MODULUS: Integrative multiscale modeling and multimodal experiments to decode systems-level molecular mecanisms of epithelial systems

MODULUS：综合多尺度建模和多模态实验来解码上皮系统的系统级分子机制

• 批准号: 2029814
• 负责人: Mark Alber
• 金额: $ 89.99万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029814&HistoricalAwards=false
• 关键词: MODULUS; Integrative; multiscale; modeling; multimodal

The goal of this project is to integrate novel multi-scale mathematical modeling approaches and specifically designed experiments for predicting how molecular signaling drives cell and tissue shape generation and maintenance during development. Experimentally validated multi-scale mathematical models of biological systems will be used to infer the basic principles and rules of epithelial morphogenesis. The interdisciplinary research program will be used to discover new roles of morphogens as regulators of cell mechanics and how cell mechanics provide indirect potential feedback into morphogen signaling during development of the fruit fly Drosophila wing imaginal disc, one of the main biological models for studying basic mechanisms of animal and human development. The study of perturbations to normal development will also provide general biological insights into the mechanistic basis for diseases from birth defects to cancers related to uncontrolled tissue growth. The project will also provide a basis for developing predictive design tools for synthetic multicellular systems, including engineering organoids or soft robotics. The project will provide training for students from underrepresented groups in mathematical and computational biology, quantitative biology and biological signaling pathways regulation. One-day workshops will be held by the UC Riverside Interdisciplinary Center for Quantitative Modeling in Biology to disseminate scientific knowledge and facilitate cross-fertilization between fields of mathematical and computational biology, applied mathematics, quantitative and experimental biology. This research project will combine mathematical modeling and quantitative experiments to answer key questions regarding how morphogens such as the Bone Morphogenetic Protein and Wingless/WNT signaling pathways regulate the cytoskeletal proteins that control cell and tissue shape formation and maintenance during organ development. The research will focus on developing multi-scale models of epithelial morphogenesis in the fruit fly wing imaginal disc during larval development due to the wealth of genetic and imaging tools available to test specific hypotheses that connect morphogens to actomyosin contractility and the extracellular matrix. Models that incorporate biochemical signaling and cell mechanics in three dimensions will be developed and calibrated using experimental data. Breakthroughs in the theory of reaction-diffusion systems on deforming surfaces coupled with coarse graining approaches describing cell membrane and cytoskeleton will be utilized together with data driven surrogate models that derived from experimental images. Machine learning approaches will be used to extract quantitative information from biological data to facilitate a comparison between experimental outcomes and model predictions. Deep learning based super-resolution imaging approaches will be developed to determine subcellular properties of cells. Statistical methods and sensitivity analysis will be incorporated to prevent model overfitting. Combinatorial perturbation simulations and experiments will result in systematic model comparison. The project will also result in a general modeling platform for predicting morphogenesis outcomes. The project will advance goals for STEM recruitment and training students from underrepresented groups. Scientific workshops will be organized to facilitate cross-fertilization between fields of mathematical and computational biology and quantitative and experimental biology.This award is being co-funded by the Division of Molecular and Cellular Biosciences (MCB) through the Systems and Synthetic Biology Program, and the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program.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.

该项目的目标是整合新的多尺度数学建模方法和专门设计的实验，以预测分子信号如何驱动细胞和组织形状的产生和维持。实验验证的生物系统的多尺度数学模型将用于推断上皮形态发生的基本原理和规则。该跨学科研究项目将用于发现形态因子作为细胞力学调节因子的新作用，以及细胞力学如何在果蝇翅膀想象盘的发育过程中为形态因子信号传递提供间接的潜在反馈。果蝇翅膀想象盘是研究动物和人类发育基本机制的主要生物学模型之一。对正常发育的扰动的研究也将为从出生缺陷到与不受控制的组织生长有关的癌症等疾病的机制基础提供一般的生物学见解。该项目还将为开发合成多细胞系统的预测设计工具提供基础，包括工程类器官或软机器人。该项目将为来自代表性不足群体的学生提供数学和计算生物学、定量生物学和生物信号通路调控方面的培训。为期一天的研讨会将由加州大学河滨分校生物学定量建模跨学科中心举办，以传播科学知识，促进数学和计算生物学、应用数学、定量和实验生物学领域的交叉施肥。该研究项目将结合数学模型和定量实验来回答关于骨形态发生蛋白和无翼/WNT信号通路等形态因子如何调节在器官发育过程中控制细胞和组织形状形成和维持的细胞骨架蛋白的关键问题。由于丰富的遗传和成像工具可用于测试将形态形成因子与肌动球蛋白收缩性和细胞外基质联系起来的特定假设，因此研究将重点放在开发幼虫发育过程中果蝇翅膀成像盘上皮形态发生的多尺度模型上。结合生化信号和细胞力学在三维模型将开发和校准使用实验数据。在变形表面上的反应扩散系统理论的突破，加上描述细胞膜和细胞骨架的粗粒化方法，将与来自实验图像的数据驱动代理模型一起利用。机器学习方法将用于从生物数据中提取定量信息，以促进实验结果和模型预测之间的比较。将开发基于深度学习的超分辨率成像方法来确定细胞的亚细胞特性。采用统计方法和敏感性分析防止模型过拟合。组合摄动模拟和实验将导致系统的模型比较。该项目还将产生一个用于预测形态发生结果的通用建模平台。该项目将推进STEM招聘和培训来自代表性不足群体的学生的目标。将组织科学研讨会，以促进数学和计算生物学以及定量和实验生物学领域之间的交叉施肥。该奖项由分子和细胞生物科学部（MCB）通过系统和合成生物学项目和MPS数学科学部（DMS）通过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

Poisson2Sparse: Self-Supervised Poisson Denoising From a Single Image

• DOI: 10.1007/978-3-031-16452-1_53
• 发表时间: 2022-06
• 作者: Calvin-Khang Ta;Abhishek Aich;Akash Gupta;A. Roy-Chowdhury

Computational biomechanical modeling of fibrin networks and platelet-fiber network interactions

纤维蛋白网络和血小板纤维网络相互作用的计算生物力学模型

• DOI: 10.1016/j.cobme.2022.100369
• 发表时间: 2022
• 期刊: Current Opinion in Biomedical Engineering
• 影响因子: 3.9
• 作者: Pancaldi, Francesco;Kim, Oleg V.;Weisel, John W.;Alber, Mark;Xu, Zhiliang
• 通讯作者: Xu, Zhiliang

Epithelial organ shape is generated by patterned actomyosin contractility and maintained by the extracellular matrix

• DOI: 10.1371/journal.pcbi.1008105
• 发表时间: 2020-01
• 期刊: PLoS Computational Biology
• 影响因子: 4.3
• 作者: Ali Nematbakhsh;Megan Levis;Nilay Kumar;Weitao Chen;Jeremiah J Zartman;M. Alber