Error-induced extinction in a multi-type critical birth-death process.

IF 2.2 4区 数学 Q2 BIOLOGY Journal of Mathematical Biology Pub Date : 2024-09-02 DOI:10.1007/s00285-024-02134-4
Meritxell Brunet Guasch, P L Krapivsky, Tibor Antal
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Abstract

Extreme mutation rates in microbes and cancer cells can result in error-induced extinction (EEX), where every descendant cell eventually acquires a lethal mutation. In this work, we investigate critical birth-death processes with n distinct types as a birth-death model of EEX in a growing population. Each type-i cell divides independently ( i ) ( i ) + ( i ) or mutates ( i ) ( i + 1 ) at the same rate. The total number of cells grows exponentially as a Yule process until a cell of type-n appears, which cell type can only divide or die at rate one. This makes the whole process critical and hence after the exponentially growing phase eventually all cells die with probability one. We present large-time asymptotic results for the general n-type critical birth-death process. We find that the mass function of the number of cells of type-k has algebraic and stationary tail ( size ) - 1 - χ k , with χ k = 2 1 - k , for k = 2 , , n , in sharp contrast to the exponential tail of the first type. The same exponents describe the tail of the asymptotic survival probability ( time ) - ξ k . We present applications of the results for studying extinction due to intolerable mutation rates in biological populations.

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多类型临界生灭过程中的错误诱导消亡。
微生物和癌细胞中的极端突变率会导致错误诱导灭绝(EEX),即每个后代细胞最终都会获得致命突变。在这项工作中,我们研究了具有 n 种不同类型的临界出生-死亡过程,以此作为不断增长的种群中 EEX 的出生-死亡模型。每个 i 型细胞以相同的速率独立分裂(i )→(i )+(i )或突变(i )→(i + 1)。细胞总数以尤勒过程的形式呈指数增长,直到出现一个 n 型细胞,这种细胞只能以 1 的速率分裂或死亡。这使得整个过程变得非常关键,因此在指数增长阶段之后,所有细胞最终都会以 1 的概率死亡。我们提出了一般 n 型临界生死过程的大时间渐近结果。我们发现,当 k = 2 , ⋯ , n 时,k 型细胞数的质量函数具有代数和静止的尾部 ( 大小 ) - 1 - χ k ,其中 χ k = 2 1 - k,这与第一种类型的指数尾部形成鲜明对比。同样的指数描述了渐近生存概率 ( 时间 ) - ξ k 的尾部。我们将这些结果应用于研究生物种群中由于无法忍受的突变率而导致的灭绝。
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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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