Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

Abstract

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using largescale kinetic models, Metabolic Engineering, http://dx.doi.org/10. 1016/j.ymben.2016.01.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Entities) to analyze the physiology of recombinant E. coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation.Independent earlier experiments on this strain confirmed that the computationally 3 obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and effectiveness of ORACLE for the accelerated design of microbial cell factories.

lumpGEM: Systematic generation of subnetworks and elementally balanced lumped reactions for the biosynthesis of target metabolites

“…By exploiting this property of lumpGEM, we built a reduced model that has an ad hoc defined core with a biomass yield very close to its parent GEM model. With a systematic approach to define the core [48], we can generate representative reduced models that are consistent with their GEM for different studies, such as kinetic modelling [49–51], in where it is crucial to base the analysis on models that do not sacrifice stoichiometric, thermodynamic and physiological constraints.…”

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

“…The resultant model, consisting of a series of classification rules built by a decision tree algorithm, found that only 27 out of the 153 potential enzymes had narrow ranges of feasible kinetic parameters. Presumed to be critical to product formation, a population of 200 000 + ORACLE models was generated in a follow‐up study to examine the effects of these 27 enzymes on predicted BDO production . While not tested in vivo in this study, the enzymes the model population suggested to be the most critical influencers of BDO production were consistent with what was seen in previous experimental studies.…”

A design–build–test cycle using modeling and experiments reveals interdependencies between upper glycolysis and xylose uptake in recombinant S. cerevisiae and improves predictive capabilities of large-scale kinetic models

“…2; Additional file 2). This implies that for all the enzymes in the network their control over fluxes and concentrations would depend on their kinetic properties and saturation state [33, 35, 38]. We assumed that the reaction catalyzed by malic enzyme (MAE) was operating in the direction of decarboxylation because it has been observed that pyruvate carboxylase-negative strains of S. cerevisiae are not capable of growing on glucose indicating that MAE cannot assume the anaplerotic role of pyruvate carboxylase [39].…”