MFB: Accelerating the Discovery of Novel Liposome Formations with Origins-of-Life Insights, Laboratory Automation, and Machine Learning

MFB：利用生命起源洞察、实验室自动化和机器学习加速新型脂质体形成的发现

• 批准号: 2226511
• 负责人: Joshua Schrier
• 金额: $ 107.42万
• 依托单位: Fordham University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226511&HistoricalAwards=false
• 关键词: MFB; Accelerating; Discovery; Novel; Liposome

Formulations chemistry is a crucial, but often overlooked area, in fields as diverse as pharmaceuticals, agricultural chemicals, paints and coatings, cosmetics, and household products. Modern designed lipid bilayer structures are complex, multicomponent blends that define the cell. How much can they be simplified and still achieve basic functionality? Understanding how to build lipid structures with specified functionality can advance both fundamental knowledge about origins of life and biotechnology. Machine learning and autonomous research methods developed in this project have direct applications to this problem. Designed lipid bilayer structures with simplified compositions, like liposomes, are important for drug delivery of novel biological pharmaceuticals, mRNA vaccines, and agrochemicals. More speculatively, the ability to create artificial, minimally functional cell-like structures could be combined with existing cell-free biochemistry systems to generate novel synthetic biological systems that combine the engineering advantages of cell-free systems with the ability to self-repair or self-support of cellular systems. Developing artificial protocells with simple components would not only inform our knowledge about how life evolved, but also enable the creation of engineered abiotic biochemical systems. To do this we must overcome the anthropogenic bias and combinatorial explosion with laboratory automation and machine-learning methods. Traditional approaches to chemical evolution have been biased by considering a “best guess” for starting conditions and reactants based on extant organisms and considered only a relatively limited numbers of chemical inputs ( 10 reactants) to tame combinatorial complexity. In this project the investigators will use a combination of laboratory automation and machine-learning-guided experimentation to obtain datasets and statistical baselines, needed to test algorithms for exploring and optimizing these complex, non-ideal mixtures. The investigators will develop algorithms for autonomous formulations chemistry. Experimental chemistry data is noisy, biased, and small compared to most machine learning datasets, and so it is necessary to both make use of existing data while also exploring new chemical systems. The investigators will develop active and meta- learning machine learning approaches to learn from existing experimental data when approaching new optimization problems, utilizing contrastive meta model changes to infer relevant variables. They will also explore graph regularized matrix factorization methods to learn low-dimensional representations directly from experimental observations. Finally, they will continue the development of open-source experimental data management software to facilitate data reuse and sharing. In this project the PIs will engage the broader machine-learning community by running open challenge competitions, using platforms like Kaggle, and disseminating open datasets, with the aim to bring new technical insights into origins-of-life and biophysics research, by drawing upon a pool of citizen scientists. This research will be conducted at two undergraduate-only chemistry departments at Central Connecticut University and Fordham University. This award will support summer and academic year research positions for undergraduate students at the two universities, as well as research of two postdoctoral researchers. Bringing postdoctoral researchers into undergraduate-focused departments exposes undergraduates to another phase of the “life of the scientist”, particularly in the form of a “near peer” who may be more relatable than a professor. It also exposes postdoctoral researchers to the possibility of active research careers at non-R1 universities. The PIs will continue the development of low-cost, open-source robotic hardware and pedagogical material that brings origins of life and laboratory automation into teaching labs, to help train the next generation of chemists to incorporate automation into their experimental process. This project is jointly supported by the Division of Chemistry (CHE), the Division of Information and Intelligent Systems (IIS), the Division of Molecular and Cellular Biosciences (MCB), and the Division of Physics (PHY) Physics.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.

制剂化学是在像药品，农业化学品，油漆和涂料，化妆品和家用产品的潜水场所中的一个至关重要但经常被忽视的区域。现代设计的脂质双层结构是复杂的多组分混合物，可定义细胞。它们可以简化多少并仍然达到基本功能？了解如何使用特定功能构建脂质结构可以提高有关生命和生物技术起源的基本知识。该项目中开发的机器学习和自主研究方法直接应用于此问题。具有简化成分（如脂质体）的设计脂质双层结构对于新型生物药物，mRNA疫苗和农业化学物质的药物输送至关重要。更猜测，可以将创建人工，最低功能的细胞状结构的能力与现有的无细胞生物化学系统结合使用，以生成新型的合成生物学系统，从而将无细胞系统的工程优势与自我重复或自我支持的细胞系统相结合。使用简单组件开发人工协议不仅会为我们的生活如何发展而告知我们的知识，而且还可以创建工程化的非生物生物化学系统。为此，我们必须与实验室自动化和机器学习方法克服人为偏见和组合爆炸。通过考虑基于现有生物的起始条件和反应物的“最佳猜测”，传统的化学进化方法是有偏见的，并且仅考虑了相对有限的化学输入（10个反应物）对驯服组合复杂性。在该项目中，研究人员将使用实验室自动化和机器学习引导实验的组合来获取数据集和统计基准，以测试算法，以探索和优化这些复合物，非理想的混合物。研究人员将开发用于自主公式化学的算法。与大多数机器学习数据集相比，实验化学数据是嘈杂的，有偏见且小的，因此两者都必须同时使用现有数据，同时还可以探索新的化学系统。研究人员将开发主动和元学习机器学习方法，以在接近新的优化问题时从现有的实验数据中学习，并利用对比度元模型更改来推断相关变量。他们还将探索直接从实验观察结果中直接学习低维表示的图形正规矩阵分解方法。最后，他们将继续开发开源实验数据管理软件，以促进数据重用和共享。在这个项目中，PI将使用Kaggle等平台进行开放挑战赛，并传播开放数据集，以吸引新的技术见解，以通过吸引一群公民科学家来吸引更广泛的机器学习社区。这项研究将在康涅狄格大学中央大学和福特汉姆大学的两个仅本科化学系进行。该奖项将支持两所大学的本科生的夏季和学年研究职位，以及两名博士后研究人员的研究。将博士后研究人员带入以本科生为中心的部门，使大学生接触了“科学家的生活”的另一阶段，特别是以“近乎同伴”的形式，他们可能比教授更相关。它还使博士后研究人员接触了非R1大学积极研究职业的可能性。 PI将继续开发低成本的开源机器人硬件和教学材料，从而将生活和实验室自动化的起源带入教学实验室，以帮助培训下一代化学家，以将自动化纳入其实验过程中。该项目由化学司（CHE），信息和智能系统（IIS），分子和蜂窝生物科学（MCB）以及物理学（PHY）物理学司共同支持。该奖项反映了NSF的法规任务，并通过评估基础的知识率和广泛的评论来诚实，并被认为是通过评估来诚实的。

项目成果

A Modern Twist on an Old Measurement: Using Laboratory Automation and Data Science to Determine the Solubility Product of Lead Iodide

旧测量的现代转变：利用实验室自动化和数据科学来确定碘化铅的溶解度乘积

• DOI: 10.1021/acs.jchemed.3c00445
• 发表时间: 2023
• 期刊: Journal of Chemical Education
• 影响因子: 3
• 作者: Norquist, Alexander J.;Jones-Thomson, Gabriel;He, Keqing;Egg, Thomas;Schrier, Joshua
• 通讯作者: Schrier, Joshua