BII: Emergent Mechanisms in Biology of Robustness, Integration & Organization (EMBRIO)

BII：生物学中的鲁棒性、整合性新兴机制

• 批准号: 2120200
• 负责人: David Umulis
• 金额: $ 1250万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120200&HistoricalAwards=false
• 关键词: BII; Emergent; Mechanisms; Biology; Robustness

A fundamental requirement for life and survival is that biological systems are robust, and that they maintain function despite external and internal perturbations such as wounding or infection. Integration of signals from multiple inputs (e.g., biochemical, mechanical) is essential to robustness, yet the mechanisms underlying such integration are poorly understood. The Emergent Mechanisms in Biology of Robustness, Integration, and Organization (EMBRIO) Biology Integration Institute will address this knowledge gap by leveraging state-of-the-art techniques for measuring cellular and tissue remodeling as well as imaging and data processing. The Institute's research will determine how living systems employ signaling molecules and intracellular second messengers (e.g., calcium), from cellular to organismal levels, to integrate signals and develop a coordinated, whole-system response to perturbations. An understanding of these dynamics and the integrated networks leading to robustness is essential for elucidating how organisms respond to attack and repair wounds. The Institute will lead to a greater understanding of how to support trans-disciplinary and cross-institutional communication and collaboration to advance science and education. Integration of activities will generate innovative curriculum and professional development tools that will be disseminated for broad use via existing NSF program infrastructure. These tools will facilitate faculty and trainee development of integrated knowledge and research approaches, like computational modeling, which will prepare students to be competitive in the STEM workforce and contribute to new discoveries in Integrative Biology.Complex biological networks are appreciated as integral components of living systems; however, knowledge about system dynamics, integration of diverse inputs, and mechanisms to coordinate responses across broad scales of space and time is lacking. The EMBRIO Institute will innovatively address this knowledge gap by determining how living systems integrate “orthogonal” signals such as chemical and mechanical stimuli, and as a result of this signal integration, develop responsive phenotypes for survival and function. The overarching hypothesis is that multimodal signal integration by second messengers, as a conserved Rule of Life, controls and synchronizes the dynamics and organization of the cytoskeleton and cell signaling to restructure cells and tissues in response to stimulation, damage, or attack. The Institute addresses the hypothesis that, as a Rule of Life, this signal integration persists across taxa, leading to emergent behavior shared by the biological systems. The Institute will study responses to second messengers in biological systems from different kingdoms and over a range of scales, by harnessing big data (e.g., image data, AFM, simulation data) through AI and ML tools to process and interpret the data and bridge simulations across systems. The Institute will develop a unified technology to quantitatively delineate signal integration and the intracellular changes targeted by these signals, and a simulation-based fusion will be developed that takes the measurements and quantifications in one system and directly predicts the responses in other systems. The innovative approaches employed by the Institute are integrated into pedagogy through cognitive apprenticeship to advance a diverse STEM workforce.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.

生命和生存的一个基本要求是生物系统是健壮的，尽管受到外部和内部的干扰，如受伤或感染，它们仍能保持功能。来自多个输入的信号的积分（例如，生物化学的、机械的）对于鲁棒性是必不可少的，然而这种整合的机制却知之甚少。在生物学的鲁棒性，整合和组织（EMBRIO）生物集成研究所的新兴机制将利用最先进的技术来测量细胞和组织重塑以及成像和数据处理，以解决这一知识差距。该研究所的研究将确定生命系统如何利用信号分子和细胞内第二信使（例如，钙），从细胞到有机体水平，整合信号，并发展协调，整个系统的扰动反应。了解这些动力学和导致鲁棒性的集成网络对于阐明生物体如何应对攻击和修复伤口至关重要。该研究所将使人们更好地了解如何支持跨学科和跨机构的交流与合作，以促进科学和教育。活动的整合将产生创新的课程和专业发展工具，这些工具将通过现有的NSF计划基础设施广泛使用。这些工具将促进教师和学员发展综合知识和研究方法，如计算建模，这将使学生在STEM劳动力中具有竞争力，并为综合生物学的新发现做出贡献。然而，缺乏关于系统动态、各种投入的综合以及协调跨越广泛时空尺度的反应的机制的知识。EMBRIO研究所将通过确定生命系统如何整合化学和机械刺激等“正交”信号，并通过这种信号整合，开发出生存和功能的响应表型，从而创新性地解决这一知识差距。总体假设是，第二信使的多模态信号整合作为保守的生命规则，控制和调节细胞骨架和细胞信号传导的动力学和组织，以响应刺激、损伤或攻击而重构细胞和组织。该研究所提出的假设是，作为生活的规则，这种信号整合在整个分类群中持续存在，导致生物系统共享的紧急行为。该研究所将通过利用大数据（例如，图像数据、原子力显微镜、模拟数据），通过人工智能和机器学习工具来处理和解释数据，并在系统之间建立模拟桥梁。该研究所将开发一种统一的技术，以定量描述信号整合和这些信号所针对的细胞内变化，并将开发一种基于模拟的融合，在一个系统中进行测量和量化，并直接预测其他系统的反应。该研究所采用的创新方法通过认知学徒制融入教学法，以促进多元化的STEM劳动力。该奖项反映了NSF的法定使命，并通过使用基金会的智力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

Self-Supervised Fair Representation Learning without Demographics

• 发表时间: 2022
• 作者: Junyi Chai;Xiaoqian Wang

Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

数据科学知识整合：计算认知学徒期对学生概念理解的启示

• DOI: 10.1002/cae.22580
• 发表时间: 2023
• 期刊: Computer Applications in Engineering Education
• 影响因子: 2.9
• 作者: Sánchez‐Peña, Matilde;Vieira, Camilo;Magana, Alejandra J.
• 通讯作者: Magana, Alejandra J.

Fairness without Demographics through Knowledge Distillation

• 发表时间: 2022
• 作者: Junyi Chai;T. Jang;Xiaoqian Wang

Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo

通过基于神经网络模型的斑马鱼胚胎 3D 有限元模型加速确定平流在骨形态发生蛋白形成模式中的作用

• DOI: 10.3389/fsysb.2022.983372
• 发表时间: 2022
• 期刊: Frontiers in Systems Biology
• 作者: Li, Linlin;Wang, Xu;Chai, Junyi;Wang, Xiaoqian;Buganza-Tepole, Adrian;Umulis, David M.
• 通讯作者: Umulis, David M.

3D nuclei segmentation for multi-cellular quantification of zebrafish embryos using NISNet3D

使用 NISNet3D 进行斑马鱼胚胎多细胞定量的 3D 细胞核分割

• DOI: 10.2352/ei.2023.35.17.3dia-109
• 发表时间: 2023
• 期刊: Electronic Imaging
• 作者: Li, Linlin;Wu, Liming;Chen, Alain;Delp, Edward J.;Umulis, David M.
• 通讯作者: Umulis, David M.