PolyRound: polytope rounding for random sampling in metabolic networks

Theorell, Axel; Jadebeck, Johann Fredrik; Nöh, Katharina; Stelling, Jörg

doi:10.1093/bioinformatics/btab552

Cited by 12 publications

(16 citation statements)

References 15 publications

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“…Herein, 13CFLUX2 was used for likelihood computation. For speed, sampling algorithms implemented in the C++ library HOPS v2.0.0 (Jadebeck et al , 2021) were employed after suitable preprocessing using Polyround v0.1.8 (Theorell et al , 2022). The code to reproduce the analysis is available at https://github.com/JuBiotech/Supplement-to_Borah-et-al.-MSB-2023 (https://doi.org/10.5281/zenodo.7506282).…”

Section: Methodsmentioning

confidence: 99%

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Borah

Beyß

et al. 2023

Molecular Systems Biology

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Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed 13 C 15 N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based 13 C 15 N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.

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Section: Methodsmentioning

confidence: 99%

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Borah

Beyß

et al. 2023

Molecular Systems Biology

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“…Herein, 13CFLUX2 was used for likelihood computation. For speed, sampling algorithms implemented in the C++ library HOPS v2.0.0 ( 60 ) were employed after suitable preprocessing using Polyround v0.1.8 ( 61 ). Parallel tempering with dynamic temperature selection was applied to sample from potentially multi-modal distributions.…”

Section: Methodsmentioning

confidence: 99%

Bayesian multi-model-based ¹³C¹⁵N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

Borah

Beyß

et al. 2022

Preprint

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Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb) this is particularly important in informing the development of effective drugs targeting metabolism of the pathogen. Here we performed 13C15N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13C15N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13C-MFA. We quantified CN fluxes for amino acid and for the first time nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.

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“…Then, inequalities that are effectively equalities are identified and reformulated as equalities. These manipulations leave the solution space of the flux polytope 𝒫 D unchanged, but improve numerical stability of the sampling workflow (Theorell et al, 2021). Next, the null space K of the (adapted) equality system is used to express the polytope in terms of orthogonal (independent) coordinates ν indep : where ν 0 is some flux solution within the flux polytope 𝒫 D , and ν indep ∈ ℝ d with d ≤ D the effective model dimension.…”

Section: Methodsmentioning

confidence: 99%

“…GEMs were downloaded from the BiGG database (King et al, 2015; Norsigian et al, 2019) in SBML (Systems Biology Markup Language) format (Hucka et al, 2003), and plugged into PolyRound v0.1.8, a python package for polytope rounding (Theorell et al, 2021). The highly optimized polytope sampling library HOPS v3.0.0 was used for MCMC sampling (Jadebeck et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…After sampling, the states have to be mapped back to the original space by inverting the rounding transformation. Together, the two strategies culminated in the celebrated Coordinate Hit-and-Run with Rounding (CHRR) algorithm (Haraldsdóttir et al, 2017), the tried-and-tested workhorse for UCPS in the COBRA domain (Herrmann et al, 2019; Fallahi et al, 2020; Theorell et al, 2021), with several implementations being available (Haraldsdóttir et al, 2017; Heirendt et al, 2019; Jadebeck et al, 2020; Gollub et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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CHRRT: boosting coordinate hit-and-run with rounding by thinning

Jadebeck

Wiechert

Nöh

2022

Preprint

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Thinning is a sub-sampling technique to reduce the memory footprint of Markov chain Monte Carlo. Despite being commonly used, thinning is rarely considered efficient. For sampling convex polytopes uniformly, a highly relevant use-case in systems biology, we here demonstrate that thinning generally boosts computational and, thereby, sampling efficiencies of the widely used Coordinate Hit-and-Run with Rounding (CHRR) algorithm. We benchmark CHRR with thinning (CHRRT) with simplices and constrained-based metabolic networks with up to thousands of dimensions. With appropriate thinning, CHRRT offers a substantial increase in computational efficiency compared to unthinned CHRR, in our examples of up to three orders of magnitude, as measured by the effective sample size per time (ESS/t). Our experiments reveal that the performance gain of CHRRT by optimal thinning grows substantially with polytope (effective model) dimension. Based on our experiments, we provide practically useful advice for tuning thinning to efficient and effective use of compute resources. Besides allocating computational resources optimally to permit sampling convex polytopes uniformly to convergence in a fraction of time, exploiting thinning unlocks investigating hitherto intractable models under limited computational budgets. CHRRT thereby paves the way to keep pace with progressing model sizes within the existing constraint-based reconstruction and analysis (COBRA) tool set. Sampling and evaluation pipelines are available at https://jugit.fz-juelich.de/IBG-1/ModSim/fluxomics/chrrt.

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PolyRound: polytope rounding for random sampling in metabolic networks

Cited by 12 publications

References 15 publications

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Bayesian multi-model-based ¹³C¹⁵N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

CHRRT: boosting coordinate hit-and-run with rounding by thinning

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PolyRound: polytope rounding for random sampling in metabolic networks

Cited by 12 publications

References 15 publications

One‐shot 13C15N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

One‐shot 13C15N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Bayesian multi-model-based 13C15N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

CHRRT: boosting coordinate hit-and-run with rounding by thinning

Contact Info

Product

Resources

About

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

One‐shot ¹³C¹⁵N‐metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Bayesian multi-model-based ¹³C¹⁵N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria