CAREER: Learning stochastic spatiotemporal dynamics in single-molecule genetics

测量哪些基因在细胞中表达的能力彻底改变了我们对生物系统的理解。发现的范围从精确定位是什么使不同的细胞类型独特（例如，皮肤与脑细胞），以了解疾病是如何从基因突变中产生的。这种基因表达数据现在是每个细胞生物学家工具箱中普遍使用的工具。然而，从这些数据中可靠地提取洞察力的数学理论已经落后于收获它的技术的惊人进步。这个CAREER项目将为分析基因表达的成像数据奠定关键的理论基础。这些进展从理论到实践，包括开发数学模型和机器学习方法，这些方法将用于实验合作者的数据。总而言之，该项目旨在为研究基因表达的空间成像数据创造一个新的技术黄金标准，并揭示新的生物学和生物医学见解。此外，这项拟议的研究将纳入跨学科的研究生和当地社区学院的本科生，以培养数据科学，生物学和数学不断发展的交叉领域的下一代科学家。 除了研究活动，该项目还将创建导师网络，以支持第一代学生科学家追求STEM劳动力的多样化。支持的研究是通过随机反应扩散模型的透镜研究单分子基因表达空间模式的综合计划。主要目的是概括这些模型和它们的观测之间的数学联系作为空间点过程。新的理论将纳入必要的因素来描述空间基因表达在亚细胞和多细胞尺度，包括各种反应，空间运动和几何效应。这个项目还将建立推断的统计理论，由此产生的逆问题，推断随机率仅从单个粒子位置的快照。对参数可识别性、最优实验设计和模型选择的调查将确保可靠和可靠的推断。作为对已开发理论的补充，该项目将实施和基准测试用于有效执行大规模统计推断的尖端方法，包括变分贝叶斯蒙特卡罗和物理信息神经网络。 这项工作的成果将被打包成开源软件，从多基因组织规模的数据集推断可解释的生物物理参数。这个CAREER奖是由数学科学部的数学生物学和统计学项目和分子细胞生物科学部的细胞动力学功能集群共同资助&&的，该奖项反映了NSF的法定使命，并被认为是值得通过评估使用基金会的智力价值和更广泛的支持影响审查标准。

The ability to measure which genes are expressed in cells has revolutionized our understanding of biological systems. Discoveries range from pinpointing what makes different cell types unique (e.g., a skin vs. brain cell) to how diseases emerge from genetic mutations. This gene expression data is now a ubiquitously used tool in every cell biologist’s toolbox. However, the mathematical theories for reliably extracting insight from this data have lagged behind the amazing progress of the techniques for harvesting it. This CAREER project will develop key theoretical foundations for analyzing imaging data of gene expression. The advances span theory to practice, including developing mathematical models and machine-learning approaches that will be used with data from experimental collaborators. Altogether, the project aims to create a new gold standard of techniques in studying spatial imaging data of gene expression and enable revelation of new biological and biomedical insights. In addition, this proposed research will incorporate interdisciplinary graduate students and local community college undergraduates to train the next generation of scientists in the ever-evolving intersection of data science, biology, and mathematics. Alongside research activities, the project will create mentorship networks for supporting first-generation student scientists in pursuit of diversifying the STEM workforce. The supported research is a comprehensive program for studying single-molecule gene expression spatial patterns through the lens of stochastic reaction-diffusion models. The key aim is to generalize mathematical connections between these models and their observation as spatial point processes. The new theory will incorporate factors necessary to describe spatial gene expression at subcellular and multicellular scales including various reactions, spatial movements, and geometric effects. This project will also establish the statistical theory of inference on the resulting inverse problem of inferring stochastic rates from only snapshots of individual particle positions. Investigations into parameter identifiability, optimal experimental design, and model selection will ensure robust and reliable inference. In complement to the developed theory, this project will implement and benchmark cutting-edge approaches for efficiently performing large-scale statistical inference, including variational Bayesian Monte Carlo and physics-informed neural networks. The culmination of this work will be packaged into open-source software that infers interpretable biophysical parameters from multi-gene tissue-scale datasets.This CAREER Award is co-funded by the Mathematical Biology and Statistics Programs at the Division of Mathematical Sciences and the Cellular Dynamics & Function Cluster in the Division of Molecular & Cellular Biosciences, BIO Directorate.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.