压力诱导反应建模:连续状态空间中的可塑性和渐进克隆进化

IF 1.3 4区 生物学 Q3 BIOLOGY
Theory in Biosciences Pub Date : 2024-02-01 Epub Date: 2024-01-30 DOI:10.1007/s12064-023-00410-3
Anuraag Bukkuri
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引用次数: 0

摘要

癌症和细菌进化的数学模型通常源于以基因为中心的框架,假定通过获得抗性突变及其相应亚群的选择实现克隆进化。最近,人们认识到表型可塑性的作用,并建立了一些模型来解释离散细胞状态(如上皮细胞和间质细胞)之间的表型转换。然而,很少有模型同时包含可塑性和突变驱动的抗性,尤其是当状态空间是连续的且抗性以连续的方式演变时。在本文中,我们建立了一个框架,以模拟以连续渐进方式获得抗药性的可塑性和突变机制。我们利用这一框架来研究癌症和细菌种群应对压力的方式,并考虑其对治疗策略的影响。虽然我们主要是在癌症和细菌的背景下讨论我们的框架,但它广泛适用于任何能够通过可塑性和基因进化而进化的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution.

Modeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution.

Mathematical models of cancer and bacterial evolution have generally stemmed from a gene-centric framework, assuming clonal evolution via acquisition of resistance-conferring mutations and selection of their corresponding subpopulations. More recently, the role of phenotypic plasticity has been recognized and models accounting for phenotypic switching between discrete cell states (e.g., epithelial and mesenchymal) have been developed. However, seldom do models incorporate both plasticity and mutationally driven resistance, particularly when the state space is continuous and resistance evolves in a continuous fashion. In this paper, we develop a framework to model plastic and mutational mechanisms of acquiring resistance in a continuous gradual fashion. We use this framework to examine ways in which cancer and bacterial populations can respond to stress and consider implications for therapeutic strategies. Although we primarily discuss our framework in the context of cancer and bacteria, it applies broadly to any system capable of evolving via plasticity and genetic evolution.

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来源期刊
Theory in Biosciences
Theory in Biosciences 生物-生物学
CiteScore
2.70
自引率
9.10%
发文量
21
审稿时长
3 months
期刊介绍: Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.
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