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.

进化可以预测吗？如果我问您是否可能会抗药新抗生素，您会说是吗？那更大的变化呢？在一百万年内，哪种动物将灭绝？新的是什么样的？人类文化本身将如何发展？这样的问题的答案对我们的生态系统和社会的弹性深深着重要。但是，此类问题在多大程度上尚不清楚。进化在理论和实践中是否可以预测？从什么？在多大程度上？这个未来的领导者奖学金将使我能够领导国际研究人员网络，以对进化可预测性的基本问题给出具体的答案。他们的答案的关键是进化融合，相似表型的重复演变或整体现象的重复演化，例如建议（但也有争议）在餐饮和splosental and Marsup mampal和Marsepial Mammamm Mammamm中的重复（也有争议）。融合提供了告诉我们我们可以从进化中期望的东西，更多相同或它的选择，这是全新的？但是，尽管已经假设了许多收敛示例，但尚无研究测量它们随进化距离变化的全部程度，因此，它们的可预测性。首次，机器学习的新计算方法将使我们能够测量整个可见现象的进化收敛程度。这项奖学金将对有史以来对各种进化群体的平行研究计划进行进化融合的最全面测试，分别是蝴蝶和飞蛾以及我们自己的进化进化枝的哺乳动物。这将比较数十万个鳞翅目照片和3D哺乳动物颅骨扫描中数十万个鳞翅目照片和图像中所有表型中收敛的程度和进化模式。这将测试在现实世界中的宏观进化多样性中最终或仅在本地可以预测的进化程度。这将为长期以来一直着迷的可预测性问题提供定量答案。除此之外，答案还将告诉我们我们可以期望有多远预测新的进化事件。这些新的见解以及为获得它们而开发的方法，将为实践进化预测提供新的途径，并通过生物医学科学到技术创新的潜在应用。作为一个未来的领导者，我将在机器学习中部署并扩展三个关键的突破。首先，深度学习方法的新应用将在多维空间中嵌入图像，衡量它们的相似性，并将其首次应用于进化（Hoyal Cuthill等人，2019年，科学进步； 2020年，自然）。其次，我将开发新的机器学习应用程序，以直接测量一种具有进化距离的表型的可预测性。然后，在进化距离的情况下，第三次创新将开发出表型图像预测的途径，结合了生成的机器学习方法。为了实现这些目标，该奖学金将建立一个围绕该研究员的国际研究人员网络，进化生物学家詹妮弗·霍伊尔·库希尔（Jennifer Hoyal Cuthill）博士由Essex和Life Sciences of Essex of Essex of Essex and Mended Inder Inder Inder Incortional contolution contolution contolution contolution contolution concolution和Data Data组成。合作项目合作伙伴和Co-I将包括Cross Labs，Cross Compass，Japan，日本的工业计算机科学家以及自然历史博物馆和剑桥动物学博物馆的世界领先收藏的专家。因此，该奖学金将资源在进化研究，开发和运用尖端技术以为基本科学问题提供新的答案中，对进化论提供了新的答案，这是可以预见的吗？洞察力的整合是由未来领导者奖学金的卓越范围来实现的，将为进化科学提供新的理论和预测框架。