标题： 多相控制机制对成纤维细胞动力学的影响：一种分段数学建模方法 显示英文标题

标题： Effects of multi-phase control mechanism on fibroblast dynamics: A segmented mathematical modeling approach

摘要： 细胞大小是细胞生理学的基本决定因素，影响生长、分裂和功能等过程。 在本研究中，我们开发了一个分段的数学框架，以研究在细胞周期多个阶段运作的不同控制机制如何影响成纤维细胞群体动力学。 在我们之前研究的尺寸调节、时间调节和增加调节策略的基础上，我们通过引入S期和G2期的特定阶段控制方案，扩展了分析，结合了非线性生长动力学和细胞死亡。 使用基于代理的随机模拟，我们研究了这些机制如何塑造稳态大小分布并响应参数变化。 我们的结果揭示了稳态细胞大小分布主要由分裂核和特定阶段的控制策略所决定，并对细胞死亡方式表现出显著的鲁棒性。 我们发现了一种外源性和内源性生长反馈之间的根本权衡：虽然依赖于种群密度的调控严格限制了总细胞数量，而依赖于细胞大小的调控则作为一种比例稳态机制，抑制了相对大小的变异性。 此外，我们证明了通过保留具有增殖能力的大细胞，可以加速种群恢复。 这项研究提供了关于生长、分裂和稳态之间复杂相互作用的生物学相关见解，对理解组织修复和疾病进展具有重要意义。 显示英文摘要

摘要： Cell size is a fundamental determinant of cellular physiology, influencing processes such as growth, division, and function. In this study, we develop a segmented mathematical framework to investigate how different control mechanisms operating across multiple phases of the cell cycle affect fibroblast population dynamics. Building on our previous work modeling sizer, timer, and adder strategies, we extend the analysis by introducing phase-specific control schemes in the S and G2 phases, incorporating nonlinear growth dynamics and cell death. Using agent-based stochastic simulations, we examine how these mechanisms shape steady-state size distributions and respond to parameter variations. Our results reveal that the steady-state cell size distribution is primarily governed by division kernels and phase-specific control strategies, and appears remarkably robust to cell death modalities. We identify a fundamental trade-off between extrinsic and intrinsic growth feedbacks: while population-density-dependent regulation tightly limits total cell numbers, cell-size-dependent regulation acts as a proportional homeostatic mechanism, suppressing relative size variability. Furthermore, we demonstrate that population recovery is accelerated by the retention of proliferation-competent large cells. This study provides biologically relevant insights into the complex interplay between growth, division, and homeostasis, with implications for understanding tissue repair and disease progression.