Collaborative Research: RoL: Deep-learning framework to quantify emergent phenotypes for functional gene annotation

这项工作的目标是利用机器学习中的最新优势，将细菌生物膜中细胞的集体行为与它们潜在的遗传网络联系起来。细胞如何自组织成复杂的组织是现代发育生物学中最大的谜题之一，也是新兴行为的标志性例子--由更简单的相互作用的成分产生的复杂模式。尽管取得了巨大的进步，但即使是研究最充分的模型系统，也缺乏对将发育现象从分子连接到细胞、组织和最终完成有机体的新特性的理解。由土壤细菌黄色粘球菌形成的生物膜是研究突现行为的一个很好的模型系统。在饥饿的情况下，黄花莲的生物膜启动了一个发育程序，在这个程序中，细胞聚集成土堆，然后分化成不同的细胞类型。许多影响黄花根结线虫发育的基因已经被识别出来，但研究人员缺乏系统地了解它们在协调自组织动态中的作用。该项目旨在通过基于机器学习的量化基因中断的发展影响，将基因和紧急行为联系起来。在这个项目中制定的方法可望广泛适用。通过联合会议和受训人员合作等密切互动，为所有参与实验室的学生提供培训机会，将进一步加强该提案的更广泛影响。此外，项目外展将包括将3D打印显微镜带入AP生物学高中教室的合作努力。将基因类型与新出现的多细胞表型联系起来是21世纪生物学面临的重大挑战之一。缺乏可靠的指标来量化遗传扰动对涌现模式的影响，这严重阻碍了我们取得进展的能力，即使是对于相对简单的模型系统，如粘菌黄色。存在三个主要问题：(1)单个细胞的运动天生是随机的，它们的集体涌现模式在实验重复之间显示出显著的差异；(2)在发育过程中表现出的涌现模式是不可预测的，并且对环境条件的变化极其敏感；(3)突变菌株的发育表型往往是微妙的，难以表征和量化。在这些问题得到解决之前，可能很难将突变的表型影响与随机性和环境敏感性的影响分开。值得注意的是，这些问题并不是黄色微囊藻独有的，因此他们的解决方案有可能在许多表现出紧急多细胞行为的不同生物系统中产生变革。深度学习在计算机视觉中应用的最新进展表明，这些方法在处理类似问题方面具有强大的能力。因此，开发的方法有望应用于广泛的模型系统，就像基于深度学习的图像量化方法正被应用于来自不同领域的大量图像一样。这项工作由集成组织系统(IOS)、分子细胞生物学(MCB)和生命规则(ROL)风险基金联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this work is to leverage recent advantages in machine learning to connect the collective behavior of cells in a bacterial biofilm to their underlying genetic networks. How cells self-organize into complex tissues is one of the greatest puzzles in modern developmental biology and a hallmark example of emergent behavior - complex patterns arising from simpler interacting components. Despite tremendous progress, even the best-studied model systems lack an understanding of the emergent properties that bridge developmental phenomena from molecules to cells, tissues, and eventually complete organisms. Biofilms formed by the soil bacterium Myxococcus xanthus are a great model system to study emergent behavior. Under starvation, an M. xanthus biofilm initiates a developmental program during which cells aggregate into mounds and then differentiate into distinct cell types. Many of the genes that influence M. xanthus development have been identified, but researchers lack metrics to systematically understand their role in coordinating self-organization dynamics. This project aims to link genes and emergent behavior through machine-learning-based quantification of the developmental impact of gene disruptions. The methodology developed in this project is expected to be broadly applicable. Broader impacts of the proposal will be further enhanced by training opportunities for students for all participating laboratories, facilitated by close interactions such as joint meetings and trainee collaborations. Furthermore, project outreach will include collaborative efforts to bring 3D-printed microscopes into AP Biology high school classrooms. Connecting genotypes to emergent multicellular phenotypes is one of the grand challenges of 21st century biology. The lack of robust metrics that quantify the effects of genetic perturbations on emergent patterns significantly impedes our ability to make progress even for relatively simple model systems such as Myxococcus xanthus. Three major problems exist: (1) individual cell movements are inherently stochastic, and their collective emergent patterns display significant variations between experimental replicates; (2) emergent patterns displayed during development are unpredictable and extremely sensitivity to changes in environmental conditions; (3) developmental phenotypes of mutant strains are often subtle and difficult to characterize and quantify. Until these problems are addressed, it may be difficult to separate the phenotypic impact of mutation from the effects of stochasticity and environmental sensitivity. Notably, these problems are not unique to M. xanthus, and therefore their solution has the potential to be transformative across many different biological systems that display emergent multicellular behaviors. Recent advances in application of deep learning in computer vision have demonstrated the power of these approaches to deal with similar problems. Therefore, developed approaches are expected to apply to a wide range of model systems, just as deep-learning-based image quantification methods are being applied to a vast array of images from a variety of fields.This work is jointly funded by Integrated Organismal Systems (IOS), Molecular Cell Biology (MCB) and the Rules of Life (RoL) venture fund.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.