Collaborative Research: Integrative Modeling, Prediction, and Validation of Multi-Scale Dynamics for Three-Dimensional Chromatin Structures in Cell Differentiation

合作研究：细胞分化中三维染色质结构的多尺度动力学综合建模、预测和验证

• 批准号: 2152011
• 负责人: Jianrong Wang
• 金额: $ 80万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152011&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrative; Modeling; Prediction

This interdisciplinary award develops mathematical models and computational algorithms to dissect the mechanisms of how different types of cells are formed from stem cells. The formation of specific cell types plays pivotal roles in all aspects of life, and different tissues are composed of hundreds of types of cells. As one of the underlying mechanisms, the 3D structure of DNA undergoes dynamic reshaping and instructs this complex process. However, it is under-studied and quantitative models are lacking due to the complexity of the system and large amounts of noisy data. The project will establish a novel mathematical framework to understand the dynamics of DNA structures and its effects on new cell type formation. Efficient computational and statistical algorithms will also be designed to discover the molecular drivers, which will be interrogated by cutting-edge experiments. The integrative strategy will lead to new systems-level insights into the molecular mechanisms of the formation of diverse cell types, with broad impacts on human health, synthetic biology and disease analyses, including neurodegenerative diseases and cancer. To expand the societal impacts, the project will integrate the scientific discoveries with a series of outreach activities for public engagement, including new interactive educational modules and platforms. Interdisciplinary hands-on training will be developed for both undergraduate and graduate students, with an emphasis on increasing the diversity and participation of under-represented groups of students for STEM education.The overall goal is to theoretically and quantitatively delineate the functional roles of dynamic 3D genome structures in the process of multi-lineage cell differentiation. Due to the complexity of multi-scale 3D organizations of chromatin, the coupled dynamics of transcription and epigenetic factors, and the high rates of missing data in genome-wide experiments, systems-level modeling of the temporal dynamics of 3D chromatin through specific cell differentiation trajectory has been challenging. The project tackles this fundamental problem using an integrative strategy by combining mathematical, computational, statistical and experimental approaches. Specifically, the project will: 1) establish a new multi-scale mathematical model for the dynamics of 3D chromatin architecture through cell differentiation, by integrating different experimental observations; 2) design novel computational algorithms to reconstruct the trajectories across cell differentiation from large-scale noisy data; and 3) develop efficient statistical inference tools to discover the underlying molecular events of cell fate determination. Both theoretical and computational predictions will be thoroughly analyzed by experimental techniques, including 3C-HTGTS and 3D FISH. The new methodology generated by the project, along with the genome-wide predictions across diverse panels of cell types, will have broad impacts on multiple scientific fields in both mathematics and biology, and will significantly improve the mechanistic understandings, predictive capacities and experimental engineering of cell differentiation.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.

这个跨学科的奖项开发数学模型和计算算法，以剖析不同类型的细胞是如何从干细胞形成的机制。特定细胞类型的形成在生命的各个方面起着关键作用，不同的组织由数百种细胞组成。作为潜在机制之一，DNA的3D结构经历动态重塑并指导这一复杂过程。然而，由于系统的复杂性和大量的噪音数据，它的研究还不够，并且缺乏定量模型。该项目将建立一个新的数学框架，以了解DNA结构的动力学及其对新细胞类型形成的影响。还将设计有效的计算和统计算法来发现分子驱动程序，这些分子驱动程序将通过尖端实验进行询问。综合战略将导致对不同细胞类型形成的分子机制的新系统水平的见解，对人类健康，合成生物学和疾病分析，包括神经退行性疾病和癌症产生广泛影响。为了扩大社会影响，该项目将把科学发现与一系列公众参与的外展活动结合起来，包括新的互动教育模块和平台。将为本科生和研究生开发跨学科实践培训，重点是增加STEM教育中代表性不足的学生群体的多样性和参与性，总体目标是从理论上和定量上描述动态3D基因组结构在多谱系细胞分化过程中的功能作用。由于染色质的多尺度3D组织的复杂性、转录和表观遗传因子的耦合动力学以及全基因组实验中的高数据丢失率，通过特定细胞分化轨迹对3D染色质的时间动力学进行系统级建模一直具有挑战性。该项目通过结合数学，计算，统计和实验方法，使用综合策略解决这一基本问题。具体来说，该项目将：1）通过整合不同的实验观察，建立一个新的多尺度数学模型，用于细胞分化过程中3D染色质结构的动态变化; 2）设计新的计算算法，从大规模噪声数据中重建细胞分化过程中的轨迹;以及3）开发有效的统计推断工具以发现细胞命运决定的潜在分子事件。理论和计算预测都将通过实验技术进行彻底分析，包括3C-HTGTS和3D FISH。 该项目产生的新方法，沿着跨不同细胞类型面板的全基因组预测，将对数学和生物学的多个科学领域产生广泛影响，并将显着提高机械理解，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准。