Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Department of Biology, University of San Diego, San Diego, CA, USA.
Integr Comp Biol. 2022 Feb 5;61(6):2011-2019. doi: 10.1093/icb/icab114.
The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast datasets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.
人类面临的生物挑战是复杂的、多因素的,与我们的健康、福利以及地球管理的未来息息相关。解决农业、生态学和医疗保健等不同领域的问题需要将包含众多组成部分和时空尺度的大量数据集联系起来。在这里,我们提供了一个新的框架和路线图,用于使用实验和计算来理解跨越多个尺度的动态生物系统。我们讨论了有助于理解复杂生物系统的理论,并强调了现有方法的局限性,并建议了数据生成实践。大数据分析和人工智能等新技术的出现可以帮助弥合不同的尺度和数据类型之间的差距。我们建议如何使这些模型具有透明性,与生物功能的现有理论兼容,并使先进的机器学习算法能够读取生物数据集。总的来说,应对紧迫的生物挑战的障碍不仅是技术上的,也是社会学上的。因此,我们还为促进科学家之间的跨学科互动提供了建议。