Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives

Hendry, John I.; Dinh, Hoang V.; Foster, Charles; Gopalakrishnan, Saratram; Wang, Lin; Maranas, Costas D.

doi:10.1016/j.coche.2020.05.008

Cited by 8 publications

(4 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…99,100 In studies on the cyanobacterium Synechococcus elongatus, 101,102 it has been shown that the substantially larger genome-scale 13C-MFA models achieved better fits to the labeling data, that these reductions in SSR were statistically justified, and that the original models of core metabolism underestimated the uncertainty in a number of flux estimates by ignoring alternative metabolic pathways that could also explain patterns in the labeling data. 100 The examples above demonstrate that rather than being a statistical curi- This method does not work when the DOF of the compared models are different, as increasing the DOF in a model inevitably allows it to fit a given data set better. This may be accounted for informally by noting the change in DOF (e.g., 94 ) or in a more statistically rigorous way using the extra-sum-of-squares test 103,104 or information criteria.…”

Section: Model Selection In 13c-mfamentioning

confidence: 99%

“…Finally, the literature on “Genome‐scale‐13C‐MFA” has provided evidence that the exclusion of many reactions peripheral to the metabolic network under consideration (typically core metabolism) in 13C‐MFA can result in artificially narrow confidence intervals. Genome‐scale‐13C‐MFA involves estimating a flux map by minimizing deviation between predicted and measured isotopic labeling but using the kind of genome‐scale metabolic network more typically used for FBA analyses 99,100 . In studies on the cyanobacterium Synechococcus elongatus , 101,102 it has been shown that the substantially larger genome‐scale 13C‐MFA models achieved better fits to the labeling data, that these reductions in SSR were statistically justified, and that the original models of core metabolism underestimated the uncertainty in a number of flux estimates by ignoring alternative metabolic pathways that could also explain patterns in the labeling data 100 .…”

Section: Validation Techniques In Fba and 13c‐mfamentioning

confidence: 99%

See 1 more Smart Citation

Model validation and selection in metabolic flux analysis and flux balance analysis

Kaste,

Shachar‐Hill

2023

Biotechnology Progress

View full text Add to dashboard Cite

Abstract13C‐Metabolic Flux Analysis (13C‐MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint‐based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state‐of‐the‐art in constraint‐based metabolic model validation and model selection. Applications and limitations of the χ2‐test of goodness‐of‐fit, the most widely used quantitative validation and selection approach in 13C‐MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C‐MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint‐based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology.

show abstract

Section: Model Selection In 13c-mfamentioning

confidence: 99%

Section: Validation Techniques In Fba and 13c‐mfamentioning

confidence: 99%

Model validation and selection in metabolic flux analysis and flux balance analysis

Kaste,

Shachar‐Hill

2023

Biotechnology Progress

View full text Add to dashboard Cite

show abstract

“…According to the metabolic network and the stoichiometric balanced equation of each reaction, the relationship among the reaction rates was obtained [23,24]. It was assumed that the intermediate metabolites were in a pseudo-steady state such that their concentration change rates were zero.…”

Section: Establishment Of Metabolic Flux Balance Model Of Propionic Acidmentioning

confidence: 99%

Efficient Production of Propionic Acid in the Fed-Batch Fermentation of Propionibacterium acidipropionici and Its Metabolic Flux Analysis

Zhang¹,

Zhang²,

X³

et al. 2021

austinjnutrmetab

View full text Add to dashboard Cite

To improve the fermentation efficiency of Propionibacterium acidipropionici, a simplified metabolic network was established to provide theoretical guidance for medium optimization and process regulation. The effect of glucose and glycerol on propionic acid production and metabolic flux distribution was investigated and the combination of glucose and glycerol was optimized. The results showed that the productivity of propionic acid could be improved by enhancing the synthesis of pyruvate and its flux distribution to the oxaloacetate branch. Finally, the scaled-up fed-batch fermentation of P. acidipropionici was conducted. The concentration of propionic acid reached 51.75 ± 3.62g/L with a glucose/glycerol ratio of 4:1, an improvement of 79.25% relative to the use of glucose alone, and the corresponding productivity and yield were 0.39g/(L· h) and 0.52g/g, respectively. Therefore, the combination of glucose and glycerol significantly improved the productivity of propionic acid and provides a new strategy for industrial production.

show abstract

“…To this end, algorithmic advances have led to decreasing the number of simulated variables [e.g. via elementary metabolite units (EMUs)] and expanding the applicability to networks of large size (Gopalakrishnan et al, 2018;Hendry et al, 2020). This workflow for flux estimation from socalled global MFA approaches is well established even for the isotopic nonstationary state and implemented in toolboxes like INCA (Young, 2014).…”

Section: Introductionmentioning

confidence: 99%

A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae

Küken,

Treves,

Nikoloski

2023

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionFlux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from 13CO2-labeling experiments, the bottleneck in flux estimation remains the application of isotopically nonstationary MFA (INST-MFA). INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway.MethodsFirst, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns.ResultsWe then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions.DiscussionOur findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement (rs=0.89) to predictions obtained from INCA, a state-of-the-art tool for INST-MFA.

show abstract

Metabolic flux analysis reaching genome wide coverage: lessons learned and future perspectives

Cited by 8 publications

References 69 publications

Model validation and selection in metabolic flux analysis and flux balance analysis

Model validation and selection in metabolic flux analysis and flux balance analysis

Efficient Production of Propionic Acid in the Fed-Batch Fermentation of Propionibacterium acidipropionici and Its Metabolic Flux Analysis

A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae

Contact Info

Product

Resources

About