Cryopreservation by Design. Bringing together experiments and simulations to deliver the next generation of cryoprotectants.

这个由MRC资助的博士培训伙伴关系（DTP）将尖端的分子和分析科学与数据分析中的创新计算方法结合在一起，使学生能够解决以假设为主导的生物医学研究问题。这是一个为期4年的计划，其第一年涉及一系列教学模块和两个基于实验室的研究项目，导致跨学科生物医学研究硕士学位。前两个学期包括一系列教学模块，让学生在多学科科学中获得坚实的基础。学生还参加了一系列由学术和行业专家领导的大师班，这些专家在分子，细胞和组织动力学，微生物学和感染，应用生物医学技术以及人工智能和数据科学领域。在第三和夏季学期，学生在他们选择的实验室进行两个为期11周的研究项目。项目：下一代医疗依赖于生物材料的长期储存，通常通过冷冻保存实现-冷冻感兴趣的材料以防止其恶化的过程。可悲的是，目前我们对大多数冷冻保存程序之后的冰的形成几乎没有控制，这导致解冻时生物材料和活性的大量损失。被称为冷冻保护剂的物质可以调节冰形成的动力学，但在过去的几十年里，只有少数有效的化合物被确定，因为我们不知道哪些结构特征使给定的分子或聚合物或蛋白质具有冷冻保护剂的活性。这个博士项目旨在解决这一未满足的需求，将实验和模拟结合起来，以揭示结构-功能关系，从而实现下一代冷冻保护剂的真正合理设计。该项目将采用机器学习，化学合成和一系列定制的冷冻保护剂测定的迭代使用，以确定活性化合物的结构和功能特征。学生将获得一个罕见的专业知识，包括计算科学，人工智能和高通量合成和表征的潜在利益的化学品在低温保存的背景下的大型库。这将使学生能够轻松有效地跨越计算物理化学和生物医学科学的界限。

This MRC-funded doctoral training partnership (DTP) brings together cutting-edge molecular and analytical sciences with innovative computational approaches in data analysis to enable students to address hypothesis-led biomedical research questions. This is a 4-year programme whose first year involves a series of taught modules and two laboratory-based research projects that lead to an MSc in Interdisciplinary Biomedical Research. The first two terms consist of a selection of taught modules that allow students to gain a solid grounding in multidisciplinary science. Students also attend a series of masterclasses led by academic and industry experts in areas of molecular, cellular and tissue dynamics, microbiology and infection, applied biomedical technologies and artificial intelligence and data science. During the third and summer terms students conduct two eleven-week research projects in labs of their choice. Project:The next generation of medical treatments rely on the long-term storage of biological material, usually achieved via cryopreservation - the process of freezing the material of interest so as to prevent its deterioration. Sadly, at the moment we have very little control on the formation of ice that follows most cryopreservation procedures, which results in a substantial loss of both biological material and activity upon thawing. Substances known as cryoprotectants can regulate the kinetics of ice formation, but only a handful of efficient compounds have been identified in the past few decades as we don't know which structural features make a given molecule or polymer or protein active as a cryoprotectant. This PhD project seeks to address this unmet need, bringing together experiments and simulations to unravel the structure-function relationship to achieve a truly rational design of the next generation of cryoprotectants. The project will employ the iterative usage of machine learning, chemical synthesis and a range of bespoke cryoprotectant assays to determine the structural and functional features of active compounds. The student will acquire a rare blend of expertise encompassing computational science, artificial intelligence and high-throughput synthesis and characterisation of large libraries of chemicals of potential interest in the context of cryopreservation. This will enable the student to move effortlessly and effectively across the boundaries of computational physical chemistry and the biomedical sciences.