计算系统生物学的下一步是什么?

Eberhard O. Voit, Ashti M. Shah, Daniel Olivença, Yoram Vodovotz
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引用次数: 0

摘要

几十年前,计算系统生物学在很大程度上还不为人所知,现在它已成为生物学和医学研究的核心方法论。这种惊人的上升提出了一个问题,即社区下一步应该做什么。这篇文章概述了我们对计算系统生物学未来的个人愿景,建议需要解决思维方式和方法。我们通过关注当前和预期的研究目标、强大计算工具的开发、可能的突出应用、下一代科学家的教育以及向公众推广来呈现这一愿景。在我们看来,近年来出现了两类广泛的研究目标,并将指导未来的努力。第一个目标是越来越大和越来越复杂的计算模型,旨在解决新出现的与健康有关的挑战,例如真实的全细胞和器官模型、疾病模拟器和数字双胞胎、计算机临床试验以及治疗药物开发背景下的临床转化应用。这种大型模型还将引导我们找到解决农业和环境可持续性等紧迫问题的办法,包括充足的粮食供应和不断变化的栖息地中的生命。第二个目标是深刻理解自然界解决问题的系统设计和策略的本质。这种理解将有助于我们解释观察到的生物结构,并指导对合成生物系统的探索。在有效的方法方面,我们建议努力实现从原始生物医学数据到时空机制模型的自动化数据管道。这些将得到动态统计方法、机器学习、人工智能和动态模型设计的精简策略的支持,在建模现实的复杂性和抽象的简单性之间取得良好的平衡。最后,我们建议需要协调一致,在社区范围内强调有效的系统生物学教育,作为正式教学和实践指导的结合来实施。教育工作还应通过书籍、博客、社会媒体和互动网络机会向公众推广,最终目标是培训最先进的技术,同时重新找回失去的综合艺术。
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What’s next for computational systems biology?
Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the next-generation of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis.
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