From Gene Expression to Metabolic Fluxes

2008 ◽  
pp. 37-66 ◽  
Author(s):  
Ana Paula Oliveira ◽  
Michael C. Jewett ◽  
Jens Nielsen
Keyword(s):  
Gene Expression ◽  
Metabolic Fluxes

scholarly journals Contribution of gene expression to metabolic fluxes in hypermetabolic livers induced through burn injury and cecal ligation and puncture in rats

2007 ◽  
Vol 97 (1) ◽  
pp. 118-137 ◽  
Author(s):  
Scott Banta ◽  
Murali Vemula ◽  
Tadaaki Yokoyama ◽  
Arul Jayaraman ◽  
François Berthiaume ◽  
...  
Keyword(s):  
Gene Expression ◽  
Burn Injury ◽  
Cecal Ligation ◽  
Cecal Ligation And Puncture ◽  
Metabolic Fluxes

Gene expression profiles based flux balance model to predict the carbon source for Bacillus subtilis

2019 ◽  
Author(s):  
Kulwadee Thanamit ◽  
Franziska Hoerhold ◽  
Marcus Oswald ◽  
Rainer Koenig
Keyword(s):  
Gene Expression ◽  
Carbon Source ◽  
Drug Targets ◽  
Expression Profiles ◽  
Carbon Sources ◽  
Gene Expression Profiles ◽  
Fast Method ◽  
Flux Balance ◽  
Metabolic Fluxes

ABSTRACTFinding drug targets for antimicrobial treatment is a central focus in biomedical research. To discover new drug targets, we developed a method to identify which nutrients are essential for microorganisms. Using 13C labeled metabolites to infer metabolic fluxes is the most informative way to infer metabolic fluxes to date. However, the data can get difficult to acquire in complicated environments, for example, if the pathogen homes in host cells. Although data from gene expression profiling is less informative compared to metabolic tracer derived data, its generation is less laborious, and may still provide the relevant information. Besides this, metabolic fluxes have been successfully predicted by flux balance analysis (FBA). We developed an FBA based approach using the stoichiometric knowledge of the metabolic reactions of a cell combining them with expression profiles of the coding genes. We aimed to identify essential drug targets for specific nutritional uptakes of microorganisms. As a case study, we predicted each single carbon source out of a pool of eight different carbon sources for B. subtilis based on gene expression profiles. The models were in good agreement to models basing on 13C metabolic flux data of the same conditions. We could well predict every carbon source. Later, we applied successfully the model to unseen data from a study in which the carbon source was shifted from glucose to malate and vice versa. Technically, we present a new and fast method to reduce thermodynamically infeasible loops, which is a necessary preprocessing step for such model-building algorithms.SIGNIFICANCEIdentifying metabolic fluxes using 13C labeled tracers is the most informative way to gain insight into metabolic fluxes. However, obtaining the data can be laborious and challenging in a complex environment. Though transcriptional data is an indirect mean to estimate the fluxes, it can help to identify this. Here, we developed a new method employing constraint-based modeling to predict metabolic fluxes embedding gene expression profiles in a linear regression model. As a case study, we used the data from Bacillus subtilis grown under different carbon sources. We could well predict the correct carbon source. Additionally, we established a novel and fast method to remove thermodynamically infeasible loops.

scholarly journals Tying Metabolic Branches With Histone Tails Using Systems Biology

2019 ◽  
Vol 12 ◽  
pp. 251686571986968
Author(s):  
Sriram Chandrasekaran
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Histone Modifications ◽  
Metabolic Pathways ◽  
Cellular Mechanism ◽  
Histone Tails ◽  
Metabolic Fluxes ◽  
Adenosyl Methionine ◽  
The Impact ◽  
Influence Gene Expression

Histone modifications represent an innate cellular mechanism to link nutritional status to gene expression. Metabolites such as acetyl-CoA and S-adenosyl methionine influence gene expression by serving as substrates for modification of histones. Yet, we lack a predictive model for determining histone modification levels based on cellular metabolic state. The numerous metabolic pathways that intersect with histone marks makes it highly challenging to understand their interdependencies. Here, we highlight new systems biology tools to unravel the impact of nutritional cues and metabolic fluxes on histone modifications.

Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints

2011 ◽  
Vol 8 (1) ◽  
pp. 206-216 ◽  
Author(s):  
Rogier J P van Berlo ◽  
Dick de Ridder ◽  
Jean-Marc Daran ◽  
Pascale A S Daran-Lapujade ◽  
Bas Teusink ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Fluxes

scholarly journals Transcriptome integrated metabolic modeling of carbon assimilation underlying storage root development in cassava

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ratchaprapa Kamsen ◽  
Saowalak Kalapanulak ◽  
Porntip Chiewchankaset ◽  
Treenut Saithong
Keyword(s):  
Gene Expression ◽  
Carbon Assimilation ◽  
Metabolic Model ◽  
Pentose Phosphate ◽  
Balance Model ◽  
High Yield ◽  
Storage Roots ◽  
Metabolic Fluxes ◽  
Constraint Based Modeling ◽  
High Consistency

AbstractThe existing genome-scale metabolic model of carbon metabolism in cassava storage roots, rMeCBM, has proven particularly resourceful in exploring the metabolic basis for the phenotypic differences between high and low-yield cassava cultivars. However, experimental validation of predicted metabolic fluxes by carbon labeling is quite challenging. Here, we incorporated gene expression data of developing storage roots into the basic flux-balance model to minimize infeasible metabolic fluxes, denoted as rMeCBMx, thereby improving the plausibility of the simulation and predictive power. Three different conceptual algorithms, GIMME, E-Flux, and HPCOF were evaluated. The rMeCBMx-HPCOF model outperformed others in predicting carbon fluxes in the metabolism of storage roots and, in particular, was highly consistent with transcriptome of high-yield cultivars. The flux prediction was improved through the oxidative pentose phosphate pathway in cytosol, as has been reported in various studies on root metabolism, but hardly captured by simple FBA models. Moreover, the presence of fluxes through cytosolic glycolysis and alanine biosynthesis pathways were predicted with high consistency with gene expression levels. This study sheds light on the importance of prediction power in the modeling of complex plant metabolism. Integration of multi-omics data would further help mitigate the ill-posed problem of constraint-based modeling, allowing more realistic simulation.

Prediction of metabolic fluxes from gene expression data with Huber penalty convex optimization function

2017 ◽  
Vol 13 (5) ◽  
pp. 901-909 ◽  
Author(s):  
Shao-Wu Zhang ◽  
Wang-Long Gou ◽  
Yan Li
Keyword(s):  
Gene Expression ◽  
Convex Optimization ◽  
Gene Expression Data ◽  
Metabolic Pathway ◽  
Essential Role ◽  
Metabolic Flux ◽  
Critical Parameters ◽  
Expression Data ◽  
Metabolic Fluxes ◽  
Optimization Function

As one of the critical parameters of a metabolic pathway, the metabolic flux in a metabolic network serves as an essential role in physiology and pathology.

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