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

Abstract

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

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

“…A detailed description of the old Limit Flux to Core algorithm be found in the pseudo code Algorithms 1 and 2. This algorithm was previously published as part of the 2S- C MFA method [ 23 ], and is implemented in the jQMM software tool [ 31 ]. The set of “boundary reactions” which can potentially alter C labeling of core metabolites is determined for both Algorithms 2 and 3 using Algorithm 1.…”

“…This requires, for any given core, first specifying a set of “currency metabolites” which participate in core reactions, but (based on known atom transitions) cannot contribute carbon to any of the simulated metabolites in a 2S- C MFA or C MFA model (e.g., ATP, NADH). Our software includes a pre-determined list of suggested “currency metabolites”; however, most popular software tools for 2S- C MFA or C MFA, including the jQMM library [ 31 ], can compute these directly from a set of core reaction atom transitions. Reactions which feed only currency metabolites into core metabolism are excluded from the set of boundary reactions for subsequent flux minimization.…”

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics

“…Mass spectrometry-based metabolomic data are already being used to assist in characterizing metabolic models by comparing the metabolites produced in cell cultures to expected biomass output via flux balance analysis (FBA) [15][16][17][18][19]. Although there are some excellent bioinformatic tools available for integrating metabolomic data into flux-balance-generated metabolic models [15], they require a significant amount of preliminary experimental work using multiple targeted quantitative metabolomics assays, often requiring use of expensive isotopically labeled reagents.…”

Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering

“…In this chapter we will show how to use metabolomic data obtained from 13 C labeling experiments to generate actionable items to increase acetate production in E. coli. We will use the JBEI Quantitative Metabolic Modeling library (jQMM) [4] to calculate cellular fluxes and make predictions. The jQMM library is currently capable of measuring and predicting internal metabolic fluxes using three different techniques: 13 C Metabolic Flux Analysis ( 13 C MFA) [5], Flux Balance Analysis (FBA) [6], and two-scale 13 C Metabolic Flux Analysis (2S-13 C MFA) [1].…”