Computer-based technologies are becoming one of the most promising novel approaches due to continuously accelerated growth of both hardware processing power and software algorithm efficiency. One recent example includes machine learning algorithms that revolutionised data analysis in computer science, and lead to new computer games, visual recognition, and other applications that overtake human performance in many cases. Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc. Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems. Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.

Computer-based technologies are becoming one of the most promising novel approaches due to continuously accelerated growth of both hardware processing power and software algorithm efficiency. One recent example includes machine learning algorithms that revolutionised data analysis in computer science, and lead to new computer games, visual recognition, and other applications that overtake human performance in many cases. Here, we propose to perform atomistic molecular simulations using novel enhanced sampling algorithms. Most biologically important processes take place on significantly longer timescales than those accessible to current computer simulations. Therefore, to obtain meaningful and accurate results regarding the kinetics and conformational dynamics of complex molecular systems, we use algorithms that enhance the sampling using parallel calculations with different biases. Developing more optimal biasing algorithms will allow us to model faster and more accurately the key biological processes of interest, including ligand binding, protein conformations, etc. Here we aim to use statistical algorithms inspired by machine learning to develop novel enhanced sampling methods for molecular simulations. Novel algorithms can be applied to a wide range of molecular modeling problems. We will focus on phosphate catalytic enzymes, and study key DNA processing enzymes to reveal the catalytic mechanism in these systems. Due to the essential nature of phosphate catalytic enzymes in most biological processes, a large number of drugs in current clinical practice also target phosphate-processing enzymes treating a wide range of diseases. Examples include reverse transcriptase and integrase inhibitors used against HIV and hepatitis B, proton pump inhibitors used in gastric diseases, kinase, PARP and topoisomerase inhibitors used against a large number of cancers. Studying phosphate catalytic systems with modern molecular modeling methods will enable fundamental advances in our current knowledge of the molecular basis of life. It will also create opportunities for rational development of better drugs to fight diseases.

Cancer is the leading cause of death in developed countries and there is a major desire to pivot towards preventative rather than curative based medicine. Currently, effective treatment heavily relies on early stage detection and an accurate diagnosis of the cancer through molecular profiling. Liver cancer is the third most common cause of death due to cancer and has a global incidence of 1 million new cases annually. The prognosis for patients is poor and even worse in resource-poor settings such as sub-Saharan Africa, and Central and Far-East Asia. For example, liver cancer, linked to hepatitis B infection, currently kills nearly four times as many people as HIV/AIDS in Africa, however early detection could have a significant impact on survival rates. In both the developed and developing world, there is a critical need for new tools and technology for the routine detection and diagnosis of cancer and diseases in general. The goal of this project is to develop a handheld device that can detect biomarkers in urine that will be able to diagnose liver cancer at the point-of-care. It will be assessed using validated patient urine samples. The technology upon which this is based is high performance liquid chromatography (HPLC). Like how a glass prism separates white light into its component colours, HPLC separates a liquid into its component analytes. HPLC is a gold standard analytical technique crucial to many industries worldwide in its ability to separate and identify chemicals in a complex mixture. HPLC is ideally suited to detecting and quantifying biomarkers in urine; however, it is not currently portable or suited to point-of-care analyses due to its size, cost and complexity. As part of this project, we will miniaturise the technology to a handheld device. Point-of-care or on-site HPLC analysis would provide results that could be acted on within minutes that otherwise would take weeks. Due to the crisis in healthcare provision, such technology would ideally be suited to monitoring any individual, not only patients, in the home in order to realise the vision of next generation precision healthcare. Such a device has the potential to monitor us on a daily basis and act as an early warning system for doctors. Such person-specific molecular data may be used to detect or even predict the onset of disease."

Cancer is the leading cause of death in developed countries and there is a major desire to pivot towards preventative rather than curative based medicine. Currently, effective treatment heavily relies on early stage detection and an accurate diagnosis of the cancer through molecular profiling. Liver cancer is the third most common cause of death due to cancer and has a global incidence of 1 million new cases annually. The prognosis for patients is poor and even worse in resource-poor settings such as sub-Saharan Africa, and Central and Far-East Asia. For example, liver cancer, linked to hepatitis B infection, currently kills nearly four times as many people as HIV/AIDS in Africa, however early detection could have a significant impact on survival rates. In both the developed and developing world, there is a critical need for new tools and technology for the routine detection and diagnosis of cancer and diseases in general. The goal of this project is to develop a handheld device that can detect biomarkers in urine that will be able to diagnose liver cancer at the point-of-care. It will be assessed using validated patient urine samples. The technology upon which this is based is high performance liquid chromatography (HPLC). Like how a glass prism separates white light into its component colours, HPLC separates a liquid into its component analytes. HPLC is a gold standard analytical technique crucial to many industries worldwide in its ability to separate and identify chemicals in a complex mixture. HPLC is ideally suited to detecting and quantifying biomarkers in urine; however, it is not currently portable or suited to point-of-care analyses due to its size, cost and complexity. As part of this project, we will miniaturise the technology to a handheld device. Point-of-care or on-site HPLC analysis would provide results that could be acted on within minutes that otherwise would take weeks. Due to the crisis in healthcare provision, such technology would ideally be suited to monitoring any individual, not only patients, in the home in order to realise the vision of next generation precision healthcare. Such a device has the potential to monitor us on a daily basis and act as an early warning system for doctors. Such person-specific molecular data may be used to detect or even predict the onset of disease."

Cancer is the leading cause of death in developed countries and there is a major desire to pivot towards preventative rather than curative based medicine. Currently, effective treatment heavily relies on early stage detection and an accurate diagnosis of the cancer through molecular profiling. Liver cancer is the third most common cause of death due to cancer and has a global incidence of 1 million new cases annually. The prognosis for patients is poor and even worse in resource-poor settings such as sub-Saharan Africa, and Central and Far-East Asia. For example, liver cancer, linked to hepatitis B infection, currently kills nearly four times as many people as HIV/AIDS in Africa, however early detection could have a significant impact on survival rates. In both the developed and developing world, there is a critical need for new tools and technology for the routine detection and diagnosis of cancer and diseases in general. The goal of this project is to develop a handheld device that can detect biomarkers in urine that will be able to diagnose liver cancer at the point-of-care. It will be assessed using validated patient urine samples. The technology upon which this is based is high performance liquid chromatography (HPLC). Like how a glass prism separates white light into its component colours, HPLC separates a liquid into its component analytes. HPLC is a gold standard analytical technique crucial to many industries worldwide in its ability to separate and identify chemicals in a complex mixture. HPLC is ideally suited to detecting and quantifying biomarkers in urine; however, it is not currently portable or suited to point-of-care analyses due to its size, cost and complexity. As part of this project, we will miniaturise the technology to a handheld device. Point-of-care or on-site HPLC analysis would provide results that could be acted on within minutes that otherwise would take weeks. Due to the crisis in healthcare provision, such technology would ideally be suited to monitoring any individual, not only patients, in the home in order to realise the vision of next generation precision healthcare. Such a device has the potential to monitor us on a daily basis and act as an early warning system for doctors. Such person-specific molecular data may be used to detect or even predict the onset of disease."