Surveillance of the mechanisms controlling proteome foldedness. This project aims to measure how cells keep the proteome folded. Cells have extensive quality control networks to govern synthesis, folding and transport of every protein but the buffering capacity of this system is not definable. This capacity is needed to understand how problems arise in managing proteome foldedness, a central feature of human diseases and biotechnology and synthetic biology applications that need cell-based produ ....Surveillance of the mechanisms controlling proteome foldedness. This project aims to measure how cells keep the proteome folded. Cells have extensive quality control networks to govern synthesis, folding and transport of every protein but the buffering capacity of this system is not definable. This capacity is needed to understand how problems arise in managing proteome foldedness, a central feature of human diseases and biotechnology and synthetic biology applications that need cell-based production of engineered proteins such as hormones and antibodies. The outcomes are expected to provide basic knowledge of this fundamental process and provide biosensors and screening methods for use in health and biotechnology industries.Read moreRead less
Biophysics-informed deep learning framework for magnetic resonance imaging. This project aims to bring about a paradigm shift from the conventional non-quantitative magnetic resonance imaging to ultra-fast, quantitative, and artefact free imaging. This project integrates biophysics and artificial intelligence, and it is expected to bring new knowledge in both fields. The expected outcomes of this project include next generation magnetic resonance imaging methods with a fundamental shift in the ....Biophysics-informed deep learning framework for magnetic resonance imaging. This project aims to bring about a paradigm shift from the conventional non-quantitative magnetic resonance imaging to ultra-fast, quantitative, and artefact free imaging. This project integrates biophysics and artificial intelligence, and it is expected to bring new knowledge in both fields. The expected outcomes of this project include next generation magnetic resonance imaging methods with a fundamental shift in the approach to image artefacts and image quantification. This project is expected to advance both single subject and population level biomedical imaging with greater accuracy and cost-effectiveness. This project also promotes explainable and generalisable artificial intelligence in medical imaging.Read moreRead less
Developing the basis for an quality control platform for human pluripotent stem cells and their differentiated derivatives. Biophotonic techniques based on spectroscopy have the potential to provide low-cost, automatable measurements for the quality control of stem and differentiated cells produced for use in industry and regenerative medicine. This project is aimed at acquiring the fundamental scientific knowledge base required to bring this about.