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

点击翻译按钮获取中文摘要

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.