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arxiv: 2605.25079 · v1 · pith:K2Y24T6Tnew · submitted 2026-05-24 · 🧬 q-bio.QM · stat.AP· stat.CO· stat.ME

Trans-dimensional Bayesian model averaging for ¹³C-based metabolic flux analysis: Evidence-based flux inference under structural model uncertainty

Johann F. Jadebeck , Anton Stratmann , Martin Bey\ss , Katharina N\"oh This is my paper

Pith reviewed 2026-06-29 22:58 UTC · model grok-4.3

classification 🧬 q-bio.QM stat.APstat.COstat.ME
keywords 13C metabolic flux analysisBayesian model averagingstructural uncertaintyreversible jump MCMCnested samplingmetabolic networksmodel selection
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The pith

Bayesian model set averaging produces robust flux estimates by averaging over many possible metabolic network structures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a method for 13C metabolic flux analysis that performs Bayesian model averaging across uncertainty in network reactions and pathways rather than fixing a single topology. It extends prior work limited to reaction directions by using reversible jump Markov chain Monte Carlo to move between models of different sizes and diffusive nested sampling to estimate how well each model fits the data. The resulting framework computes averaged flux values weighted by model evidence and scales to spaces containing billions of variants. Tests on synthetic cases show that the approach keeps multiple models when data is limited and improves model and flux recovery as data become more informative. This manages structural uncertainty explicitly instead of committing to one network formulation.

Core claim

Our approach combines reversible jump Markov chain Monte Carlo for trans-dimensional exploration of model spaces with diffusive nested sampling for robust estimation of model evidences, enabling averaging over large families of metabolic network models.

What carries the argument

Bayesian model set averaging, which uses reversible jump Markov chain Monte Carlo for trans-dimensional model exploration and diffusive nested sampling for model evidence estimation to average fluxes over large families of network structures.

If this is right

  • Flux estimates remain stable when multiple network configurations fit the isotope data equally well.
  • The method identifies cases where competing models cannot be statistically distinguished.
  • Increasing data informativeness improves recovery of both supported model structures and flux values.
  • The framework provides a practical way to handle misspecification in metabolic network models for 13C-MFA.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The scalability claim suggests the method could guide experimental design by quantifying how much labeling data is needed to resolve network ambiguities.
  • Extension to genome-scale networks would require checking whether the samplers maintain mixing when the model space grows further.
  • Integration with additional data types such as proteomics could further reduce the effective number of competing models.

Load-bearing premise

Reversible jump MCMC and diffusive nested sampling can practically explore and give reliable evidence estimates for model spaces containing billions of variants.

What would settle it

A synthetic data set generated from a known true network where the method fails to assign high posterior weight to the generating model or produces inaccurate averaged fluxes despite data sufficient to distinguish structures.

Figures

Figures reproduced from arXiv: 2605.25079 by Anton Stratmann, Johann F. Jadebeck, Katharina N\"oh, Martin Bey\ss.

Figure 1
Figure 1. Figure 1: Triangulus case study. (A) Ground truth network with ILE data for the positionally labeled tracer (A ). Due to the scrambling reaction bd, the labeling pattern of C reflects the flux ratio between the two pathways. (B) Model variants and their reaction sets; within each subset models differ by uni- or bidirectionality of reaction bc. (C) Inference results at the model subset level. Subset posterior probabi… view at source ↗
Figure 2
Figure 2. Figure 2: Inference results for the Escherichia coli case study. (A) Model inference results shown as a stacked histogram over effective model DOFs. Each bar represents the total posterior probability of all model variants with a given number of DOF, partitioned by model subset (color-coded). For the single-ILE evaluation, only two subsets receive non-negligible probability: the subset containing the reference model… view at source ↗
Figure 3
Figure 3. Figure 3: Schematic representation of the evidence integral transform that nested sampling exploits. Left: Likelihood (black solid) shown as a function of model-specific fluxes. The fluxes are here formally represented as a tuple, being an element of the augmented flux space. Colored domains represent the surviving prior mass ξ for different likelihood values L{Mk}Ik . The length of the colored domains corresponds t… view at source ↗
Figure 4
Figure 4. Figure 4: Marginal flux priors for the Triangulus case study. Each row corresponds to a different model and shows all its fluxes. M1 2 is the data generating Triangulus model with ground truth fluxes indicated by dashed lines. For the models of the green model set, zero values of v n dout and v x bc collapse the models to the (nested) Triangulus model. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Single-model flux posteriors for the Triangulus case study. Each row corresponds to a different model and shows all of its fluxes. The ground truth fluxes are indicated by dashed lines. Only the ground truth model (M2 1 , third row) accurately recovers all the fluxes. The more complex models exhibit biases for the internal fluxes. The models in the blue model set are unable to explain the data due to the w… view at source ↗
Figure 6
Figure 6. Figure 6: Diagnostics of the TDNS runs for the Triangulus case study. Each column corresponds to one model subset. The first row shows the log-likelihood over the prior mass X{Mk}Ik . The increase in likelihood levels off once sufficient prior mass has been accumulated. In the bottom row, sharp peaks in the posterior weight indicate that the typical set has been identified. There is no posterior weight for very smal… view at source ↗
Figure 7
Figure 7. Figure 7: Reference metabolic map for the Escherichia coli case study. The latent pathways with uncertain metabolic activity (GOX - glyoxylate shunt, MGOX - methylglyoxal pathway, EDP - Entner-Doudoroff pathway), as well as the TPI pathway with the triosephosphate isomerase gene knocked out, are highlighted. Values give net fluxes in mmol g−1 CDW h −1 . The reference model has 11 (independent) net fluxes and 23 exch… view at source ↗
Figure 8
Figure 8. Figure 8: Marginal full posteriors for all net fluxes contained in at least one of the models in the full model set {Mk}K. Posteriors are shown for both the single-ILE evaluation and the multi-ILE evaluation. As expected, the marginal distributions are wider for the single dataset evaluation. The names of the fluxes refer to the naming convention used in the FluxML files, while the names in brackets refer to the ori… view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of BMA-based inference using BMSA and the traditional single-model inference approach for the single-ILE evaluation. The marginal posteriors for all net fluxes are shown. For the traditional analysis, a super-model based on the original study by Long and Antoniewicz [2019] was used. This model contains all latent pathways (EDP-GOX-MGOX-TPI) with 38 DOF (12 independent net fluxes and 26 exchange … view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of BMA-based inference generated using BMSA and the single-model inference approach for the multi-ILE evaluation. The marginal posteriors for all net fluxes are shown. For the single-model analysis, a super-model from the original study by Long and Antoniewicz [2019] was used. This model contains all latent pathways (EDP-GOX-MGOX-TPI) and has 38 DOF (12 independent net fluxes and 26 exchange fl… view at source ↗
Figure 11
Figure 11. Figure 11: Diagnostics of the TDNS for BMSA runs for Escherichia coli. Each column corresponds to one of the model subsets in [PITH_FULL_IMAGE:figures/full_fig_p028_11.png] view at source ↗
Figure 2
Figure 2. Figure 2: The activation probabilities for the bidirectional reactions in both runs are given in Table 3. The two runs [PITH_FULL_IMAGE:figures/full_fig_p029_2.png] view at source ↗
Figure 12
Figure 12. Figure 12: Result summary for BMSA run 1 and 2 of the single and multi-ILE evaluation. Hatches were added to distinguish the runs while keeping the colors. The results of both runs are the same. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_12.png] view at source ↗
read the original abstract

Accurate quantification of intracellular metabolic fluxes is central to systems biology and biotechnology. Flux estimation relies on biochemical network models, with $^{13}$C metabolic flux analysis (MFA) being the state-of-the-art approach. However, isotope labeling data are often insufficient to uniquely support a single network formulation. In such cases, flux estimates become model-dependent, highlighting the need for methods that explicitly account for structural uncertainty. Bayesian model averaging (BMA) provides a principled framework for this purpose, but its application to $^{13}$C-MFA has so far been restricted to uncertainty in reaction bidirectionality within fixed network topologies. We introduce a scalable Bayesian inference framework for $^{13}$C-MFA, Bayesian model set averaging, that applies BMA to encompass uncertainty in reactions and pathways. Our approach combines reversible jump Markov chain Monte Carlo for trans-dimensional exploration of model spaces with diffusive nested sampling for robust estimation of model evidences, enabling averaging over large families of metabolic network models. Using illustrative and application-scale synthetic case studies, we demonstrate that the method yields robust flux estimates, reveals when multiple network configurations are statistically indistinguishable, and recovers data-supported model structures. Importantly, rather than committing to a single model, the framework manages structural uncertainty: under limited data, competing models are retained, whereas increasing data informativeness improved model and flux recovery. The approach scales to billions of model variants, providing a practical foundation for uncertainty- and misspecification-aware quantitative flux inference in $^{13}$C-MFA.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript introduces a Bayesian model set averaging framework for 13C metabolic flux analysis. It combines reversible jump MCMC for trans-dimensional exploration of network model spaces with diffusive nested sampling for model evidence estimation, enabling averaging over structural uncertainties (reactions and pathways) rather than fixing a single topology. The central claims are that the method yields robust flux estimates, identifies statistically indistinguishable models, recovers data-supported structures on synthetic cases, and scales practically to model spaces containing billions of variants.

Significance. If the sampling methods are shown to mix and converge reliably, the framework would address a recognized limitation in 13C-MFA by providing a principled way to propagate structural model uncertainty into flux estimates, which is particularly relevant when labeling data are limited.

major comments (1)
  1. [Abstract] Abstract: the central claim that the approach 'scales to billions of model variants' and enables practical averaging over large families rests on the unvalidated assumption that RJMCMC mixes sufficiently and that diffusive nested sampling returns reliable marginal likelihoods in combinatorially enormous spaces. No acceptance rates, effective sample sizes, convergence diagnostics, or timing results are reported for the application-scale synthetic cases, making it impossible to confirm that the reported flux recovery and model selection are not artifacts of poor exploration or biased evidence estimates.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. The single major comment raises an important point about the need for explicit sampling diagnostics to support the scalability claims. We address this below and will incorporate the requested information in a revised version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the approach 'scales to billions of model variants' and enables practical averaging over large families rests on the unvalidated assumption that RJMCMC mixes sufficiently and that diffusive nested sampling returns reliable marginal likelihoods in combinatorially enormous spaces. No acceptance rates, effective sample sizes, convergence diagnostics, or timing results are reported for the application-scale synthetic cases, making it impossible to confirm that the reported flux recovery and model selection are not artifacts of poor exploration or biased evidence estimates.

    Authors: We agree that convergence diagnostics are essential to substantiate the scalability claims. The current manuscript does not report acceptance rates, effective sample sizes, Gelman-Rubin statistics, or timing results for the application-scale synthetic cases. We will add these metrics in a revised Methods section and/or supplementary material, including trace plots, autocorrelation times, and evidence of adequate mixing for both RJMCMC and diffusive nested sampling on the largest model spaces examined. This addition will directly address the concern and allow readers to evaluate the reliability of the reported flux and model recovery results. revision: yes

Circularity Check

0 steps flagged

No circularity; framework combines independent sampling methods

full rationale

The paper presents a methodological combination of reversible jump MCMC for trans-dimensional model exploration and diffusive nested sampling for evidence estimation. No derivation step reduces by construction to a fitted parameter, self-citation load-bearing premise, or renamed input. Synthetic case studies are described as independent validation. The central claim of scalability is presented as an empirical demonstration rather than a tautological re-expression of inputs. No self-definitional, fitted-input, or uniqueness-imported patterns appear in the provided abstract or described structure.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based on abstract only: relies on standard assumptions of Bayesian inference and MCMC convergence; no free parameters, axioms, or invented entities explicitly introduced beyond the sampling algorithms themselves.

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Reference graph

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