iML1515, a knowledgebase that computes Escherichia coli traits

Monk, Jonathan M.; Lloyd, Colton J.; Brunk, Elizabeth; Mih, Nathan; Sastry, Anand V.; King, Zachary A.; Takeuchi, Rikiya; Nomura, Wataru; Zhang, Zhen; Mori, Hirotada; Feist, Adam M.; Palsson, Bernhard Ø.

doi:10.1038/nbt.3956

Cited by 434 publications

(606 citation statements)

References 30 publications

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“…Validation of different turnover number vectors in the MOMENT model was conducted as described in Heckmann et al 16 . The genome-scale metabolic model iML1515 45 was used in the R 46 packages sybil 47 and sybilccFBA 48 to construct linear programming problems that were solved in IBM CPLEX version 12.7.…”

Section: Moment Modelingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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Enzyme turnover numbers (kcats) are essential for a quantitative understanding of cells. Because kcats are traditionally measured in low-throughput assays, they are often noisy, nonphysiological, inconsistent, and labor-intensive to obtain. We use a data-driven approach to estimate in vivo kcats using metabolic specialist E. coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo kcats are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo kcats predict unseen proteomics data with much higher precision than in vitro kcats. The results demonstrate that in vivo kcats can solve the problem of noisy and inconsistent parameterizations of cellular models.

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Section: Moment Modelingmentioning

confidence: 99%

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Heckmann

Campeau

Lloyd

et al. 2019

Preprint

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“…Choi et al, T 2016;S. Choi et al, 2016;Park et al, 2011Park et al, , 2007Yim et al, 2011) owing to the rich toolset available for gene and expression modification (Datsenko and Wanner, 2000;Jiang et al, 2015;Ronda et al, 2016;Wang et al, 2009) as well as accumulated historical knowledge base of biochemical understanding (Guo et al, 2013;Kanehisa et al, 2017;Keseler et al, 2013;Monk et al, 2017). Seven commonly used strains in biotechnology applications include K-12 MG1655, K-12 W3110, K-12 DH5a, BL21, C, Crooks, and W. It has been shown that particular strains are better suited for different applications (Monk et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

McCloskey

Schrübbers

et al. 2018

Metabolic Engineering

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A B S T R A C TFast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.

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“…However, “next‐generation GEMs” further need to be developed to expand the knowledge of a biological network beyond metabolism . GEMs of some well‐studied microorganisms have already seen such improvements, such as incorporating protein structure data in Escherichia coli GEM iML1515 and enzyme kinetics and abundances in Saccharomyces cerevisiae GEM Yeast7 . The same upgrade can be made with GEMs of various actinomycetes by incorporating kinetic and regulatory information in the GEM.…”

Section: Resultsmentioning

confidence: 99%

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

Mohite

Weber

Kim

et al. 2018

Biotechnology Journal

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Systems biology approaches are increasingly applied to explore the potential of actinomycetes for the discovery and optimal production of antibiotics. In particular, genome-scale metabolic models (GEMs) of various actinomycetes are reconstructed at a faster rate in recent years, which has opened avenues to study interaction between primary and secondary metabolism at systems level, and to predict gene manipulation targets for overproduction of important antibiotics. Here, the status of actinomycetes' GEMs and their applications for designing antibiotics-overproducing strains are presented. Despite advances in the practice of GEM reconstruction, actinomycetes' GEMs still remain incomplete in describing a full set of biosynthetic pathways of secondary metabolites. As to the GEM-based strategies, various simulation methods are deployed to better describe secondary metabolism by introducing changes in constraints and/or objective function as well as by using omics data. Gene manipulation targeting algorithms developed for metabolic engineering of model organisms have also been actively applied to actinomycetes for the antibiotics production. Further consideration of computational resources dedicated to secondary metabolites in addition with automated GEM reconstruction tools will further upgrade GEMs of actinomycetes for antibiotics discovery and development.

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iML1515, a knowledgebase that computes Escherichia coli traits

Cited by 434 publications

References 30 publications

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli

Genome‐Scale Metabolic Reconstruction of Actinomycetes for Antibiotics Production

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