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The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

View Article: PubMed Central - PubMed

ABSTRACT

Background: Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results: The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S-13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions: jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

No MeSH data available.


FluxModels module class diagram (part I). The FluxModels module contains classes for the different types of models used for each flux analysis type: FBA, 13C MFA, 2S-13C MFA, ELVA, etc. Arrows indicate derived classes
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Fig3: FluxModels module class diagram (part I). The FluxModels module contains classes for the different types of models used for each flux analysis type: FBA, 13C MFA, 2S-13C MFA, ELVA, etc. Arrows indicate derived classes

Mentions: jQMM is implemented in Python 2.7, and we have aimed to provide a code base that is as modular as possible, in order to facilitate understanding and reusability. The code is divided into a set of modules that incorporate classes and methods which will typically be used together. Figure 1 details the different modules and how they interact with each other for flux-based analysis. The following subsections provide a brief explanation of the purpose of each module and Figs. 2, 3 and 4 (and Additional file 1: Figures S1-S3) provide graphical depictions of each module together with their main classes and methods. Tutorials are provided for each module, along with unit tests.Fig. 1


The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism
FluxModels module class diagram (part I). The FluxModels module contains classes for the different types of models used for each flux analysis type: FBA, 13C MFA, 2S-13C MFA, ELVA, etc. Arrows indicate derived classes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5382524&req=5

Fig3: FluxModels module class diagram (part I). The FluxModels module contains classes for the different types of models used for each flux analysis type: FBA, 13C MFA, 2S-13C MFA, ELVA, etc. Arrows indicate derived classes
Mentions: jQMM is implemented in Python 2.7, and we have aimed to provide a code base that is as modular as possible, in order to facilitate understanding and reusability. The code is divided into a set of modules that incorporate classes and methods which will typically be used together. Figure 1 details the different modules and how they interact with each other for flux-based analysis. The following subsections provide a brief explanation of the purpose of each module and Figs. 2, 3 and 4 (and Additional file 1: Figures S1-S3) provide graphical depictions of each module together with their main classes and methods. Tutorials are provided for each module, along with unit tests.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed.

Results: The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S-13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user’s specific needs.

Conclusions: jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will attract additions from the community and grow with the rapidly changing field of metabolic engineering.

Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1615-y) contains supplementary material, which is available to authorized users.

No MeSH data available.