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  "slug": "inhibitor-segregation-explosion",
  "title": "Inhibitor segregation can make a nonexplosive reaction network explode",
  "shortTitle": "When dividing compartments throw away the brakes",
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  "oneLine": "Two simple chemistries that are safe on their own explode once dividing compartments keep the substrate together and export the inhibitor, with no inflow reactions or fusion; the exact thresholds are d + λE[log Q] < b and bλ(1 − EQ) > dc.",
  "abstract": "A stochastic chemical system explodes when infinitely many reaction events occur in finite time. Anderson, Howells and Rojas La Luz (2026) conjectured that a nonexplosive chemistry stays nonexplosive inside dividing compartments and pointed to inflow reactions as the main danger. This candidate gives two two-species chemistries with no inflow reaction, no compartment fusion and no exit that are nonexplosive alone but explode with positive probability once compartments divide at a rate proportional to their substrate, keep all substrate in one daughter and retain each inhibitor with a possibly random probability Q. For conversion inhibition (2S→3S, S→S+I, S+I→2I) explosion is possible exactly when d+λE[log Q]<b; for catalytic inhibition (I+2S→I+S) exactly when bλ(1−EQ)>dc; both boundaries are nonexplosive. The candidate proves exact conditional explosion laws, blow-up asymptotics, monotonicity and continuity at threshold, and robustness to bounded substrate leakage. Comparison with a well-mixed degradation reaction removing the same mean amount of inhibitor isolates the compartmental effect: a strictly larger explosive region for the self-reproducing inhibitor, the same threshold for the catalytic one. For a three-molecule example the explosion probability is at least 1/2 and lies in [0.79744, 0.79751]. The probabilistic components are classical and cited; Yoo (2026) reported an earlier counterexample to the unrestricted conjecture using inflow and fusion. AI-assisted, unrefereed candidate; no external peer review, independent reproduction or formal verification is claimed.",
  "datePublished": "2026-10-31",
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    "stochastic reaction networks",
    "compartment models",
    "explosion",
    "cell division",
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    "random time change",
    "Yule process",
    "birth–death processes in random environments"
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  "keyResults": [
    "Conversion inhibition (Theorem 3.1, proof): with 2S→3S at bs(s−1), S→S+I at cs and S+I→2I at dsi, d>b, the chemistry is nonexplosive alone; with division at rate λs keeping all S together and each inhibitor kept with probability p, explosion has positive probability iff d+λ log p<b, and is impossible at equality.",
    "Exact law (Theorem 3.1(f), proof): conditional on the inhibitor history, the explosion probability is the chance that fewer than s₀−1 independent coins with success probabilities e^(−bτⱼ) succeed, τⱼ being the conversion times; on explosion (T−t)S(t)→1/b (Theorem 3.3).",
    "Catalytic inhibition (Theorem 4.1, proof): with I+2S→I+S at dis(s−1), explosion has positive probability iff bλ(1−p)>dc, and the conditional probability is 1−(1−1/(bD∞))^(s₀−1).",
    "Random retention (Theorems 5.1–5.2, proof): thresholds d+λE[log Q]<b and bλ(1−EQ)>dc; two division rules with the same mean retention can put the conversion chemistry on opposite sides.",
    "What compartments add (Theorem 6.1, Corollary 6.2, Proposition 6.3, proof): removing the same mean amount of inhibitor by a well-mixed reaction S+I→S gives the strictly smaller explosive region λ(1−EQ)>d−b for conversion and the same threshold for catalysis.",
    "Monotonicity, continuity, leakage (Propositions 6.4–6.5, proof): the explosion probability falls as more inhibitor is kept, tends to zero at both thresholds, and stays positive if each division leaks a bounded number of substrate molecules.",
    "Worked example (Theorem 3.2 and Section 7.1): for b=c=1, d=2, λ=3, all inhibitor exported, from (S,I)=(3,0), P(explosion)≥1/2, and a deterministic computation with rounding control gives P(explosion)∈[0.79744, 0.79751]."
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  "relatedWorks": [
    {
      "citation": "Anderson, Howells and Rojas La Luz (2026), Stochastic Reaction Networks Within Interacting Compartments with Content-Dependent Fragmentation. Bulletin of Mathematical Biology 88, 106. The source framework, Conjecture 3.6 and Observation 3.7.",
      "url": "https://doi.org/10.1007/s11538-026-01659-7",
      "doi": "10.1007/s11538-026-01659-7"
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      "citation": "Yoo (2026), Fragmentation perturbations and explosion in interacting compartments. Zenodo preprint, version 1.1.0. An earlier counterexample to the unrestricted conjecture using inflow and fusion.",
      "url": "https://doi.org/10.5281/zenodo.22728357",
      "doi": "10.5281/zenodo.22728357"
    },
    {
      "citation": "Anonymous (2026), Critical nonexplosion and phase transitions in finite-catalyst compartment systems. Evidence Press release. The companion result: when division prevents explosion.",
      "url": "https://doi.org/10.5281/zenodo.23505123",
      "doi": "10.5281/zenodo.23505123"
    },
    {
      "citation": "Leskelä, Robert and Simatos (2010), Interacting branching processes and linear file-sharing networks. Advances in Applied Probability 42, 834–854. Yule populations with scheduled removals.",
      "url": "https://doi.org/10.1239/aap/1282924065",
      "doi": "10.1239/aap/1282924065"
    },
    {
      "citation": "Hermann and Pfaffelhuber (2020), Markov branching processes with disasters: Extinction, survival and duality to p-jump processes. Stochastic Processes and their Applications 130, 2488–2518. The logarithmic disaster threshold.",
      "url": "https://doi.org/10.1016/j.spa.2019.07.011",
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    },
    {
      "citation": "Kaplan, Sudbury and Nilsen (1975), A branching process with disasters. Journal of Applied Probability 12, 47–59.",
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      "doi": "10.1214/EJP.v18-2774"
    },
    {
      "citation": "Gallo and Roldán-Correa (2026), Immigration Processes with Binomial Catastrophes and Random Survival Parameters. arXiv:2605.24671. Random retention at catastrophes.",
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    },
    {
      "citation": "Brockwell, Gani and Resnick (1982), Birth, immigration and catastrophe processes. Advances in Applied Probability 14, 709–731.",
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    },
    {
      "citation": "Kapodistria, Phung-Duc and Resing (2016), Linear birth/immigration-death process with binomial catastrophes. Probability in the Engineering and Informational Sciences 30, 79–111.",
      "url": "https://doi.org/10.1017/S0269964815000297",
      "doi": "10.1017/S0269964815000297"
    },
    {
      "citation": "Kendall (1948), On the Generalized \"Birth-and-Death\" Process. The Annals of Mathematical Statistics 19, 1–15. The conditional branching formula.",
      "url": "https://doi.org/10.1214/aoms/1177730285",
      "doi": "10.1214/aoms/1177730285"
    },
    {
      "citation": "Cogburn and Torrez (1981), Birth and death processes with random environments in continuous time. Journal of Applied Probability 18, 19–30.",
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    },
    {
      "citation": "Huh and Paulsson (2011), Non-genetic heterogeneity from stochastic partitioning at cell division. Nature Genetics 43, 95–100. Partitioning at division as a source of variability.",
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    },
    {
      "citation": "Ethier and Kurtz (1986), Markov Processes: Characterization and Convergence. Wiley. Random time changes.",
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      "doi": "10.1002/9780470316658"
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