标题： 生物系统的计算建模现在与过去：重温本世纪初的工具和愿景 显示英文标题

标题： Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century

摘要： 自21世纪以来，生物系统的计算建模取得了显著发展，并在基础和临床研究中得到了成熟的运用。 尽管关于“还原论与整合论”优缺点的千年之交的争论如今似乎不再那么有争议，但一个新的明显二分法主导了讨论：机制模型与数据驱动模型。 鉴于这一区别，我们综述了近期取得的成就和新的挑战，重点放在心血管系统上。 注意力已从生成一个人类的通用模型转向个体人类的模型（数字孪生）或代表临床人群的整个模型队列，以实现计算机模拟的临床试验。 疾病特异性参数化、个体间和个体内的变异性、不确定性量化以及可互操作、标准化和质量控制的数据是当今的重要问题，这需要开放的工具、数据和元数据标准，以及强大的社区互动。 计算机模拟方法提供的定量、生物物理和高度受控的方法已成为生理学和医学研究不可或缺的一部分。 计算机模拟方法在集成多物理场建模、多尺度模型、虚拟队列研究和机器学习等领域具有加速未来进步的潜力，这比目前可行的更为广阔。 事实上，机制模型和数据驱动模型可以协同互补，并推动明天的人工智能应用，以进一步理解生理学和疾病机制，生成新假设并评估其合理性，从而促进预防、诊断和治疗方法的演变。 显示英文摘要

摘要： 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 vs. 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 the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parameterisation, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardised, 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 in silico methods has become an integral part of physiological and medical research. In silico 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.