Loewe Axel, Hunter Peter J, Kohl Peter
Institute of Biomedical Engineering, Karlsruher Institut für Technologie, Karlsruhe, Germany.
Bioengineering Institute, University of Auckland, Auckland, New Zealand.
Philos Trans A Math Phys Eng Sci. 2025 May 8;383(2296):20230384. doi: 10.1098/rsta.2023.0384.
Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic versus data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable clinical trials. Disease-specific parametrization, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardized and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical and highly controlled approach provided by methods has become an integral part of physiological and medical research. methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic and therapeutic approaches.This article is part of the theme issue 'Science into the next millennium: 25 years on'.
自千禧年之交以来，生物系统的计算建模有了显著发展，在基础研究和临床研究中都得到了成熟应用。虽然千禧年前后关于“还原论与整合论”优缺点的争论话题如今似乎争议较小，但一种新的明显二分法主导了讨论：机制建模与数据驱动建模。鉴于这种区别，我们概述了近期的成就和新挑战，重点是心血管系统。关注点已从生成通用的人类模型转向个体人类模型（数字孪生）或代表临床人群的整个模型队列，以开展临床试验。疾病特异性参数化、个体间和个体内变异性、不确定性量化以及可互操作、标准化和质量可控的数据是当今的重要问题，这需要开放工具、数据和元数据标准，以及强大的社区互动。这些方法提供的定量、生物物理和高度可控方法已成为生理和医学研究的一个组成部分。这些方法还有潜力在集成多物理建模、多尺度模型、虚拟队列研究和机器学习等领域加速未来进展，超越目前可行的范围。事实上，机制建模和数据驱动建模可以相互协同补充，推动未来人工智能应用，以增进我们对生理和疾病机制的理解，生成新假设并评估其合理性，从而为预防、诊断和治疗方法的发展做出贡献。本文是主题为“科学进入下一个千年：25年后”的一部分。
