Give biologists the space to be curious, not just better tools
摘要
弗朗西斯·克里克研究所所长在研究所成立十周年、自己履职首年之际撰文指出,生命科学正变得日益多学科交叉,AI 能整合跨尺度复杂数据集,Google DeepMind 的 AlphaFold 已向全球研究者开放数百万个蛋白质结构预测。他强调,AI 识别出的模式仍需由人审视并通过实验验证,科研环境应围绕人与科学问题而非机构划分来组织。
摘要由模型根据原文撰写,以原文为准。
正文 · 原文
As the Francis Crick Institute in London celebrates its tenth anniversary and I look back on my first year as chief executive, I find myself reflecting on both how much has been achieved and how profoundly life science is changing.
As an epigeneticist, I can safely say that history and context matter. When I began my scientific career in the 1980s, experiments were painstaking, time-consuming, expensive and repetitive. Generating a knockout mouse or sequencing a gene could take weeks or months, and required resources that were available at few institutions.
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Biology has since entered an extraordinary era. It is increasingly multidisciplinary, and researchers have an unprecedented range of technologies at their disposal, from single-cell and spatial biology techniques to advanced imaging, genome editing, tissue engineering and computational approaches.
Artificial intelligence is also transforming science. Its ability to bring together complex data sets offers fresh ways to connect the study of biology across scales, from molecules and cells to individual organisms and populations. It is also enabling the democratization of discovery and knowledge. Google DeepMind’s AlphaFold system, for example, has made predictions of millions of protein structures available to researchers worldwide.
Biologists are poised to at last understand the complexity of life: the influence of the environment, ageing and sex, alongside genetics, on the differences between us. Among the questions to which answers are within reach are how the genetic code gives rise to a living organism; how the control and expression of genes varies during development; how tissues communicate and bodily functions depend on one another; and why diseases develop differently in different people and treatments work for some but not others.
Could measuring immune health reshape medical care?
Reductionism has served biology well, giving us a deep mechanistic understanding of life, health and disease. But many features of living systems cannot be predicted from their individual parts alone. They emerge from interactions between components and depend on the context in which those interactions occur.
Science needs both reductionist and holistic approaches: connecting molecular mechanisms and signalling pathways with inter-organ communication and physiology, while understanding how these systems respond and adapt to environmental challenges. And performing such studies requires research environments that allow scientists to think and work across conventional boundaries.
As director of the Crick, I have seen the incredible value of an environment that is organized around people and scientific questions rather than institutional divisions. Shared infrastructure and specialist teams enable research to cross disciplines and deploy technologies in new ways. Connections between fundamental research, clinical science and industry also help discoveries to move towards real-world applications, swiftly and efficiently.
But even the most sophisticated technologies and research environments are only as powerful as the scientists using them. Creating an environment in which people from diverse backgrounds and with different life experiences are encouraged to perform bold, rigorous science and harness the power of AI will be key to future discoveries. Patterns identified by AI need to be scrutinized by people and validated through experiment.
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来源:Nature · Machine Learning 主题 · nature.com
引用原文
Nature · Machine Learning 主题. Give biologists the space to be curious, not just better tools[EB/OL]. (2026-11-11)[访问日期]. https://www.nature.com/articles/d41586-026-03669-w.

