scholarly journals COBRAme: A computational framework for genome-scale models of metabolism and gene expression

2018 ◽  
Vol 14 (7) ◽  
pp. e1006302 ◽  
Author(s):  
Colton J. Lloyd ◽  
Ali Ebrahim ◽  
Laurence Yang ◽  
Zachary A. King ◽  
Edward Catoiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Computational Framework ◽  
Scale Models ◽  
Genome Scale

scholarly journals TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models

2019 ◽  
Author(s):  
Vikash Pandey ◽  
Daniel Hernandez Gardiol ◽  
Anush Chiappino-Pepe ◽  
Vassily Hatzimanikatis
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Solution Space ◽  
Metabolic Model ◽  
Scale Models ◽  
Different Types ◽  
Transcriptomics Data ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Genome Scale

AbstractA large number of genome-scale models of cellular metabolism are available for various organisms. These models include all known metabolic reactions based on the genome annotation. However, the reactions that are active are dependent on the cellular metabolic function or environmental condition. Constraint-based methods that integrate condition-specific transcriptomics data into models have been used extensively to investigate condition-specific metabolism. Here, we present a method (TEX-FBA) for modeling condition-specific metabolism that combines transcriptomics and reaction thermodynamics data to generate a thermodynamically-feasible condition-specific metabolic model. TEX-FBA is an extension of thermodynamic-based flux balance analysis (TFA), which allows the simultaneous integration of different stages of experimental data (e.g., absolute gene expression, metabolite concentrations, thermodynamic data, and fluxomics) and the identification of alternative metabolic states that maximize consistency between gene expression levels and condition-specific reaction fluxes. We applied TEX-FBA to a genome-scale metabolic model ofEscherichia coliby integrating available condition-specific experimental data and found a marked reduction in the flux solution space. Our analysis revealed a marked correlation between actual gene expression profile and experimental flux measurements compared to the one obtained from a randomly generated gene expression profile. We identified additional essential reactions from the membrane lipid and folate metabolism when we integrated transcriptomics data of the given condition on the top of metabolomics and thermodynamics data. These results show TEX-FBA is a promising new approach to study condition-specific metabolism when different types of experimental data are available.Author summaryCells utilize nutrients via biochemical reactions that are controlled by enzymes and synthesize required compounds for their survival and growth. Genome-scale models of metabolism representing these complex reaction networks have been reconstructed for a wide variety of organisms ranging from bacteria to human cells. These models comprise all possible biochemical reactions in a cell, but cells choose only a subset of reactions for their immediate needs and functions. Usually, these models allow for a large flux solution space and one can integrate experimental data in order to reduce it and potentially predict the physiology for a specific condition. We developed a method for integrating different types of omics data, such as fluxomics, transcriptomics, metabolomics into genome-scale metabolic models that reduces the flux solution space. Using gene expression data, the algorithm maximizes the consistency between the predicted and experimental flux for the reactions and predicts biologically relevant flux ranges for the remaining reactions in the network. This method is useful for determining fluxes of metabolic reactions with reduced uncertainty and suitable for performing context- and condition-specific analysis in metabolic models using different types of experimental data.

scholarly journals Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction

2013 ◽  
Vol 9 (1) ◽  
pp. 693 ◽  
Author(s):  
Edward J O'Brien ◽  
Joshua A Lerman ◽  
Roger L Chang ◽  
Daniel R Hyduke ◽  
Bernhard Ø Palsson
Keyword(s):  
Gene Expression ◽  
Phenotype Prediction ◽  
Scale Models ◽  
Growth Phenotype ◽  
Genome Scale

scholarly journals A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation

2020 ◽  
Vol 48 (5) ◽  
pp. 1889-1903
Author(s):  
Fernando Cruz ◽  
José P. Faria ◽  
Miguel Rocha ◽  
Isabel Rocha ◽  
Oscar Dias
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Transcriptional Regulatory Networks ◽  
Flux Balance ◽  
Transcriptional Regulatory ◽  
Scale Models ◽  
Current Survey ◽  
Balance Analysis ◽  
Or Gene ◽  
Genome Scale

The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.

scholarly journals COBRAme: A Computational Framework for Genome-Scale Models of Metabolism and Gene Expression

2017 ◽  
Author(s):  
Colton J. Lloyd ◽  
Ali Ebrahim ◽  
Laurence Yang ◽  
Zachary King ◽  
Edward Catoiu ◽  
...  
Keyword(s):  
Marked Improvement ◽  
Thermotoga Maritima ◽  
Cellular Growth ◽  
Software Framework ◽  
Computational Framework ◽  
E Coli ◽  
Growing Cell ◽  
Scale Models ◽  
Model Size ◽  
Genome Scale

AbstractGenome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable new and exciting insights that are fundamental to understanding the basis of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives virtually identical solutions to previous E. coli ME-models while using ¼ the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be reconstructed and edited most efficiently using the software.

scholarly journals Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

2020 ◽  
Author(s):  
Colton J. Lloyd ◽  
Jonathan Monk ◽  
Laurence Yang ◽  
Ali Ebrahim ◽  
Bernhard O. Palsson
Keyword(s):  
Amino Acids ◽  
Growth Conditions ◽  
Anaerobic Growth ◽  
Metabolic Phenotype ◽  
E Coli ◽  
Scale Models ◽  
Protein Components ◽  
K 12 ◽  
Genome Scale ◽  
Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

scholarly journals Identifying functional metabolic shifts in heart failure with the integration of omics data and a cardiomyocyte-specific, genome-scale model

2020 ◽  
Author(s):  
Bonnie V. Dougherty ◽  
Kristopher D. Rawls ◽  
Glynis L. Kolling ◽  
Kalyan C. Vinnakota ◽  
Anders Wallqvist ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Specific Model ◽  
Metabolic Phenotype ◽  
Scale Model ◽  
Metabolic Markers ◽  
Metabolic Functions ◽  
Carbon Substrates ◽  
End Stage ◽  
Genome Scale

SummaryThe heart is a metabolic omnivore, known to consume many different carbon substrates in order to maintain function. In diseased states, the heart’s metabolism can shift between different carbon substrates; however, there is some disagreement in the field as to the metabolic shifts seen in end-stage heart failure and whether all heart failure converges to a common metabolic phenotype. Here, we present a new, validated cardiomyocyte-specific GEnome-scale metabolic Network REconstruction (GENRE), iCardio, and use the model to identify common shifts in metabolic functions across heart failure omics datasets. We demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying Tasks Inferred from Differential Expression (TIDEs) which represent metabolic functions associated with changes in gene expression. We identify decreased NO and Neu5Ac synthesis as common metabolic markers of heart failure across datasets. Further, we highlight the differences in metabolic functions seen across studies, further highlighting the complexity of heart failure. The methods presented for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissue and diseases of interest.

scholarly journals Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
N. T. Devika ◽  
Karthik Raman
Keyword(s):  
Short Chain Fatty Acids ◽  
Acetate Production ◽  
Scale Models ◽  
Powerful Approach ◽  
Metabolic Models ◽  
Commercial Applications ◽  
Different Strains ◽  
Genome Scale ◽  
Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

scholarly journals BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

2019 ◽  
Author(s):  
Charles J Norsigian ◽  
Neha Pusarla ◽  
John Luke McConn ◽  
James T Yurkovich ◽  
Andreas Dräger ◽  
...  
Keyword(s):  
Phylogenetic Tree ◽  
Knowledge Base ◽  
The Past ◽  
Scale Models ◽  
Metabolic Models ◽  
New Community ◽  
Genome Annotations ◽  
Genome Scale ◽  
High Quality Genome ◽  
Strain Genome

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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