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HDR: DIRSE-IL: Collaborative Research: Harnessing data advances in systems biology to design a biological 3D printer: the synthetic coral

HDR: DIRSE-IL: Collaborative Research: Harnessing data advances in systems biology to design a biological 3D printer: the synthetic coral
HDR:DIRSE-IL:协作研究:利用系统生物学的数据进步来设计生物 3D 打印机:合成珊瑚
批准号:
1939263
负责人:
Lenore Cowen
金额:
$26.01万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2022-09-30

项目摘要

项目成果

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中文摘要
翻译
珊瑚是重要的自然资源,是海洋巨大生物多样性的关键,并提供经济、文化和科学效益。由于当地和全球的人类活动,珊瑚礁正在迅速减少。珊瑚的复杂性使得保护和恢复珊瑚礁非常具有挑战性。珊瑚是由数千种不同的生物组成的,包括动物宿主和作为所谓的全息生物共存的藻类、细菌、病毒和真菌。因此,珊瑚更像城市,而不是单个动物,因为它们为整个生态系统提供工厂、住房、餐馆、苗圃等。该项目汇集了计算机科学、材料科学和生物学方面的专家,利用生物学中的数据革命和机器学习来研究珊瑚是如何生长和功能的,就好像它们是海洋中的制造基地一样。这项研究将集中在珊瑚的三个关键功能上:(1)它们产生碳酸钙骨架,为各种海洋生物提供三维结构,(2)它们可以治愈组织损伤,(3)它们与其他生物一起生活在一个被称为共生的过程中。通过这些非凡的能力,珊瑚可以像3D打印机一样为自己和成千上万的其他物种“打印”资源。该项目的目标是充分了解这些过程,以便在实验室中控制它们。这个项目可能会找到新的方法来帮助珊瑚生存,通过破译某些条件损害它们的原因,并找到修复它们的方法。此外,通过人工种植珊瑚,可以找到新的制造材料。该项目通过创造和传播融合研究环境的成果,为培养多样化的科学工作者和公众提供了机会,并将培养博士后研究人员、研究生和本科生。这项研究的结果将通过网络界面、同行评审的出版物和研讨会/会议提供给更广泛的科学界,并通过在线、学校和公共水族馆的外展活动与公众分享。通过计算机科学、材料科学和生物学这三个学科的融合,该项目将提供一个数据驱动的框架和工具集,用于学习、控制、设计和制造一种组合形式的活材料,即“合成珊瑚”,从而为材料合成和制造开辟新的途径。研究方法将提供新的分析方法,以确定和量化控制珊瑚生长的参数,并培育控制珊瑚生长的创新工具。为了了解珊瑚生物矿化、伤口愈合和共生的关键功能,本研究将:(1)利用和分析大量珊瑚“组学”数据,以破译关键分子及其对上述关键功能的相互作用;(2)在珊瑚个体和细胞系中实验验证所得预测;(3)操纵珊瑚个体和细胞系的碳酸钙结构的材料特性;(4)在“合成珊瑚”网络模型中测试生物和物理相互作用。该项目开发并集成了一个集成计算和实验验证系统所必需的基本构建块。具体来说,使用机器学习,将利用各种数据来识别与珊瑚全息生物的关键结构和功能特性相关的物理条件(例如,表面特征)、环境条件(例如,温度、pH值)和关键生物成分(例如,DNA中编码的小分子配体和蛋白质)。这些预测的条件和分子将通过干扰完整珊瑚或其组成细胞的3D打印阵列网络中的单个珊瑚节点,并测量它们对相互作用网络和最终结构的影响,进行实验验证。然后,预测-验证周期的结果将作为输入转移回制造完全包含有机/无机界面的新型自适应材料。该项目是美国国家科学基金会“利用数据革命(HDR)大创意”活动的一部分。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。
英文摘要
Corals are important natural resources that are key to the ocean's vast biodiversity and provide economic, cultural, and scientific benefits. As a result of human activities, locally and globally, coral reefs are declining rapidly. The complexity of corals makes conserving and restoring reefs very challenging. Corals are made up of thousands of different organisms, including the animal host and the algae, bacteria, viruses, and fungi that coexist as a so-called holobiont. Thus, corals are more like cities than individual animals, as they provide factories, housing, restaurants, nurseries, and more for an entire ecosystem. This project brings together experts in computer science, materials science, and biology to harness the data revolution in biology with machine learning to study how corals grow and function, when viewed as if they were manufacturing sites in the ocean. The study will focus on three key coral capabilities: (1) they create calcium carbonate skeletons that provide 3D structures for diverse sea life to live in, (2) they can heal damage to their tissues, and, (3) they live with the other organisms in a process referred to as symbiosis. Through these remarkable abilities, corals can 'print' resources for themselves and hundreds of thousands of other species, just like a 3D printer. The goal of this project is to understand these processes well enough to control them in the lab. This project may allow finding new ways to help coral survival, by deciphering the reasons why certain conditions damage them and find ways of repairing them. Furthermore, by synthetically growing corals, new types of materials may be identified for manufacturing. This project offers an opportunity to educate a diverse scientific workforce and the public by creating and disseminating the outcomes of a convergent research environment and will train postdoctoral researchers, graduate, and undergraduate students. Results of this research will be made available to the broader scientific community through web interfaces, peer-reviewed publications and workshops/conferences and shared with the public through outreach activities online, at schools, and public aquariums. Through convergence of three disciplines, computer science, material science and biology, this project will provide a data-driven framework and toolset to learn from, control, engineer, and manufacture a combined form of living material, the 'synthetic coral', thereby opening new avenues for material synthesis and manufacturing. The research methodology will offer new analytical approaches to identify and quantify the parameters that govern coral growth and foster innovative new tools for controlling their growth. To understand the key functions of coral biology of biomineralization, wound healing, and symbiosis, this research will : (1) harness and analyze large amounts of coral '-omics' data to decipher critical molecules and their interactions for the aforementioned key functions, (2) experimentally validate the resulting predictions in coral individuals and cell lines, (3) manipulate the material properties of the calcium carbonate structures of the coral individuals and cell lines, and (4) test the biological and physical interactions in a network model of the 'synthetic coral'. This project develops and integrates fundamental building blocks that are essential for an integrated computational and experimental validation system. Specifically, using machine learning, diverse data will be harnessed to identify physical conditions (e.g., surface characteristics), environmental conditions (e.g., temperature, pH), and key biological constituents (e.g., small molecule ligands and proteins encoded in the DNA) that are correlated to key structural and functional properties of the coral holobiont. These predicted conditions and molecules will be verified experimentally by perturbing individual coral nodes in a network of a 3D printed array of intact corals or their constituent cells and measuring their effects on the network of interactions and resulting structures. The results from this prediction-validation cycle will then be transferred back as input to manufacture novel adaptive materials fully embracing the organic/inorganic interface. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.
期刊论文(7)
专著(0)
科研奖励(0)
会议论文
MEtaData Format for Open Reef Data (MEDFORD)
开放珊瑚礁数据元数据格式 (MEDFORD)
DOI: --
发表时间: 2022
期刊: Metadata and Semantic Research. MTSR 2021.
影响因子: --
作者: [Shpilker, P., Freeman, J., McKelvie, H., Ashey, J., Fonticella, J.M., Putnam, H., Greenberg, J., Cowen, L., Couch, A., Daniels, N.M.]
通讯作者: Daniels, N.M.
DOI: 10.1146/annurev-biodatasci-122120-030732
发表时间: 2022-01-01
期刊: ANNUAL REVIEW OF BIOMEDICAL DATA SCIENCE
影响因子: --
作者: [Cowen, Lenore J., Putnam, Hollie M.]
通讯作者: Putnam, Hollie M.
Identifying Cognitive and Creative Support Needs for Remote Scientific Collaboration using VR: Practices, Affordances, and Design Implications
确定使用 VR 进行远程科学协作的认知和创造性支持需求:实践、功能可供性和设计含义
DOI: 10.1145/3527927.3532797
发表时间: 2022
期刊: C&C '22: Creativity and Cognition
影响因子: --
作者: [Olaosebikan, Monsurat, Aranda Barrios, Claudia, Kolawole, Blessing, Cowen, Lenore, Shaer, Orit]
通讯作者: Shaer, Orit
Embodied Notes: A Cognitive Support Tool For Remote Scientific Collaboration in VR
Embodied Notes:VR 中远程科学协作的认知支持工具
DOI: 10.1145/3491101.3519664
发表时间: 2022
期刊: CHI Conference on Human Factors in Computing Systems Extended Abstracts
影响因子: --
作者: [Olaosebikan, Monsurat, Aranda Barrios, Claudia, Cowen, Lenore, Shaer, Orit]
通讯作者: Shaer, Orit
HDR TRIPODS: Building the Foundation for a Data-Intensive Studies Center-
  • 批准号:
    1934553
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $150.0万
  • 财政年份:
    2019
  • 负责人:
    Lenore Cowen
  • 依托单位:
Mining Multi-Layer Protein-Protein Association Networks: An Integrated Spectral Approach
  • 批准号:
    1812503
  • 项目类别:
    Standard Grant
  • 资助金额:
    $21.0万
  • 财政年份:
    2018
  • 负责人:
    Lenore Cowen
  • 依托单位:
CCF-TFNSG: Uniting the Discrete Methods, Optimization and the CISE Community with Community Studying Matrix Operations, Tensors,Verifiable Computational Experiments and Scalability
  • 批准号:
    0843426
  • 项目类别:
    Standard Grant
  • 资助金额:
    $4.41万
  • 财政年份:
    2008
  • 负责人:
    Lenore Cowen
  • 依托单位:
Algorithms for Approximate Routing Problems
  • 批准号:
    0208629
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $22.26万
  • 财政年份:
    2002
  • 负责人:
    Lenore Cowen
  • 依托单位:
海外基金