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Genome replication in asynchronously growing microbial populations

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Florian Pflug, Deepak Bhat, Simone Pigolotti

Subcellular Processes · Submitted 24 Aug 2023

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

The authors' main claim In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program.

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arXiv ID2308.12664 (version 2)
Pith ID5XJ4PEKP, Pith's permanent record of this paper and its review
CitationDeepak Bhat, Florian Pflug, Simone Pigolotti. "Genome replication in asynchronously growing microbial populations." arXiv:2308.12664 (2023). https://pith.science/paper/5XJ4PEKP
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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-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 7,352 input tokens.

What the abstract itself states, as probabilities: claims first 0.10 · states limitation 0.19 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.39 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory to explain these data is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.

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 Biological cells replicate their genomes in a well-planned manner. background 1.00 0.04 0.02 asserted 1.00
s1 The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. background 1.00 0.04 0.03 asserted 1.00
s2 Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. background 1.00 0.05 0.03 hedged possibility 0.79
s3 This abundance pattern can be experimentally measured using deep sequencing. background 0.77 0.09 0.02 asserted 0.90
s4 However, a general quantitative theory to explain these data is still lacking. gap 1.00 0.05 0.02 asserted 1.00
s5 In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. contribution 0.93 0.94 0.22 asserted 0.98
s6 As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. contribution 0.58 0.93 0.05 asserted 0.99
s7 In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. result 0.97 0.95 0.54 asserted 1.00
s8 In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. result 1.00 0.94 0.16 asserted 0.99
s9 These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes. interpretation 1.00 0.61 0.26 asserted 0.86

What kind of paper the classifier reports this is: theoretical result 0.68 · evidence from proof or derivation 0.67.

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  • references and citing sentences
  • relations to other papers
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  • C citations
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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

Biological cells replicate their genomes in a well-planned manner. The DNA replication program of an organism determines the timing at which different genomic regions are replicated, with fundamental consequences for cell homeostasis and genome stability. Qualitatively, in a growing cell culture, one expects that genomic regions that are replicated early should be more abundant than regions that are replicated late. This abundance pattern can be experimentally measured using deep sequencing. However, a general quantitative theory to explain these data is still lacking. In this paper, we predict the abundance of DNA fragments in asynchronously growing cultures from any given stochastic model of the DNA replication program. As key examples, we present stochastic models of the DNA replication programs in Escherichia coli and in budding yeast. In both cases, our approach leads to analytical predictions that are in excellent agreement with experimental data and permit to infer key information about the replication program. In particular, our method is able to infer the locations of known replication origins in budding yeast with high accuracy. These examples demonstrate that our method can provide insight into a broad range of organisms, from bacteria to eukaryotes.

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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

What it cites

Cites as background 1 paper
  • This assumption is sometimes referred to as “identity at birth” in the literature on renewal processes [ref] : all genomes are born identical, and the differences that they might develop as they synthesize can be seen as independent outcomes of the same stochastic process.

    Population growth in discrete time: a renewal equation oriented survey · Multiple genome types · 0.89

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