Understanding the host-microbe interactions using metabolic modeling

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using in silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce to experimental biologists the possible applications of flux balance analysis in the host-microbiota interaction field and discusses its potential use to improve human health.

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Understanding the host-microbe interactions using metabolic modeling

10.1101/2020.06.12.147918 ◽

2020 ◽

Author(s):

Jack Jansma ◽

Sahar El Aidy

Keyword(s):

Human Health ◽

Flux Balance Analysis ◽

Bacterial Species ◽

Flux Balance ◽

Balance Analysis ◽

Microbe Interactions ◽

Host Microbe Interactions ◽

Genome Scale ◽

Enormous Number ◽

Potential Use

AbstractThe human gut harbors an enormous number of symbiotic microbes, which is vital for human health. However, interactions within the complex microbiota community and between the microbiota and its host are challenging to elucidate, limiting development in the treatment for a variety of diseases associated with microbiota dysbiosis. Using In silico simulation methods based on flux balance analysis, those interactions can be better investigated. Flux balance analysis uses an annotated genome-scale reconstruction of a metabolic network to determine the distribution of metabolic fluxes that represent the complete metabolism of a bacterium in a certain metabolic environment such as the gut. Simulation of a set of bacterial species in a shared metabolic environment can enable the study of the effect of numerous perturbations, such as dietary changes or addition of a probiotic species in a personalized manner. This review aims to introduce these applications of flux balance analysis to experimental biologists and discusses its potential use to improve human health.

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Why does yeast ferment? A flux balance analysis study

Biochemical Society Transactions ◽

10.1042/bst0381225 ◽

2010 ◽

Vol 38 (5) ◽

pp. 1225-1229 ◽

Cited By ~ 23

Author(s):

Evangelos Simeonidis ◽

Ettore Murabito ◽

Kieran Smallbone ◽

Hans V. Westerhoff

Keyword(s):

Flux Balance Analysis ◽

Energy Cost ◽

Yeast Fermentation ◽

Minimal Amount ◽

Aerobic Growth ◽

Analysis Study ◽

Flux Balance ◽

Balance Analysis ◽

Genome Scale ◽

Excess Glucose

Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.

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Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

Molecular Omics ◽

10.1039/d1mo00351h ◽

2022 ◽

Author(s):

Javad Zamani ◽

Sayed-Amir Marashi ◽

Tahmineh Lohrasebi ◽

Mohammad-Ali Malboobi ◽

Esmail Foroozan

Keyword(s):

Arabidopsis Thaliana ◽

Flux Balance Analysis ◽

Critical Assessment ◽

Flux Balance ◽

Balance Analysis ◽

Metabolic Models ◽

Living Organisms ◽

Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

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Predicting the accumulation of storage compounds by Rhodococcus jostii RHA1 in the feast-famine growth cycles using genome-scale flux balance analysis

PLoS ONE ◽

10.1371/journal.pone.0191835 ◽

2018 ◽

Vol 13 (3) ◽

pp. e0191835 ◽

Cited By ~ 4

Author(s):

Mohammad Tajparast ◽

Dominic Frigon

Keyword(s):

Flux Balance Analysis ◽

Growth Cycles ◽

Storage Compounds ◽

Flux Balance ◽

Balance Analysis ◽

Rhodococcus Jostii Rha1 ◽

Genome Scale ◽

Rhodococcus Jostii

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Simulating serial-target antibacterial drug synergies using flux balance analysis

10.1101/030791 ◽

2015 ◽

Author(s):

Andrew S Krueger ◽

Christian Munck ◽

Gautam Dantas ◽

George M Church ◽

James Galagan ◽

...

Keyword(s):

Flux Balance Analysis ◽

Enzyme Inhibitors ◽

Metabolic Inhibitors ◽

Metabolic Enzyme ◽

Antibacterial Drug ◽

Flux Balance ◽

Single Target ◽

Metabolic Systems ◽

Balance Analysis ◽

Genome Scale

Flux balance analysis (FBA) is an increasingly useful approach for modeling the behavior of metabolic systems. However, standard FBA modeling of genetic knockouts can not predict drug combination synergies observed between serial metabolic targets, even though such synergies give rise to some of the most widely used antibiotic treatments. Here we extend FBA modeling to simulate responses to chemical inhibitors at varying concentrations, by diverting enzymatic flux to a waste reaction. This flux diversion yields very similar qualitative predictions to prior methods for single target activity. However, we find very different predictions for combinations, where flux diversion, which mimics the kinetics of competitive metabolic inhibitors, can explain serial target synergies between metabolic enzyme inhibitors that we confirmed in Escherichia coli cultures. FBA flux diversion opens the possibility for more accurate genome-scale predictions of drug synergies, which can be used to suggest treatments for infections and other diseases.

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