scholarly journals Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

Science ◽  
2013 ◽  
Vol 340 (6137) ◽  
pp. 1220-1223 ◽  
Author(s):  
Roger L. Chang ◽  
Kathleen Andrews ◽  
Donghyuk Kim ◽  
Zhanwen Li ◽  
Adam Godzik ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Systems Biology ◽  
Structural Information ◽  
Protein Structures ◽  
Limiting Factors ◽  
Scale Model ◽  
Structural Systems ◽  
A Genome ◽  
Structural Systems Biology ◽  
Genome Scale

Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.

scholarly journals ssbio: a Python framework for structural systems biology

2018 ◽  
Vol 34 (12) ◽  
pp. 2155-2157 ◽  
Author(s):  
Nathan Mih ◽  
Elizabeth Brunk ◽  
Ke Chen ◽  
Edward Catoiu ◽  
Anand Sastry ◽  
...  
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Third Party ◽  
Supplementary Information ◽  
Scale Models ◽  
Protein Properties ◽  
Scale Network ◽  
Structural Systems Biology ◽  
Genome Scale

Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ssbio: A Python Framework for Structural Systems Biology

2017 ◽  
Author(s):  
Nathan Mih ◽  
Elizabeth Brunk ◽  
Ke Chen ◽  
Edward Catoiu ◽  
Anand Sastry ◽  
...  
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Third Party ◽  
Supplementary Information ◽  
Link Type ◽  
Protein Properties ◽  
Scale Network ◽  
Structural Systems Biology ◽  
Genome Scale

AbstractSummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.Availability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/[email protected] InformationSupplementary data are available at Bioinformatics online.

scholarly journals EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Oliver Hädicke ◽  
Steffen Klamt
Keyword(s):  
Escherichia Coli ◽  
Reference Model ◽  
Model Organism ◽  
Solution Space ◽  
Scale Model ◽  
Reference Network ◽  
Central Metabolism ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

Abstract Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

scholarly journals Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Anjani Ragothaman ◽  
Sairam Chowdary Boddu ◽  
Nayong Kim ◽  
Wei Feinstein ◽  
Michal Brylinski ◽  
...  
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Structural Bioinformatics ◽  
Genome Wide ◽  
Viable Solution ◽  
A Genome ◽  
Structural Systems Biology ◽  
Computational Resources ◽  
Genome Scale ◽  
Level Parallelism

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

scholarly journals Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli

2006 ◽  
Vol 188 (23) ◽  
pp. 8259-8271 ◽  
Author(s):  
Andrew R. Joyce ◽  
Jennifer L. Reed ◽  
Aprilfawn White ◽  
Robert Edwards ◽  
Andrei Osterman ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Minimal Medium ◽  
Essential Genes ◽  
Integrative Model ◽  
Scale Model ◽  
Data Sets ◽  
Data Set ◽  
E Coli ◽  
A Genome ◽  
Genome Scale

ABSTRACT Genome-wide gene essentiality data sets are becoming available for Escherichia coli, but these data sets have yet to be analyzed in the context of a genome scale model. Here, we present an integrative model-driven analysis of the Keio E. coli mutant collection screened in this study on glycerol-supplemented minimal medium. Out of 3,888 single-deletion mutants tested, 119 mutants were unable to grow on glycerol minimal medium. These conditionally essential genes were then evaluated using a genome scale metabolic and transcriptional-regulatory model of E. coli, and it was found that the model made the correct prediction in ∼91% of the cases. The discrepancies between model predictions and experimental results were analyzed in detail to indicate where model improvements could be made or where the current literature lacks an explanation for the observed phenotypes. The identified set of essential genes and their model-based analysis indicates that our current understanding of the roles these essential genes play is relatively clear and complete. Furthermore, by analyzing the data set in terms of metabolic subsystems across multiple genomes, we can project which metabolic pathways are likely to play equally important roles in other organisms. Overall, this work establishes a paradigm that will drive model enhancement while simultaneously generating hypotheses that will ultimately lead to a better understanding of the organism.

scholarly journals Genome-Scale Metabolic Network Analysis of the Opportunistic Pathogen Pseudomonas aeruginosa PAO1

2008 ◽  
Vol 190 (8) ◽  
pp. 2790-2803 ◽  
Author(s):  
Matthew A. Oberhardt ◽  
Jacek Puchałka ◽  
Kimberly E. Fryer ◽  
Vítor A. P. Martins dos Santos ◽  
Jason A. Papin
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Metabolic Network ◽  
Opportunistic Pathogen ◽  
Scale Model ◽  
Modeling Framework ◽  
Major Life ◽  
Metabolic Versatility ◽  
A Genome ◽  
Scale Properties ◽  
Genome Scale

ABSTRACT Pseudomonas aeruginosa is a major life-threatening opportunistic pathogen that commonly infects immunocompromised patients. This bacterium owes its success as a pathogen largely to its metabolic versatility and flexibility. A thorough understanding of P. aeruginosa's metabolism is thus pivotal for the design of effective intervention strategies. Here we aim to provide, through systems analysis, a basis for the characterization of the genome-scale properties of this pathogen's versatile metabolic network. To this end, we reconstructed a genome-scale metabolic network of Pseudomonas aeruginosa PAO1. This reconstruction accounts for 1,056 genes (19% of the genome), 1,030 proteins, and 883 reactions. Flux balance analysis was used to identify key features of P. aeruginosa metabolism, such as growth yield, under defined conditions and with defined knowledge gaps within the network. BIOLOG substrate oxidation data were used in model expansion, and a genome-scale transposon knockout set was compared against in silico knockout predictions to validate the model. Ultimately, this genome-scale model provides a basic modeling framework with which to explore the metabolism of P. aeruginosa in the context of its environmental and genetic constraints, thereby contributing to a more thorough understanding of the genotype-phenotype relationships in this resourceful and dangerous pathogen.

scholarly journals Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation

Microbiology ◽  
2014 ◽  
Vol 160 (6) ◽  
pp. 1252-1266 ◽  
Author(s):  
Hassan B. Hartman ◽  
David A. Fell ◽  
Sergio Rossell ◽  
Peter Ruhdal Jensen ◽  
Martin J. Woodward ◽  
...  
Keyword(s):  
Salmonella Enterica ◽  
Energy Demand ◽  
Drug Targets ◽  
Minimal Medium ◽  
Model Organism ◽  
Metabolic Model ◽  
Scale Model ◽  
Glucose Minimal Medium ◽  
A Genome ◽  
Genome Scale

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

scholarly journals A Genome-Scale Model of Shewanella piezotolerans Simulates Mechanisms of Metabolic Diversity and Energy Conservation

mSystems ◽  
2017 ◽  
Vol 2 (2) ◽  
Author(s):  
Keith Dufault-Thompson ◽  
Huahua Jian ◽  
Ruixue Cheng ◽  
Jiefu Li ◽  
Fengping Wang ◽  
...  
Keyword(s):  
Energy Conservation ◽  
Deep Sea ◽  
Carbon Sources ◽  
Scale Model ◽  
Anaerobic Conditions ◽  
A Genome ◽  
Group 2 ◽  
Genome Scale ◽  
Genome Scale Model ◽  
Group 1

ABSTRACT The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms. Shewanella piezotolerans strain WP3 belongs to the group 1 branch of the Shewanella genus and is a piezotolerant and psychrotolerant species isolated from the deep sea. In this study, a genome-scale model was constructed for WP3 using a combination of genome annotation, ortholog mapping, and physiological verification. The metabolic reconstruction contained 806 genes, 653 metabolites, and 922 reactions, including central metabolic functions that represented nonhomologous replacements between the group 1 and group 2 Shewanella species. Metabolic simulations with the WP3 model demonstrated consistency with existing knowledge about the physiology of the organism. A comparison of model simulations with experimental measurements verified the predicted growth profiles under increasing concentrations of carbon sources. The WP3 model was applied to study mechanisms of anaerobic respiration through investigating energy conservation, redox balancing, and the generation of proton motive force. Despite being an obligate respiratory organism, WP3 was predicted to use substrate-level phosphorylation as the primary source of energy conservation under anaerobic conditions, a trait previously identified in other Shewanella species. Further investigation of the ATP synthase activity revealed a positive correlation between the availability of reducing equivalents in the cell and the directionality of the ATP synthase reaction flux. Comparison of the WP3 model with an existing model of a group 2 species, Shewanella oneidensis MR-1, revealed that the WP3 model demonstrated greater flexibility in ATP production under the anaerobic conditions. Such flexibility could be advantageous to WP3 for its adaptation to fluctuating availability of organic carbon sources in the deep sea. IMPORTANCE The well-studied nature of the metabolic diversity of Shewanella bacteria makes species from this genus a promising platform for investigating the evolution of carbon metabolism and energy conservation. The Shewanella phylogeny is diverged into two major branches, referred to as group 1 and group 2. While the genotype-phenotype connections of group 2 species have been extensively studied with metabolic modeling, a genome-scale model has been missing for the group 1 species. The metabolic reconstruction of Shewanella piezotolerans strain WP3 represented the first model for Shewanella group 1 and the first model among piezotolerant and psychrotolerant deep-sea bacteria. The model brought insights into the mechanisms of energy conservation in WP3 under anaerobic conditions and highlighted its metabolic flexibility in using diverse carbon sources. Overall, the model opens up new opportunities for investigating energy conservation and metabolic adaptation, and it provides a prototype for systems-level modeling of other deep-sea microorganisms.

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