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 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

2021 ◽  
Vol 17 (1) ◽  
pp. e1007694
Author(s):  
Claudio Tomi-Andrino ◽  
Rupert Norman ◽  
Thomas Millat ◽  
Philippe Soucaille ◽  
Klaus Winzer ◽  
...  
Keyword(s):  
Experimental Data ◽  
In Silico ◽  
Physicochemical Parameters ◽  
Metabolic Flux ◽  
Growth Conditions ◽  
Central Carbon Metabolism ◽  
Centrality Measures ◽  
Flux Analysis ◽  
Metabolomics Data

Metabolic 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.

scholarly journals Metabolic fluxes in recombinant Streptomyces lividans analyzed with 13 C-based metabolic flux analysis

2016 ◽  
Vol 49 (26) ◽  
pp. 336-343 ◽  
Author(s):  
Jeroen Bouvin ◽  
Wouter Daniels ◽  
Jozef Anné ◽  
Bart Nicolaï ◽  
Kristel Bernaerts
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Streptomyces Lividans ◽  
Flux Analysis ◽  
Metabolic Fluxes

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 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 ScalaFlux: a scalable approach to quantify fluxes in metabolic subnetworks

2019 ◽  
Author(s):  
Pierre Millard ◽  
Uwe Schmitt ◽  
Patrick Kiefer ◽  
Julia A. Vorholt ◽  
Stéphanie Heux ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Isotope Labeling ◽  
Building Blocks ◽  
Label Propagation ◽  
Cell Physiology ◽  
Flux Analysis ◽  
New Approach ◽  
Metabolic Fluxes ◽  
Metabolic Systems

Abstract13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.Author SummaryMetabolism is a fundamental biochemical process that enables all organisms to operate and grow by converting nutrients into energy and ‘building blocks’. Metabolic flux analysis allows the quantification of metabolic fluxes in vivo, i.e. the actual rates of biochemical conversions in biological systems, and is increasingly used to probe metabolic activity in biology, biotechnology and medicine. Isotope labeling experiments coupled with mathematical models of large metabolic networks are the most commonly used approaches to quantify fluxes within cells. However, many biological questions only require flux information from a subset of reactions, not the full network. Here, we propose a new approach with three main advantages over existing methods: better scalability (fluxes can be measured through a single reaction, a metabolic pathway or a set of pathways of interest), better robustness to missing data and information gaps, and lower requirements in terms of measurements and computational resources. We validate our method both theoretically and experimentally. ScalaFlux can be used for high-throughput flux measurements in virtually any metabolic system and paves the way to the analysis of dynamic fluxome rearrangements.

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