MODULUS: Data-Driven Mechanistic Modeling of Hierarchical Tissues

MODULUS：分层组织的数据驱动机制建模

• 批准号: 1936833
• 负责人: Babak Shahbaba
• 金额: $ 80万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1936833&HistoricalAwards=false
• 关键词: MODULUS; Data; Driven; Mechanistic; Modeling

This project will develop new statistical and mathematical models that describe how cells and molecules within cells self organize to perform biological functions within an organism. While it is increasingly feasible to study biological systems as a whole by collecting information across many scales (e.g., cellular and molecular levels), a major challenge in such studies is to properly integrate information from individual components in order to obtain a complete picture of the system. What makes this task even more daunting is the fact that biological systems are typically heterogeneous and dynamic, meaning that the system properties tend to change across individuals, time, and space. For investigating such complex biological systems, this project brings together an interdisciplinary team of data and biological scientists in order to develop and validate a new mathematical framework that combines statistical and mechanistic models together to enable scientists to discover emergent biological phenomena and to understand the rules that govern them. This framework will then specifically be used to investigate hematopoiesis, which is a remarkable biological process responsible for creation and maintenance of blood cells, and involves complex interactions among biochemical and physical events across temporal and spatial scales that are still not well-understood. Additionally, this project will provide undergraduate and graduate students with a true interdisciplinary experience with equal mentorship from data and biological scientists. The overarching objective of this project is to develop a new data-driven framework for investigating complex biological systems that are characterized by heterogeneity, dynamics, and interactions across multiple time and space scales. The investigators will achieve this goal by embedding mechanistic models in a hierarchical Bayesian framework. Hierarchical Bayesian models provide a natural framework for integrating information (as well as prior knowledge) available at different scales. Mechanistic models, on the other hand, provide a flexible framework for modeling heterogeneous and dynamic systems in ways that enable prediction and control. This mathematical framework will be used to develop optimal experimental design strategies in order to elucidate hematopoiesis dynamics, perform new in vivo experiments to produce serially sampled barcoded single-cell gene expression profiles, and analyze the resulting data. Hematopoiesis is an ideal biological process for this modeling framework because 1) cell populations (stem, progenitor, and mature cells) are well-defined, 2) experimental model systems allow for easy manipulation, and 3) it is possible to apply stressors to minimally perturb the system and observe the process of returning to homeostasis/equilibrium. Successful implementation of this project will allow scientists to gain insights into physiologic hematopoiesis. The methodology developed in this project will be transferable to other heterogeneous and dynamic biological systems in developmental biology, ecology, and epidemiology. This award was co-funded by Systems and Synthetic Biology in the Division of Molecular and Cellular Biosciences and the Mathematical Biology Program of the Division of Mathematical Sciences.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.

该项目将开发新的统计和数学模型，描述细胞和细胞内的分子如何在生物体中自我组织以执行生物功能。虽然通过收集多个尺度（如细胞和分子水平）的信息来研究整个生物系统越来越可行，但这类研究的一个主要挑战是如何适当地整合来自各个组成部分的信息，以获得系统的完整图像。使这项任务更加艰巨的是，生物系统通常是异质的和动态的，这意味着系统属性倾向于在个体、时间和空间之间变化。为了研究如此复杂的生物系统，该项目汇集了一个跨学科的数据和生物科学家团队，以开发和验证一个新的数学框架，将统计和机械模型结合在一起，使科学家能够发现新兴的生物现象，并理解控制它们的规则。这一框架将专门用于研究造血，这是一个重要的生物学过程，负责血细胞的产生和维持，涉及跨越时间和空间尺度的生化和物理事件之间的复杂相互作用，目前仍未得到很好的理解。此外，该项目将为本科生和研究生提供真正的跨学科经验，并得到数据和生物科学家的平等指导。该项目的总体目标是开发一个新的数据驱动框架，用于研究复杂的生物系统，这些系统具有异质性、动态性和跨时间和空间尺度的相互作用。研究人员将通过在层次贝叶斯框架中嵌入机制模型来实现这一目标。层次贝叶斯模型为整合不同尺度的信息（以及先验知识）提供了一个自然的框架。另一方面，机制模型提供了一个灵活的框架，以支持预测和控制的方式对异构和动态系统进行建模。该数学框架将用于制定最佳的实验设计策略，以阐明造血动力学，执行新的体内实验，以产生连续采样的条形码单细胞基因表达谱，并分析结果数据。造血是该建模框架的理想生物过程，因为1)细胞群（干细胞、祖细胞和成熟细胞）定义良好，2)实验模型系统易于操作，3)可以应用压力源对系统进行最小的干扰，并观察到恢复稳态/平衡的过程。这个项目的成功实施将使科学家们对生理性造血有更深入的了解。本项目开发的方法将可转移到发育生物学、生态学和流行病学等其他异质和动态生物系统中。该奖项由分子和细胞生物科学部的系统和合成生物学以及数学科学部的数学生物学项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The SMAC mimetic LCL-161 selectively targets JAK2V617F mutant cells

• DOI: 10.1186/s40164-019-0157-6
• 发表时间: 2020-01-02
• 期刊: EXPERIMENTAL HEMATOLOGY & ONCOLOGY
• 影响因子: 10.9
• 作者: Craver, Brianna M.;Thanh Kim Nguyen;Fleischman, Angela G.
• 通讯作者: Fleischman, Angela G.

Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks

• DOI: 10.1137/21m1439456
• 发表时间: 2021-01
• 期刊: SIAM/ASA J. Uncertain. Quantification
• 作者: Shiwei Lan;Shuyi Li;B. Shahbaba

Quality of life independently predicts overall survival in myelofibrosis: Key insights from the COntrolled MyeloFibrosis Study with ORal Janus kinase inhibitor Treatment (COMFORT)‐I study

生活质量独立预测骨髓纤维化患者的总体生存率：使用 ORal Janus 激酶抑制剂治疗的控制性骨髓纤维化研究 (COMFORT) 的主要见解 –I 研究

• DOI: 10.1111/bjh.18329
• 发表时间: 2022
• 期刊: British Journal of Haematology
• 影响因子: 6.5
• 作者: Kosiorek, Heidi E.;Scherber, Robyn M.;Geyer, Holly L.;Verstovsek, Srdan;Langlais, Blake T.;Mazza, Gina L.;Gotlib, Jason;Gupta, Vikas;Padrnos, Leslie J.;Palmer, Jeanne M.
• 通讯作者: Palmer, Jeanne M.

E-Cigarette Exposure Decreases Bone Marrow Hematopoietic Progenitor Cells

• DOI: 10.3390/cancers12082292
• 发表时间: 2020-08-01
• 期刊: CANCERS
• 影响因子: 5.2
• 作者: Ramanathan, Gajalakshmi;Craver-Hoover, Brianna;Fleischman, Angela G.
• 通讯作者: Fleischman, Angela G.

Fecal Microbial Community Composition in Myeloproliferative Neoplasm Patients Is Associated with an Inflammatory State.

• DOI: 10.1128/spectrum.00032-22
• 发表时间: 2022-06-29
• 期刊: Microbiology spectrum
• 影响因子: 3.7