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.

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 MODE-TASK: Large-scale protein motion tools

2017 ◽  
Author(s):  
Caroline Ross ◽  
Bilal Nizami ◽  
Michael Glenister ◽  
Olivier Sheik Amamuddy ◽  
Ali Rana Atilgan ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Complexes ◽  
Normal Mode Analysis ◽  
Md Simulations ◽  
Supplementary Information ◽  
Mode Analysis ◽  
Analysis Tool ◽  
Link Type ◽  
Supplementary Material ◽  
Anisotropic Network

AbstractSummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.Availability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.Supplementary informationDocumentation available at http://mode-task.readthedocs.io.

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 Principal metabolic flux mode analysis

2018 ◽  
Vol 34 (14) ◽  
pp. 2409-2417 ◽  
Author(s):  
Sahely Bhadra ◽  
Peter Blomberg ◽  
Sandra Castillo ◽  
Juho Rousu
Keyword(s):  
Metabolic Flux ◽  
Mode Analysis ◽  
Flux Mode
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