CAREER: Learning stochastic spatiotemporal dynamics in single-molecule genetics

职业：学习单分子遗传学中的随机时空动力学

• 批准号: 2339241
• 负责人: Christopher Miles
• 金额: $ 49.97万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339241&HistoricalAwards=false
• 关键词: CAREER; Learning; stochastic; spatiotemporal; dynamics

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.

测量在细胞中表达哪些基因的能力已经彻底改变了我们对生物系统的理解。发现范围从确定使不同细胞类型的独特之处（例如，皮肤与脑细胞）到遗传突变中的疾病如何出现。现在，该基因表达数据是每个细胞生物学家工具箱中普遍使用的工具。但是，从这些数据中可靠提取见解的数学理论落后于收集它的惊人进步。这个职业项目将开发关键的理论基础，以分析基因表达的成像数据。进步跨越了实践理论，包括开发数学模型和机器学习方法，这些方法将与实验合作者的数据一起使用。总的来说，该项目旨在在研究基因表达的空间成像数据时创建新的金标准技术，并能够识别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大学生，以在不断发展的数据科学，生物学和数学的交集中培训下一代科学家。除研究活动外，该项目还将创建心态网络，以支持第一代学生科学家追求多样化的STEM劳动力。支持的研究是一个综合计划，用于通过随机反应扩散模型的镜头研究单分子基因表达空间模式。关键目的是将这些模型及其观察到的数学连接概括为空间点过程。新理论将结合描述亚细胞和多细胞尺度上的空间基因表达所必需的因素，包括各种反应，空间运动和几何效应。该项目还将建立对仅从单个粒子位置的快照推断随机速率的逆问题推断的统计理论。对参数身份，最佳实验设计和模型选择的研究将确保鲁棒和可靠的推论。为了完成开发理论，该项目将实施和基准测试尖端方法，以有效地执行大规模统计推断，包括各种贝叶斯蒙特卡洛和物理知识的神经网络。这项工作的最终形式将包装到开源软件中，该软件从多基因组织尺度数据集中取代可解释的生物物理参数。该职业奖是由数学生物学和统计计划共同提供的数学生物学和统计学计划，该计划是数学科学划分的数学生物学和统计学计划，以及细胞授予的统计和统计型统计型和统计范围内的统计数据和统计范围，Biosular Bioscore，Bio cruption.biosory.bioscruits.bioscrutration.biocrutation.bioscority.biosiver.bioscruits.bio cruption.biocrutation.bioscruits.bio cruption。使用基金会的知识分子优点和更广泛的审查标准，通过评估被认为值得支持。