Protein Noise and Distribution in a Two-Stage Gene-Expression Model Extended by an mRNA Inactivation Loop

Çelik, Candan; Bokes, Pavol; Singh, Abhyudai

doi:10.1007/978-3-030-85633-5_13

Cited by 2 publications

(2 citation statements)

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“…By the first relation in (13), the population n(t) increases eventually exponentially with the rate constant (8); the initial condition and the low-population noise affects the large-time behaviour of the population only through the random pre-exponential factor W (x 0 , v 0 ) (Figure 2, right panel). By the second relation in (13), the distribution of protein concentration and cell volume among a large population is (nearly) nonrandom and given by ( 9)-(10) (Figure 3).…”

Section: Population Model: a Measure-valued Markov Processmentioning

confidence: 99%

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Joint Distribution of Protein Concentration and Cell Volume Coupled by Feedback in Dilution

Zabaikina

Bokes

Singh

2023

Preprint

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We consider a protein that negatively regulates the rate with which a cell grows. Since less growth means less protein dilution, this mechanism forms a positive feedback loop on the protein concentration. We couple the feedback model with a simple description of the cell cycle, in which a division event is triggered when the cell volume reaches a critical threshold. Following the division we either track only one of the daughter cells (single cell framework) or both cells (population framework). For both frameworks, we find an exact time-independent distribution of protein concentration and cell volume. We explore the consequences of dilution feedback on ergodicity, population growth rate, and the bias of the population distribution towards faster growing cells with less protein.

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Section: Population Model: a Measure-valued Markov Processmentioning

confidence: 99%

“…Degradation removes one molecule at a time and leads to tractable models with explicit steady state distributions [13,24,32]. Partitioning based depletion requires that a model for the cell cycle be combined with the model for gene expression [17,42].…”

Section: Introductionmentioning

confidence: 99%