scholarly journals Flux Balance Analysis with Objective Function Defined by Proteomics Data—Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0134014 ◽  
Author(s):  
Daniel Montezano ◽  
Laura Meek ◽  
Rashmi Gupta ◽  
Luiz E. Bermudez ◽  
José C. M. Bermudez
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Objective Function ◽  
Flux Balance Analysis ◽  
Proteomics Data ◽  
Flux Balance ◽  
Balance Analysis

Dynamic flux balance analysis with nonlinear objective function

2017 ◽  
Vol 75 (6-7) ◽  
pp. 1487-1515 ◽  
Author(s):  
Xiao Zhao ◽  
Stephan Noack ◽  
Wolfgang Wiechert ◽  
Eric von Lieres
Keyword(s):  
Objective Function ◽  
Flux Balance Analysis ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis ◽  
Nonlinear Objective Function

scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2019 ◽  
Vol 15 (30) ◽  
pp. 3483-3490 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

Aim: Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux balance analysis is used to explore these differences as well as drug response. Materials & methods: Proteomics data from breast tumors were obtained by mass-spectrometry. Flux balance analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models. Results: Flux activities of vitamin A, tetrahydrobiopterin and β-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups. Conclusion: Flux activities summarize flux balance analysis data and can be associated with prognosis in cancer.

scholarly journals Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

2018 ◽  
Author(s):  
Lucía Trilla-Fuertes ◽  
Angelo Gámez-Pozo ◽  
Mariana Díaz-Almirón ◽  
Guillermo Prado-Vázquez ◽  
Andrea Zapater-Moros ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Flux Balance Analysis ◽  
Risk Groups ◽  
Prognostic Models ◽  
Breast Cancer Patients ◽  
Proteomics Data ◽  
Cancer Subtypes ◽  
Flux Balance ◽  
Balance Analysis

AbstractAims:Differences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.Materials & Methods:Proteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.Results:Flux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.Conclusions:Flux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

scholarly journals Integration of Metabolomics and Transcriptomics Reveals a Complex Diet of Mycobacterium tuberculosis during Early Macrophage Infection

mSystems ◽  
2017 ◽  
Vol 2 (4) ◽  
Author(s):  
Michael Zimmermann ◽  
Maria Kogadeeva ◽  
Martin Gengenbacher ◽  
Gayle McEwen ◽  
Hans-Joachim Mollenkopf ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Flux Balance Analysis ◽  
Intracellular Pathogens ◽  
Macrophage Infection ◽  
Content Type ◽  
Flux Balance ◽  
Genome Wide ◽  
A Genome ◽  
Host Pathogen ◽  
Balance Analysis

ABSTRACT The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments. Nutrient acquisition from the host environment is crucial for the survival of intracellular pathogens, but conceptual and technical challenges limit our knowledge of pathogen diets. To overcome some of these technical roadblocks, we exploited an experimentally accessible model for early infection of human macrophages by Mycobacterium tuberculosis, the etiological agent of tuberculosis, to study host-pathogen interactions with a multi-omics approach. We collected metabolomics and complete transcriptome RNA sequencing (dual RNA-seq) data of the infected macrophages, integrated them in a genome-wide reaction pair network, and identified metabolic subnetworks in host cells and M. tuberculosis that are modularly regulated during infection. Up- and downregulation of these metabolic subnetworks suggested that the pathogen utilizes a wide range of host-derived compounds, concomitant with the measured metabolic and transcriptional changes in both bacteria and host. To quantify metabolic interactions between the host and intracellular pathogen, we used a combined genome-scale model of macrophage and M. tuberculosis metabolism constrained by the dual RNA-seq data. Metabolic flux balance analysis predicted coutilization of a total of 33 different carbon sources and enabled us to distinguish between the pathogen’s substrates directly used as biomass precursors and the ones further metabolized to gain energy or to synthesize building blocks. This multiple-substrate fueling confers high robustness to interventions with the pathogen’s metabolism. The presented approach combining multi-omics data as a starting point to simulate system-wide host-pathogen metabolic interactions is a useful tool to better understand the intracellular lifestyle of pathogens and their metabolic robustness and resistance to metabolic interventions. IMPORTANCE The nutrients consumed by intracellular pathogens are mostly unknown. This is mainly due to the challenge of disentangling host and pathogen metabolism sharing the majority of metabolic pathways and hence metabolites. Here, we investigated the metabolic changes of Mycobacterium tuberculosis, the causative agent of tuberculosis, and its human host cell during early infection. To this aim, we combined gene expression data of both organisms and metabolite changes during the course of infection through integration into a genome-wide metabolic network. This led to the identification of infection-specific metabolic alterations, which we further exploited to model host-pathogen interactions quantitatively by flux balance analysis. These in silico data suggested that tubercle bacilli consume up to 33 different nutrients during early macrophage infection, which the bacteria utilize to generate energy and biomass to establish intracellular growth. Such multisubstrate fueling strategy renders the pathogen’s metabolism robust toward perturbations, such as innate immune responses or antibiotic treatments.

scholarly journals Integration of Metabolic Modeling and Phenotypic Data in Evaluation and Improvement of Ethanol Production Using Respiration-Deficient Mutants of Saccharomyces cerevisiae

2008 ◽  
Vol 74 (18) ◽  
pp. 5809-5816 ◽  
Author(s):  
Duygu Dikicioglu ◽  
Pinar Pir ◽  
Z. Ilsen Onsan ◽  
Kutlu O. Ulgen ◽  
Betul Kirdar ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Objective Function ◽  
Ethanol Production ◽  
Flux Balance Analysis ◽  
Glutamate Synthase ◽  
Complex Iii ◽  
Phenotypic Data ◽  
Flux Balance ◽  
Balance Analysis ◽  
Selection Of

ABSTRACT Flux balance analysis and phenotypic data were used to provide clues to the relationships between the activities of gene products and the phenotypes resulting from the deletion of genes involved in respiratory function in Saccharomyces cerevisiae. The effect of partial or complete respiratory deficiency on the ethanol production and growth characteristics of hap4Δ/hap4Δ, mig1Δ/mig1Δ, qdr3Δ/qdr3Δ, pdr3Δ/pdr3Δ, qcr7Δ/qcr7Δ, cyt1Δ/cyt1Δ, and rip1Δ/rip1Δ mutants grown in microaerated chemostats was investigated. The study provided additional evidence for the importance of the selection of a physiologically relevant objective function, and it may improve quantitative predictions of exchange fluxes, as well as qualitative estimations of changes in intracellular fluxes. Ethanol production was successfully predicted by flux balance analysis in the case of the qdr3Δ/qdr3Δ mutant, with maximization of ethanol production as the objective function, suggesting an additional role for Qdr3p in respiration. The absence of similar changes in estimated intracellular fluxes in the qcr7Δ/qcr7Δ mutant compared to the rip1Δ/rip1Δ and cyt1Δ/cyt1Δ mutants indicated that the effect of the deletion of this subunit of complex III was somehow compensated for. Analysis of predicted flux distributions indicated self-organization of intracellular fluxes to avoid NAD+/NADH imbalance in rip1Δ/rip1Δ and cyt1Δ/cyt1Δ mutants, but not the qcr7Δ/qcr7Δ mutant. The flux through the glycerol efflux channel, Fps1p, was estimated to be zero in all strains under the investigated conditions. This indicates that previous strategies for improving ethanol production, such as the overexpression of the glutamate synthase gene GLT1 in a GDH1 deletion background or deletion of the glycerol efflux channel gene FPS1 and overexpression of GLT1, are unnecessary in a respiration-deficient background.

Selection of objective function in genome scale flux balance analysis for process feed development in antibiotic production

2008 ◽  
Vol 10 (5) ◽  
pp. 227-233 ◽  
Author(s):  
Chiraphan Khannapho ◽  
Hongjuan Zhao ◽  
Bhushan K. Bonde ◽  
Andrzej M. Kierzek ◽  
Claudio A. Avignone-Rossa ◽  
...  
Keyword(s):  
Objective Function ◽  
Flux Balance Analysis ◽  
Antibiotic Production ◽  
Flux Balance ◽  
Balance Analysis ◽  
Genome Scale ◽  
Selection Of

Dynamic flux balance analysis of biomass and lipid production by Antarctic thraustochytrid Oblongichytrium sp. RT2316‐13

2020 ◽  
Vol 117 (10) ◽  
pp. 3006-3017 ◽  
Author(s):  
Carolina Shene ◽  
Paris Paredes ◽  
Liset Flores ◽  
Allison Leyton ◽  
Juan A. Asenjo ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Lipid Production ◽  
Dynamic Flux Balance Analysis ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals Understanding the host-microbe interactions using metabolic modeling

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jack Jansma ◽  
Sahar El Aidy
Keyword(s):  
Human Health ◽  
Flux Balance Analysis ◽  
Bacterial Species ◽  
Flux Balance ◽  
Interaction Field ◽  
In Silico Simulation ◽  
Balance Analysis ◽  
Microbe Interactions ◽  
Host Microbe Interactions ◽  
Genome Scale

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.

scholarly journals Why does yeast ferment? A flux balance analysis study

2010 ◽  
Vol 38 (5) ◽  
pp. 1225-1229 ◽  
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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