and glycerol secretion rates under aerobic glucose-limited continuous conditions (Fig.…”

Section: Resultsmentioning

confidence: 99%

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili

Förster

Nielsen

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

338

244

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Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for Ϸ70 -80% of the conditions considered. S ystems biology is commonly viewed as a four-step procedure (1-3): (i) the enumeration of the biological components that make up the biological process of interest, (ii) the reconstruction of the network of interactions among these components, (iii) the in silico simulation of the network function, and (iv) the comparison of computed network properties with actual phenotypic observations. A wealth of available biological data for Saccharomyces cerevisiae (4-7) led to the establishment of the first genome-scale reconstruction of the metabolic network in a eukaryotic cell (8). The initial completion of the first two steps of the systems biology procedure for yeast metabolism has been achieved. Steps iii and iv have not yet been carried out for a eukaryotic organism.Integrated functions of reconstructed metabolic networks can be determined in silico by using a number of analytical approaches (9-11). The relatively young constraint-based approach differs fundamentally from the more traditional kinetic theory-based approaches in that it is not aimed at finding the solution or behavior of the network under certain conditions but rather at eliminating solutions (behaviors) that the network cannot exhibit (Fig. 1). By using this approach, a network of interactions is successively constrained by defining the stoichiometry of the interacting components, the direction of network reactions, and the maximum allowable throughput. In this way, candidate solutions to the network equations are systematically eliminated by the successive application of the governing constraints, i.e., stoichiometry, thermodynamics, and maximal enzymatic rates (12). One thus can define the range of capabilities of the reconstructed network and then, through the use of optimization procedur...

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Section: Resultsmentioning

confidence: 99%

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Famili

Förster

Nielsen

et al. 2003

Proc. Natl. Acad. Sci. U.S.A.

338

244

View full text Add to dashboard Cite

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“…It should be noted that 52 direct and indirect regulatory interactions using 12 regulatory proteins for the discrimination between the photosynthetic and nonphotosynthetic cells under normal and stressed conditions were also incorporated into the model using Boolean logic formalism, on the basis of established procedures (Covert et al, 2001; see "Materials and Methods"). As a result, the activation of 40 light-specific metabolic reactions in the model can be controlled by a relevant logic statement.…”

Section: Reconstruction Of the Rice Central Metabolic Modelmentioning

confidence: 99%

“…Finally, this information was represented as a set of regulatory rules using the Boolean formalism, which can guide whether relevant reactions are either active (ON) or not (OFF; Covert et al, 2001). Specifically, the activity/inactivity of a particular reaction in the presence of a certain regulatory protein(s) can be described using various logical operators such as IF, AND, OR, and NOT.…”

Section: Metabolic Network Reconstructionmentioning

confidence: 99%

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

et al. 2013

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Rice (Oryza sativa) is one of the major food crops in world agriculture, especially in Asia. However, the possibility of subsequent occurrence of flood and drought is a major constraint to its production. Thus, the unique behavior of rice toward flooding and drought stresses has required special attention to understand its metabolic adaptations. However, despite several decades of research investigations, the cellular metabolism of rice remains largely unclear. In this study, in order to elucidate the physiological characteristics in response to such abiotic stresses, we reconstructed what is to our knowledge the first metabolic/regulatory network model of rice, representing two tissue types: germinating seeds and photorespiring leaves. The phenotypic behavior and metabolic states simulated by the model are highly consistent with our suspension culture experiments as well as previous reports. The in silico simulation results of seed-derived rice cells indicated (1) the characteristic metabolic utilization of glycolysis and ethanolic fermentation based on oxygen availability and (2) the efficient sucrose breakdown through sucrose synthase instead of invertase. Similarly, flux analysis on photorespiring leaf cells elucidated the crucial role of plastid-cytosol and mitochondrion-cytosol malate transporters in recycling the ammonia liberated during photorespiration and in exporting the excess redox cofactors, respectively. The model simulations also unraveled the essential role of mitochondrial respiration during drought stress. In the future, the combination of experimental and in silico analyses can serve as a promising approach to understand the complex metabolism of rice and potentially help in identifying engineering targets for improving its productivity as well as enabling stress tolerance.

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“…Dynamic batch-culture growth simulations (8,9) have been performed by using constraint-based analysis (10)(11)(12)(13)(14). Constraint-based analysis has been used to assess optimal growth states (15), consequences of gene knockouts (16)(17)(18), endpoints of adaptive evolution (19), synthetic lethals (20), epistatic interactions between metabolic genes (21), and enzyme dispensability (22).…”

mentioning

confidence: 99%

The global transcriptional regulatory network for metabolism inEscherichia coliexhibits few dominant functional states

Barrett

Herring

Reed

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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A principal aim of systems biology is to develop in silico models of whole cells or cellular processes that explain and predict observable cellular phenotypes. Here, we use a model of a genome-scale reconstruction of the integrated metabolic and transcriptional regulatory networks for Escherichia coli, composed of 1,010 gene products, to assess the properties of all functional states computed in 15,580 different growth environments. The set of all functional states of the integrated network exhibits a discernable structure that can be visualized in 3-dimensional space, showing that the transcriptional regulatory network governing metabolism in E. coli responds primarily to the available electron acceptor and the presence of glucose as the carbon source. This result is consistent with recently published experimental data. The observation that a complex network composed of 1,010 genes is organized to achieve few dominant modes demonstrates the utility of the systems approach for consolidating large amounts of genome-scale molecular information about a genome and its regulation to elucidate an organism's preferred environments and functional capabilities.systems biology ͉ transcriptional regulation T wo prominent paradigms in modern biology about which there has been much discussion are the reductionist and integrative (or systems) approaches to biological discovery and understanding (1-5). The utility of the systems approach is demonstrated when computational models are used to integrate large amounts of molecular data to interpret and predict the range of cellular phenotypes. We investigated the utility of the systems approach by using a genome-scale reconstruction of the integrated transcriptional regulatory and metabolic network for Escherichia coli (iMC1010 v1

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Regulation of Gene Expression in Flux Balance Models of Metabolism

Cited by 389 publications

References 48 publications

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis

The global transcriptional regulatory network for metabolism inEscherichia coliexhibits few dominant functional states

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