Genome-scale constraint-based modeling of Geobacter metallireducens

Sun, Junjun; Sayyar, Bahareh; Butler, Jessica; Pharkya, Priti; Fahland, Tom R; Famili, Iman; Schilling, Christophe H.; Lovley, Derek R.; Mahadevan, Radhakrishnan

doi:10.1186/1752-0509-3-15

Cited by 71 publications

(73 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The models for G. Sulfurreducens and G. Metallireducens are described in [21] and [22], respectively, and the model files are available in the supplementary materials of these papers.expFBA was able to increase Fe 2+ excretion in Geobacter Sulfurreducens by a maximum of 206 %, while keeping biomass production at 96 % of the wild-type value. Geobacter Metallireducens was able to achieve a more modest result, with excretion increased by a maximum of 35 % of the wild-type value, with a biomass production of 98 % of wild-type.…”

Section: Geobacter Comparison Case Studymentioning

confidence: 99%

Iterative Multi Level Calibration of Metabolic Networks

Conway

Angione²,

Lió³

2016

CBIO

View full text Add to dashboard Cite

Section: Geobacter Comparison Case Studymentioning

confidence: 99%

Iterative Multi Level Calibration of Metabolic Networks

Conway

Angione²,

Lió³

2016

CBIO

View full text Add to dashboard Cite

“…Because the constraint-based flux balance approach uses constraints such as mass balance, reaction reversibility, and bounds of reaction fluxes, a feasible solution may be found, given an objective function. Among the nearly 20 constraint-based models (15) for bacterial species, 2 have been used to analyze the metabolism and physiology of the Geobacter species that are capable of U(VI) reduction (37,52). A constraint-based metabolic model of Geobacter sulfurreducens (37) has been successfully incorporated into a continuum scale reactive transport model (17,50).…”

mentioning

confidence: 99%

“…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 advent of high-throughput sequencing data for genomics and transcriptomics, a number of genome-scale in silico models have been built for various organisms to study cell metabolism (13,15,37,52,54,60) using different mathematical models, such as detailed kinetic models (36) and constraint-based models (16,44,58). The genome-scale models make the integration of cellular dynamics and continuum scale model possible by exchanging information on the growth rate, substrate uptake rates, and by-product rates under changing environmental conditions between the models (17, 27, 50).…”

mentioning

confidence: 99%

“…The genome-scale models make the integration of cellular dynamics and continuum scale model possible by exchanging information on the growth rate, substrate uptake rates, and by-product rates under changing environmental conditions between the models (17,27,50). Highly detailed, constraint-based genome-scale metabolic in silico models have been developed using genomic data to identify metabolic pathways (28,31,37,38,44,45,52,55). Because the constraint-based flux balance approach uses constraints such as mass balance, reaction reversibility, and bounds of reaction fluxes, a feasible solution may be found, given an objective function.…”

mentioning

confidence: 99%

“…With the advent of high-throughput sequencing data for genomics and transcriptomics, a number of genome-scale in silico models have been built for various organisms to study cell metabolism (13,15,37,52,54,60) using different mathematical models, such as detailed kinetic models (36) and constraint-based models (16,44,58). The genome-scale models make the integration of cellular dynamics and continuum scale model possible by exchanging information on the growth rate, substrate uptake rates, and by-product rates under changing environmental conditions between the models (17,27,50).…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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...

show abstract

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Masoudi‐Nejad

Salehzadeh‐Yazdi

Akbari‐Birgani

et al. 2013

Biological Knowledge Discovery Handbook

View full text Add to dashboard Cite

INTRODUCTIONGenome-scale metabolic reconstruction is the ultimate goal of system biologists for finding out the genotype-phenotype relationship. For achieving this purpose, we need enough knowledge about the metabolic pathways and genome annotation. During the present study, we aim to describe the foundational concepts, central to omics data, model formulation, history, method, and applications of metabolic network reconstruction as well as some related sources. In Section 45.2, we describe omics data and high-throughput technologies for gaining these data. In section 45.3, we present different ways for metabolic network modeling. In section 45.4, we summarize the history of genome-scale modeling as one of the most important methods for metabolic network modeling. In sections 45.5 and 45.6, we elucidate how genome-scale metabolic models could be generated and what their applications are. Finally, in section 45.7, we review biochemical pathways and genome annotation databases. OMICs ERAInnovative omics technologies such as genomics, transcriptomics, proteomics, and metabolomics facilitate a strategy toward the simultaneous analysis of the large number of genes, transcripts, proteins, and metabolites. Therefore, a huge volume of data has generated about the make-up of cells and their behavior at various cellular levels and different environmental conditions, which enable us to reconstruct genome-scale biomolecular networks (e.g., transcriptional regulatory networks, interactomic networks, and metabolic networks)Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data, First Edition. Edited by Mourad Elloumi and Albert Y. Zomaya.

show abstract

Genome-scale constraint-based modeling of Geobacter metallireducens

Cited by 71 publications

References 38 publications

Iterative Multi Level Calibration of Metabolic Networks

Iterative Multi Level Calibration of Metabolic Networks

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

Integration of Metabolic Knowledge for Genome‐Scale Metabolic Reconstruction

Contact Info

Product

Resources

About