scholarly journals Time Integrated Flux Analysis: Exploiting the Concentration Measurements Directly for Cost-Effective Metabolic Network Flux Analysis

2019 ◽  
Vol 7 (12) ◽  
pp. 620 ◽  
Author(s):  
Portela ◽  
Richelle ◽  
Dumas ◽  
von Stosch
Keyword(s):  
Metabolic Flux ◽  
Cost Effective ◽  
Flux Analysis ◽  
Concentration Data ◽  
Practical Applications ◽  
Variability Analysis ◽  
Flux Balance ◽  
Balance Analysis ◽  
The Impact

Background: Flux analyses, such as Metabolic Flux Analysis (MFA), Flux Balance Analysis (FBA), Flux Variability Analysis (FVA) or similar methods, can provide insights into the cellular metabolism, especially in combination with experimental data. The most common integration of extracellular concentration data requires the estimation of the specific fluxes (/rates) from the measured concentrations. This is a time-consuming, mathematically ill-conditioned inverse problem, raising high requirements for the quality and quantity of data. Method: In this contribution, a time integrated flux analysis approach is proposed which avoids the error-prone estimation of specific flux values. The approach is adopted for a Metabolic time integrated Flux Analysis and (sparse) time integrated Flux Balance/Variability Analysis. The proposed approach is applied to three case studies: (1) a simulated bioprocess case studying the impact of the number of samples (experimental points) and measurements’ noise on the performance; (2) a simulation case to understand the impact of network redundancies and reaction irreversibility; and (3) an experimental bioprocess case study, showing its relevance for practical applications. Results: It is observed that this method can successfully estimate the time integrated flux values, even with relatively low numbers of samples and significant noise levels. In addition, the method allows the integration of additional constraints (e.g., bounds on the estimated concentrations) and since it eliminates the need for estimating fluxes from measured concentrations, it significantly reduces the workload while providing about the same level of insight into the metabolism as classic flux analysis methods.

scholarly journals How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1577
Author(s):  
Philippe Bogaerts ◽  
Alain Vande Vande Wouwer
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Convex Polytope ◽  
Flux Analysis ◽  
Linear Algebraic System ◽  
Balance Equations ◽  
Flux Variability Analysis ◽  
Variability Analysis ◽  
Flux Balance ◽  
Balance Analysis

Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.

scholarly journals Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

2020 ◽  
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Flux Analysis ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Living Systems ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Fluxes ◽  
Balance Analysis

AbstractMetabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.Author summaryBiotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

scholarly journals Entner–Doudoroff pathway and Non-OxPPP bypasses glycolysis and OxPPP in Ralstonia solanacearum

2020 ◽  
Author(s):  
Poonam Jyoti ◽  
Manu Shree ◽  
Chandrakant Joshi ◽  
Tulika Prakash ◽  
Suvendra Kumar Ray ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Ralstonia Solanacearum ◽  
Metabolic Flux ◽  
Pentose Phosphate ◽  
Flux Analysis ◽  
Flux Balance ◽  
Metabolic Network Analysis ◽  
Central Carbon ◽  
Balance Analysis

AbstractIn Ralstonia solanacearum, a devastating phytopathogen whose metabolism is poorly understood, we observed that Entner-Doudoroff (ED) pathway and NonOxidative pentose phosphate pathway (OxPPP) bypasses glycolysis and OxPPP under glucose oxidation. Evidences derived from 13C stable isotopes feeding and genome annotation based comparative metabolic network analysis supported the observations. Comparative metabolic network analysis derived from the currently available 53 annotated R. solanacearum strains also including the recently reported strain (F1C1), representing the four phylotypes confirmed the lack of key genes coding for phosphofructokinase (pfk-1) and phosphogluconate dehydrogenase (gnd) enzymes that are relevant for glycolysis and OxPPP respectively. R. solanacearum F1C1 cells fed with 13C Glucose (99%[1-13C]- or 99%[1,2-13C]- or 40%[13C6]-glucose) followed by GC-MS based labelling analysis of fragments from amino acids, glycerol and ribose provided clear evidence that rather than Glycolysis and OxPPP, ED pathway and NonOxPPP are the main routes sustaining metabolism in R. solanacearum. The 13C incorporation in the mass ions of alanine (m/z 260, m/z 232); valine (m/z 288, m/z 260), glycine (m/z 218), serine (m/z 390, m/z 362), histidine (m/z 440, m/z 412), tyrosine (m/z 466, m/z 438), phenylalanine (m/z 336, m/z 308), glycerol (m/z 377) and ribose (m/z 160) mapped the pathways supporting the observations. The outcomes help better defining the central carbon metabolic network of R. solanacearum that can be integrated with 13C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes.ImportanceUnderstanding the metabolic versatility of Ralstonia solanacearum is important as it regulates the tradeoff between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on potential roles of Glycolysis and Oxidative pentose phosphate pathways (OxPPP) in R. solanacearum (3, 4). This work provided evidence from 13C stable isotopes feeding and genome annotation based comparative metabolic network analysis that Entner-Doudoroff pathway and Non-OxPPP bypasses glycolysis and OxPPP during the oxidation of Glucose, one of the host xylem pool that serves as a potential carbon source (5). The outcomes help better defining the central carbon metabolic network of R. solanacearum that can be integrated with 13C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

scholarly journals Semi-automated Curation of Metabolic Models via Flux Balance Analysis: A Case Study with Mycoplasma gallisepticum

2013 ◽  
Vol 9 (9) ◽  
pp. e1003208 ◽  
Author(s):  
Eddy J. Bautista ◽  
Joseph Zinski ◽  
Steven M. Szczepanek ◽  
Erik L. Johnson ◽  
Edan R. Tulman ◽  
...  
Keyword(s):  
Flux Balance Analysis ◽  
Mycoplasma Gallisepticum ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models

A simple metabolic flux balance analysis of biomass and bioethanol production in Saccharomyces cerevisiae fed-batch cultures

2011 ◽  
Vol 16 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Iliana Barrera-Martínez ◽  
R. Axayácatl González-García ◽  
Edgar Salgado-Manjarrez ◽  
Juan S. Aranda-Barradas
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Flux Balance Analysis ◽  
Metabolic Flux ◽  
Bioethanol Production ◽  
Fed Batch ◽  
Batch Cultures ◽  
Flux Balance ◽  
Balance Analysis

scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals A systematic simulation of the effect of salicylic acid on sphingolipid metabolism

2014 ◽  
Author(s):  
Chao Shi ◽  
Jian Yin ◽  
Zhe Liu ◽  
Jian-Xin Wu ◽  
Qi Zhao ◽  
...  
Keyword(s):  
Salicylic Acid ◽  
Turnover Rate ◽  
Metabolic Flux ◽  
Defense Responses ◽  
Sphingolipid Metabolism ◽  
Short Time Scale ◽  
Flux Analysis ◽  
Cell Simulation ◽  
Balance Analysis ◽  
Short Time

The phytohormone salicylic acid (SA) affects plant development and defense responses. Recent studies revealed that SA is also involved in the regulation of sphingolipid metabolism, but the details of this regulation remain to be explored. Here, we use in silico Flux Balance Analysis (FBA) with published microarray data to construct a whole-cell simulation model, including 23 pathways, 259 reactions and 172 metabolites, to predict the alterations in flux of major sphingolipid species after treatment with exogenous SA. This model predicts significant changes in fluxes of certain sphingolipid species after SA treatment, changes that likely trigger downstream physiological and phenotypic effects. To validate the simulation, we used isotopic non-stationary metabolic flux analysis to measure sphingolipid contents and turnover rate in Arabidopsis thaliana seedlings treated with SA or the SA analog benzothiadiazole (BTH). The results show that both SA and BTH affect sphingolipid metabolism by not only concentration of certain species, but also the optimal flux distribution and turnover rate of sphingolipid contents. Our strategy allows us to formally estimate sphingolipid fluxes on a short time scale and gives us a systemic view of the effect of SA on sphingolipid homeostasis.

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