Cryopreservation by Design. Bringing together experiments and simulations to deliver the next generation of cryoprotectants.
Lead Research Organisation:
University of Warwick
Department Name: Warwick Medical School
Abstract
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.
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.
