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Catalytic Buffering for Optimal Scheduling of Self-Replication

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Rami Pugatch

Molecular Networks · Submitted 18 Sep 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show that by maintaining a sufficiently large inventory of intermediate metabolites and catalysts required for self-replication, optimal replication times can be achieved by a family of random scheduling algorithms that are biochemically feasible, for which catalysts never idle if they can perform de-novo bio-synthesis.

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arXiv ID1409.5182 (version 1)
Pith IDBJDYDW4Z, Pith's permanent record of this paper and its review
CitationRami Pugatch. "Catalytic Buffering for Optimal Scheduling of Self-Replication." arXiv:1409.5182 (2014). https://pith.science/paper/BJDYDW4Z
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,068 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.11 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.03 · states quantitative result 0.21 · compares with prior results 0.20 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We study the scheduling problem of a self-replicating factory. We show that by maintaining a sufficiently large inventory of intermediate metabolites and catalysts required for self-replication, optimal replication times can be achieved by a family of random scheduling algorithms that are biochemically feasible, for which catalysts never idle if they can perform de-novo bio-synthesis. Optimally scheduled self-replication is facilitated by allowing several production lines to run in parallel. The excess inventory of catalysts and substrates decouples these lines, while dynamical balancing tunes average and variance completion, resulting in an overall universal distribution for the replication times belonging to the generalized extreme value family. We discuss biological implications and postulate that bacteria that are tuned by evolution for fast replication employ this natural scheduling strategy to achieve optimal asymptotic growth rates by stoichiometrically balancing the amount of work in progress thus globally controlling the number of parallel basic self-replicating units within them. Analysis of recently measured data of E. coli growth in rich media shows data-collapse on a single universal curve consistent with our prediction, suggesting wild type E. coli optimally schedule its replication.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We study the scheduling problem of a self-replicating factory. background 0.55 0.83 0.03 asserted 0.75
s1 We show that by maintaining a sufficiently large inventory of intermediate metabolites and catalysts required for self-replication, optimal replication times can be achieved by a family of random scheduling algorithms that are biochemically feasible, for which catalysts never idle if they can perform de-novo bio-synthesis. result 0.92 0.94 0.76 asserted 1.00
s2 Optimally scheduled self-replication is facilitated by allowing several production lines to run in parallel. result 0.96 0.73 0.12 asserted 0.95
s3 The excess inventory of catalysts and substrates decouples these lines, while dynamical balancing tunes average and variance completion, resulting in an overall universal distribution for the replication times belonging to the generalized extreme value family. result 0.94 0.83 0.19 asserted 1.00
s4 We discuss biological implications and postulate that bacteria that are tuned by evolution for fast replication employ this natural scheduling strategy to achieve optimal asymptotic growth rates by stoichiometrically balancing the amount of work in progress thus globally controlling the number of parallel basic self-replicating units within them. interpretation 1.00 0.19 0.19 speculative 0.90
s5 Analysis of recently measured data of E. coli growth in rich media shows data-collapse on a single universal curve consistent with our prediction, suggesting wild type E. coli optimally schedule its replication. result 0.89 0.66 0.25 hedged possibility 0.97

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from analysis of existing data 0.64.

What was examined, and what was not

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  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We study the scheduling problem of a self-replicating factory. We show that by maintaining a sufficiently large inventory of intermediate metabolites and catalysts required for self-replication, optimal replication times can be achieved by a family of random scheduling algorithms that are biochemically feasible, for which catalysts never idle if they can perform de-novo bio-synthesis. Optimally scheduled self-replication is facilitated by allowing several production lines to run in parallel. The excess inventory of catalysts and substrates decouples these lines, while dynamical balancing tunes average and variance completion, resulting in an overall universal distribution for the replication times belonging to the generalized extreme value family. We discuss biological implications and postulate that bacteria that are tuned by evolution for fast replication employ this natural scheduling strategy to achieve optimal asymptotic growth rates by stoichiometrically balancing the amount of work in progress thus globally controlling the number of parallel basic self-replicating units within them. Analysis of recently measured data of E. coli growth in rich media shows data-collapse on a single universal curve consistent with our prediction, suggesting wild type E. coli optimally schedule its replication.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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  • While the questions and modeling challenges in this context have been long appreciated [ref] , there is renewed interest [ref] due to the recent availability of large datasets for single cell growth trajectories and cell divisions, made possible by major breakthroughs in single cell technologies for unicellular organisms [ref] .

    First Passage processes in cellular biology · Biological context. · 0.86

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