Is evolution predictable? Unlocking fundamental biological insights using new machine learning methods

进化是可预测的吗？

• 批准号: MR/X033880/1
• 负责人: Jennifer Hoyal Cuthill
• 金额: $ 182.98万
• 依托单位: University of Essex
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX033880%2F1
• 关键词: evolution; predictable; Unlocking; fundamental; biological

Is evolution predictable? If I asked you whether a new antibiotic might face resistance, would you say yes? What about larger changes? Which animal species will be extinct in a million years, or ten? What will new ones look like? How will human culture itself have evolved? The answers to questions like these matter deeply for the resilience of our ecosystem and society. But it is not yet known to what degree such questions are answerable. Is evolution predictable in theory and practice? From what? To what extent?This Future Leaders Fellowship will empower me to lead an international network of researchers to give concrete answers to fundamental questions of evolutionary predictability.A key to their answers is evolutionary convergence, the repeated evolution of similar phenotypes, or overall phenomes, like the suggested (but also disputed) repetition of body forms in placental and marsupial mammals. Convergence offers to tell us what we can expect from evolution, more of the same, or its alternative, something entirely new? But, while many examples of convergence have been hypothesised, no study has measured the full extent to which they vary with evolutionary distance and, therefore, just how predictable they are. For the first time, new computational methods of machine learning will allow us to measure the extent of evolutionary convergence across entire visible phenomes. This fellowship will undertake the most comprehensive tests of evolutionary convergence ever performed, across two parallel research programs on diverse evolutionary groups, the butterflies and moths and the mammals, our own evolutionary clade. This will compare the extent and evolutionary patterns of convergence in all phenotype visible among hundreds of thousands of lepidopteran photographs and images from 3D mammal skull scans. This will test the extent to which evolution is ultimately, or only locally, predictable across real-world macroevolutionary diversifications.This will give quantitative answers to questions of predictability that have long fascinated humanity. Beyond this, the answers will tell us how far we can expect to predict new evolutionary events. These new insights, and the methods developed to gain them, will provide new avenues for practical evolutionary prediction, with potential applications from biomedical science to technological innovation.To deliver these insights, as a Future Leaders Fellow, I will deploy and extend three key breakthroughs in machine learning. First, new applications of deep-learning methods will embed images in multidimensional spaces, measuring their similarity, leading their first applications to evolution (Hoyal Cuthill et al., 2019, Science Advances; 2020, Nature). Second, I will develop new machine learning applications to directly measure the predictability of one phenotype from another with evolutionary distance. The third innovation will then develop avenues for phenotypic image prediction given evolutionary distance, incorporating generative machine learning methods.To achieve these aims, this fellowship will build an international network of researchers around the Fellow, evolutionary biologist Dr Jennifer Hoyal Cuthill, hosted by the School of Life Sciences at the University of Essex and mentored by leading evolutionary, ecological and data scientists. Collaborative Project Partners and Co-I will include industrial computer scientists at Cross Labs, Cross Compass, Japan and experts on world-leading collections at the Natural History Museum and University of Cambridge Zoology Museum. This fellowship will, thereby, resource a sea-change in evolutionary research, developing and applying cutting-edge technology to provide new answers to the fundamental scientific question, is evolution predictable? Integration of the insights, enabled by the exceptional scope of the Future Leaders Fellowship, will provide new theoretical and predictive frameworks for evolutionary science.

进化是可预测的吗？如果我问你一种新的抗生素是否可能面临耐药性，你会说是吗？更大的改变又如何呢？哪些动物物种将在一百万年或十年内灭绝？新的会是什么样子？人类文化本身将如何演变？此类问题的答案对于我们的生态系统和社会的恢复力至关重要。但目前尚不清楚这些问题在多大程度上可以得到回答。进化论在理论和实践上是可预测的吗？从什么？到什么程度？这个未来领袖奖学金将使我能够领导一个国际研究人员网络，对进化可预测性的基本问题给出具体答案。他们答案的关键是进化趋同，相似表型或整体现象的重复进化，例如胎盘类和有袋类哺乳动物的身体形态的重复（但也有争议）。趋同告诉我们，我们可以从进化中期待什么，是更多相同的东西，还是它的替代方案，一些全新的东西？但是，虽然已经假设了许多趋同的例子，但没有研究测量它们随进化距离而变化的全部程度，以及它们的可预测性。机器学习的新计算方法将首次使我们能够测量整个可见现象的进化收敛程度。该奖学金将进行有史以来最全面的进化趋同测试，涉及两个并行的研究项目，涉及不同的进化群体，蝴蝶和飞蛾以及哺乳动物，我们自己的进化分支。这将比较数十万张鳞翅目照片和 3D 哺乳动物头骨扫描图像中所有可见表型的趋同程度和进化模式。这将测试现实世界的宏观进化多样化中进化最终或仅局部可预测的程度。这将为长期困扰人类的可预测性问题提供定量答案。除此之外，答案将告诉我们我们可以在多大程度上预测新的进化事件。这些新见解以及为获得它们而开发的方法将为实际进化预测提供新途径，并具有从生物医学科学到技术创新的潜在应用。为了提供这些见解，作为未来领袖研究员，我将部署和扩展三个关键突破在机器学习中。首先，深度学习方法的新应用将把图像嵌入多维空间，测量它们的相似性，从而首次应用于进化（Hoyal Cuthill et al., 2019, Science Advances; 2020, Nature）。其次，我将开发新的机器学习应用程序，以直接测量一种表型与另一种表型的进化距离的可预测性。第三项创新将结合生成机器学习方法，开发给定进化距离的表型图像预测的途径。为了实现这些目标，该奖学金将围绕研究员、进化生物学家 Jennifer Hoyal Cuthill 博士建立一个由学院主办的国际研究人员网络埃塞克斯大学生命科学博士，由领先的进化、生态和数据科学家指导。合作项目合作伙伴和 Co-I 将包括来自日本 Cross Compass 实验室的工业计算机科学家以及自然历史博物馆和剑桥大学动物博物馆的世界领先藏品专家。因此，这项奖学金将为进化研究、开发和应用尖端技术的巨变提供资源，为基本科学问题“进化是否可预测”提供新的答案。未来领袖奖学金的特殊范围促成了见解的整合，将为进化科学提供新的理论和预测框架。