本文回顾了细胞周期计算建模的当前知识和最新进展。它提供了各种建模范例的比较分析，突出了它们独特的优势、局限性和应用。具体来说，本文比较了确定性模型和随机模型、单细胞模型与群体模型以及机械模型与抽象模型。这种详细的分析有助于确定最适合各种研究需求的建模框架。此外，讨论还扩展到利用这些计算模型来阐明细胞周期动力学，特别关注细胞周期活力、与信号通路的串扰、肿瘤微环境、DNA复制和修复机制，强调它们在肿瘤进展和修复中的关键作用。癌症治疗的优化。通过将这些模型应用于癌症治疗规划的关键方面，以获得更好的结果，包括药效量化、药物发现、耐药性分析和剂量优化，该综述强调了计算见解在提高癌症治疗的精度和有效性方面的巨大潜力。对计算建模和治疗策略开发之间复杂关系的强调强调了先进建模技术在处理细胞周期动态的复杂性及其对癌症治疗的影响方面的关键作用。© 2024。作者。

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.© 2024. The Author(s).