pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis

Salvy, Pierre; Fengos, Georgios; Ataman, Meriç; Pathier, Thomas; Soh, Keng Cher; Hatzimanikatis, Vassily

doi:10.1093/bioinformatics/bty499

Cited by 83 publications

(157 citation statements)

References 22 publications

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“…The lack of a methodology that allows integrating fluxomics, thermodynamic constraints, metabolomics, and transcriptomics simultaneously into a GEM motivated us to develop TEX-FBA. The TEX-FBA formulation is based on the iMAT methodology [32,33] and extends it with the integration of thermodynamic constraints [25,26,50] and a new definition of flux ranges consistent with gene expression. The implementation of TEX-FBA follows a sequence of eight steps:…”

Section: The Tex-fba Formulation and Maximum Consistency Score (Mcs)mentioning

confidence: 99%

“…We solved FBA [22] with the version of the COBRA Toolbox v2.0 [56] included in the matTFA toolbox [50]. We performed TFA [25,26] with the matTFA toolbox [50]. We used MATLAB (R2016a and R2017a) and CPLEX (ILOG IBM v12.51 and v12.71) as the linear solver.…”

Section: Software Usage and Settingsmentioning

confidence: 99%

“…The tutorials indicate how to reproduce the results and figures for this paper. TEX-FBA requires the matTFA toolbox [50] for TFA.…”

Section: Software Availabilitymentioning

confidence: 99%

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

Pandey¹,

Gardiol²,

Chiappino-Pepe³

et al. 2019

Preprint

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A 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 of Escherichia coli by 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.

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Section: The Tex-fba Formulation and Maximum Consistency Score (Mcs)mentioning

confidence: 99%

Section: Software Usage and Settingsmentioning

confidence: 99%

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

Pandey¹,

Gardiol²,

Chiappino-Pepe³

et al. 2019

Preprint

Self Cite

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“…Within the thermodynamic formulation [17] of the stoichiometric models D1, D2 and D3, we integrated fluxomics and metabolomics data for aerobically grown E.coli (S1 Table). Several assumptions were made on reaction directionalities, based on literature [14,[18][19][20][21], to further constrain the models (Methods).…”

Section: Thermodynamic-based Variability Analysismentioning

confidence: 99%

The effects of model complexity and size on metabolic flux distribution and control. Case study inE. coli

Hameri

Fengos

Hatzimanikatis

2019

Preprint

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Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. It has not been studied previously how network complexity affects the Metabolic Sensitivity Coefficients (MSCs) of large-scale kinetic models build around consistently reduced models. We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction (GEM) systematically to build three stoichiometric models of variable size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are modular. We propose a method for scaling up the flux profile and the concentration vector reference steadystates from the smallest model to the larger ones, whilst preserving maximum equivalency.Populations of non-linear kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their MSCs were computed. The analysis of the populations of MSCs for the reduced models evidences that metabolic engineering strategies -independent of network complexity -can be designed using our proposed workflow. These findings suggest that we can successfully construct reduced kinetic models from a GEM, without losing information relevant to the scope of the study. Our proposed workflow can serve as an approach for testing the suitability of a model for answering certain study-specific questions. Author SummaryKinetic models of metabolism are very useful tools for metabolic engineering. However, they are generated ad hoc because, to our knowledge, there exists no standardized procedure for constructing kinetic models of metabolism. We sought to investigate systematically the effect of model complexity and size on sensitivity characteristics. Hence, we used the redGEM and the lumpGEM algorithms to build the backbone of three consistently and modularly reduced stoichiometric models from the iJO1366 genome-scale model for aerobically grown E.coli. These three models were of increasing complexity in terms of network topology and served as basis for building populations of kinetic models. We proposed for the first time a way for scaling up steadystates of the metabolic fluxes and the metabolite concentrations from one kinetic model to another and developed a workflow for fixing kinetic parameters between the models in order to preserve equivalency. We performed metabolic control analysis (MCA) around the populations of kinetic models and used their MCA control coefficients as measurable outputs to compare the three models. We demonstrated that we can systematically reduce genome-scale models to construct kinetic models of different complexity levels for a phenotype...

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“…pyTFA (a Python package for thermodynamics‐based flux analysis [TFA]) and matTFA (a MATLAB toolbox) help include TFA in metabolic models to enable the integration of quantitative metabolomics data to study their effects on the net‐flux directionality of reactions in the network . Using both a reduced and a genome‐scale model of Escherichia coli , the authors demonstrated that TFA and data integration through TFA reduced the feasible flux space with respect to flux balance analyses (FBA).…”

Section: Miscellaneous Tools Of Interestmentioning

confidence: 99%

Tools and resources for metabolomics research community: A 2017–2018 update

Misra¹,

Mohapatra

2018

Electrophoresis

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The scale at which MS‐ and NMR‐based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences—ranging from biomedical to agricultural—is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017–2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

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pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis

Cited by 83 publications

References 22 publications

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

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

The effects of model complexity and size on metabolic flux distribution and control. Case study inE. coli

Tools and resources for metabolomics research community: A 2017–2018 update

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