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2410.10145

EXAMINING THE LINK BETWEEN PEROXIREDOXIN PROTEINS AND MUTUALLY EXCLUSIVE TRANSCRIPTION FACTOR ACTIVATION WITH A MATHEMATICAL MODEL

Zachary Allen Schlamowitz

incompletemedium confidence
Category
Not specified
Journal tier
Specialist/Solid
Processed
Sep 28, 2025, 12:56 AM

Audit review

The paper establishes, via ODE simulations, three core qualitative claims: a staggered, sigmoidal dose–response with three regimes at a fixed timepoint (creating regions below/ between/ above the PrxII and PrxI hyperoxidation thresholds), early-time co-activation of PrxI/II disulfide signaling at low doses, and asymmetric knockout shifts with pool-size rescue; all are clearly described in the PDF’s Results and Predictions sections . However, these are presented as proof-of-principle simulations rather than mathematical theorems and the authors themselves acknowledge model limitations and a biochemistry mismatch in kOx handling . The candidate model solution correctly formalizes the first and third claims under minimal monotonicity/continuity assumptions, but its Phase-2 argument (uniform early-time co-activation) tacitly assumes s_i(D,0)>0 and gives a derivative-bound alternative that does not suffice to ensure positivity unless a strictly positive one-sided time-derivative is also imposed. Thus, both are incomplete: the paper for lack of formal proofs and unresolved modeling caveats, and the model for a technical gap in Task 2.

Referee report (LaTeX)

\textbf{Recommendation:} major revisions

\textbf{Journal Tier:} specialist/solid

\textbf{Justification:}

The manuscript offers a timely, simulation-based proof-of-principle that staggered Prx hyperoxidation can encode dose information into transcriptional regimes and makes concrete knockout/pool-size predictions. However, key kinetic uncertainties (e.g., indistinguishable kOx for PrxI/II) and the absence of sensitivity and robustness analyses limit interpretability. Strengthening parameter justification, clarifying the mapping from hyperoxidation/disulfide metrics to transcriptional outputs, and reporting uncertainty on thresholds would materially improve the work.