An unconventional uptake rate objective function approach enhances applicability of genome-scale models for mammalian cells

AbstractGenome-scale models (GEMs) rely on a biomass objective function (BOF) to predict phenotype from genotype. Here we present BOFdat, a Python package that offers functions to generate biomass objective function stoichiometric coefficients (BOFsc) from macromolecular cell composition and relative abundances of macromolecules obtained from omic datasets. Growth-associated and non-growth associated maintenance (GAM and NGAM) costs can also be calculated by BOFdat.BOFdat is freely available on the Python Package Index (pip install BOFdat). The source code and an example usage (Jupyter Notebook and example files) are available on GitHub (https://github.com/jclachance/BOFdat). The documentation and API are available through ReadTheDocs (https://bofdat.readthedocs.io)[email protected], [email protected], [email protected]

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Systems metabolic engineering: Genome-scale models and beyond

Biotechnology Journal ◽

10.1002/biot.200900247 ◽

2010 ◽

Vol 5 (7) ◽

pp. 647-659 ◽

Cited By ~ 96

Author(s):

John Blazeck ◽

Hal Alper

Keyword(s):

Metabolic Engineering ◽

Scale Models ◽

Systems Metabolic Engineering ◽

Genome Scale

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Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

10.1101/2020.03.23.003236 ◽

2020 ◽

Cited By ~ 1

Author(s):

Colton J. Lloyd ◽

Jonathan Monk ◽

Laurence Yang ◽

Ali Ebrahim ◽

Bernhard O. Palsson

Keyword(s):

Amino Acids ◽

Growth Conditions ◽

Anaerobic Growth ◽

Metabolic Phenotype ◽

E Coli ◽

Scale Models ◽

Protein Components ◽

K 12 ◽

Genome Scale ◽

Prosthetic Groups

AbstractSustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

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Deciphering the metabolic capabilities of Bifidobacteria using genome-scale metabolic models

Scientific Reports ◽

10.1038/s41598-019-54696-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

N. T. Devika ◽

Karthik Raman

Keyword(s):

Short Chain Fatty Acids ◽

Acetate Production ◽

Scale Models ◽

Powerful Approach ◽

Metabolic Models ◽

Commercial Applications ◽

Different Strains ◽

Genome Scale ◽

Modelling Approach

AbstractBifidobacteria, the initial colonisers of breastfed infant guts, are considered as the key commensals that promote a healthy gastrointestinal tract. However, little is known about the key metabolic differences between different strains of these bifidobacteria, and consequently, their suitability for their varied commercial applications. In this context, the present study applies a constraint-based modelling approach to differentiate between 36 important bifidobacterial strains, enhancing their genome-scale metabolic models obtained from the AGORA (Assembly of Gut Organisms through Reconstruction and Analysis) resource. By studying various growth and metabolic capabilities in these enhanced genome-scale models across 30 different nutrient environments, we classified the bifidobacteria into three specific groups. We also studied the ability of the different strains to produce short-chain fatty acids, finding that acetate production is niche- and strain-specific, unlike lactate. Further, we captured the role of critical enzymes from the bifid shunt pathway, which was found to be essential for a subset of bifidobacterial strains. Our findings underline the significance of analysing metabolic capabilities as a powerful approach to explore distinct properties of the gut microbiome. Overall, our study presents several insights into the nutritional lifestyles of bifidobacteria and could potentially be leveraged to design species/strain-specific probiotics or prebiotics.

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Global sensitivity analysis of parameter uncertainty in landscape evolution models

Geoscientific Model Development ◽

10.5194/gmd-11-4873-2018 ◽

2018 ◽

Vol 11 (12) ◽

pp. 4873-4888 ◽

Cited By ~ 11

Author(s):

Christopher J. Skinner ◽

Tom J. Coulthard ◽

Wolfgang Schwanghart ◽

Marco J. Van De Wiel ◽

Greg Hancock

Keyword(s):

Sensitivity Analysis ◽

Objective Function ◽

Landscape Evolution ◽

Relative Sensitivity ◽

Function Approach ◽

Sensitivity Analyses ◽

Model Parameters ◽

Model Function ◽

Sediment Yields ◽

Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

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BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

Nucleic Acids Research ◽

10.1093/nar/gkz1054 ◽

2019 ◽

Cited By ~ 7

Author(s):

Charles J Norsigian ◽

Neha Pusarla ◽

John Luke McConn ◽

James T Yurkovich ◽

Andreas Dräger ◽

...

Keyword(s):

Phylogenetic Tree ◽

Knowledge Base ◽

The Past ◽

Scale Models ◽

Metabolic Models ◽

New Community ◽

Genome Annotations ◽

Genome Scale ◽

High Quality Genome ◽

Strain Genome

Abstract The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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