Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 232-240
Author(s):  
Axel Theorell ◽  
Katharina Nöh
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Bayesian Model Averaging ◽  
Linear Models ◽  
Model Averaging ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Large Set ◽  
Vast Number

Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals 2H and 13C metabolic flux analysis elucidates in vivo thermodynamics of the ED pathway in Zymomonas mobilis

2019 ◽  
Vol 54 ◽  
pp. 301-316 ◽  
Author(s):  
Tyler B. Jacobson ◽  
Paul A. Adamczyk ◽  
David M. Stevenson ◽  
Matthew Regner ◽  
John Ralph ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Zymomonas Mobilis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis

scholarly journals Principal Metabolic Flux Mode Analysis

2017 ◽  
Author(s):  
Sahely Bhadra ◽  
Peter Blomberg ◽  
Sandra Castillo ◽  
Juho Rousu
Keyword(s):  
Metabolic Flux ◽  
Principal Component ◽  
Supplementary Information ◽  
Mode Analysis ◽  
Flux Analysis ◽  
Large Set ◽  
Elementary Mode Analysis ◽  
Modes Of Variability ◽  
Link Type ◽  
Flux Mode

AbstractMotivationIn the analysis of metabolism using omics data, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and Stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, produce results that are readily interpretable in terms of metabolic flux modes, however, they are not best suited for exploratory analysis on a large set of samples.ResultsWe propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and Stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a Stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology.AvailabilityMatlab software for PMFA and SPMFA is available in https://github.com/ aalto-ics-kepaco/[email protected], [email protected], [email protected], [email protected] informationDetailed results are in Supplementary files. Supplementary data are available at https://github.com/aalto-ics-kepaco/PMFA/blob/master/Results.zip.

scholarly journals Progress towards in vivo brain 13C-MRS in mice: Metabolic flux analysis in small tissue volumes

2017 ◽  
Vol 529 ◽  
pp. 229-244 ◽  
Author(s):  
Marta Lai ◽  
Rolf Gruetter ◽  
Bernard Lanz
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
Small Tissue ◽  
13C Mrs

In Vivo NMR for 13C metabolic Flux Analysis

2013 ◽  
pp. 143-152 ◽  
Author(s):  
Albrecht Roscher ◽  
Stéphanie Troufflard ◽  
Abdelghani Idrissi Taghki
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Metabolic Flux Analysis ◽  
In Vivo Nmr

Assessment and Expansion of Models for13C-Metabolic Flux Analysis (MFA) In Vivo

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 258-LB
Author(s):  
CLINTON HASENOUR ◽  
MOHSIN RAHIM ◽  
JAMEY YOUNG
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis

scholarly journals Age-Specific Differences in Whole-Body Amino Acid Kinetics

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 88-88
Author(s):  
Agata Wierzchowska-McNew ◽  
Mariëlle Engelen ◽  
Gabriella Ten Have ◽  
John Thaden ◽  
Nicolaas Deutz
Keyword(s):  
Older Adults ◽  
Amino Acids ◽  
Body Composition ◽  
Amino Acid ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Whole Body ◽  
Flux Analysis ◽  
Large Set ◽  
Body Production

Abstract Objectives Aging is associated with changes in body composition (eg. sarcopenia) but the overall effects of aging on systemic amino acid kinetics need further exploration. We previously reported metabolic differences in certain amino acids between young and older adults using comprehensive metabolic flux analysis. We expanded this novel single stable tracer pulse approach by the addition of several other isotopically-labeled amino acids to confirm and extend our findings in a new cohort of young and older adults. Methods We studied 18 healthy young (∼23 y, 9 females and 9 males) and 16 older adults (∼67 y, 8 females and 8 males) by administering a single dose of a mixture of stable amino acid tracers related to arginine-citrulline, glutamate, branched-chain amino acid (BCAA: leucine, isoleucine, valine), and protein-related metabolism. A baseline blood sample was collected before administration of the pulse tracer followed by 1.5 hours blood sampling protocol. We measured plasma enrichments by LC-MS/MS to calculate their whole body production (WBP) rates and metabolite interconversions. In addition, body composition by dual-energy X-ray absorptiometry was measured. Statistics were performed by unpaired student t-test. Results Older adults had a 13% higher Body Mass Index (P = 0.005) and 13% lower appendicular skeletal muscle index than the younger group (P = 0.04). WBP of glutamate was 26% lower (P < 0.05) in older adults whereas WBP of tau-methylhistidine was higher (31%, P = 0.045), in line with our previously reported data. In addition, older adults were characterized by lower WBP of all 3 BCAAs (P = 0.007), histidine (P = 0.001) and tryptophan (P = 0.003) by 17%, 16%, and 15%, respectively. However, higher whole-body production rates were observed for citrulline (24%, P = 0.036) and de novo arginine synthesis (21%, P = 0.027) in older adults. Conclusions Metabolic flux analysis reveals that the kinetics of a large set of amino acids differ between younger and older adults which indicates that amino acid metabolism is age-related. The clinical relevance of those changes needs further investigation. Funding Sources CTRAL Internal Funds.

scholarly journals OpenMebius: An Open Source Software for Isotopically Nonstationary13C-Based Metabolic Flux Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shuichi Kajihata ◽  
Chikara Furusawa ◽  
Fumio Matsuda ◽  
Hiroshi Shimizu
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Simulated Data ◽  
Isotopic Labeling ◽  
Metabolic Model ◽  
Cell Physiology ◽  
Flux Analysis

Thein vivomeasurement of metabolic flux by13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA.

scholarly journals In Vivo Estimation of Ketogenesis Using Metabolic Flux Analysis—Technical Aspects and Model Interpretation

Metabolites ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 279
Author(s):  
Stanislaw Deja ◽  
Blanka Kucejova ◽  
Xiaorong Fu ◽  
Jeffrey D. Browning ◽  
Jamey D. Young ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Accurate Determination ◽  
Liver Mitochondria ◽  
Flux Analysis ◽  
Peripheral Tissues ◽  
Model Interpretation ◽  
Liquid Chromatography Tandem Mass ◽  
Pool Model

Ketogenesis occurs in liver mitochondria where acetyl-CoA molecules, derived from lipid oxidation, are condensed into acetoacetate (AcAc) and reduced to β-hydroxybutyrate (BHB). During carbohydrate scarcity, these two ketones are released into circulation at high rates and used as oxidative fuels in peripheral tissues. Despite their physiological relevance and emerging roles in a variety of diseases, endogenous ketone production is rarely measured in vivo using tracer approaches. Accurate determination of this flux requires a two-pool model, simultaneous BHB and AcAc tracers, and special consideration for the stability of the AcAc tracer and analyte. We describe the implementation of a two-pool model using a metabolic flux analysis (MFA) approach that simultaneously regresses liquid chromatography-tandem mass spectrometry (LC-MS/MS) ketone isotopologues and tracer infusion rates. Additionally, 1H NMR real-time reaction monitoring was used to evaluate AcAc tracer and analyte stability during infusion and sample analysis, which were critical for accurate flux calculations. The approach quantifies AcAc and BHB pool sizes and their rates of appearance, disposal, and exchange. Regression analysis provides confidence intervals and detects potential errors in experimental data. Complications for the physiological interpretation of individual ketone fluxes are discussed.

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