Australia has an ageing population and an already overburdened healthcare system. Currently, healthcare is a primarily centralised model, the patient has to visit a doctor or hospital where tests are either administered or sent to a pathology lab. But it does not have to be this way– a large number of tests could be transformed from a multi-day, multi-appointment ordeal into a simple, cheap, point of care test using biosensors– and that's where my research comes in.
The cells of your body are all tiny machines. They communicate with each other via molecular signalling, indicating their status to the body whether that be hunger, distress or disease. If we wish to integrate with this system– either to monitor health or treat disease– we need to be capable of designing components to convert biological signals into digital ones, a "cell API", and therefore: a cyborg cell. Currently, there are limited cases where this is possible and the process to generate new biosensors is slow and expensive. By creating a more general method for the design pipeline of biosensor components, my research will push the frontier of what is possible in sensing and interacting with cells, providing the next-generation of medical diagnostics.
Proteins are the machines of life. Almost everything which does something in your body is a protein and an incredible diversity of these have evolved to be both efficient and incredibly specific. But nature is the ultimate blind-watchmaker, taking billions of years to evolve the systems we see today. In order to interact and communicate with these systems– to build a cyborg cell– we need to understand them well enough to build our own components, and in a shorter time-frame! My project aims to use computational methods to rationally design protein components of biosensors and then test them for use in point of care diagnostics.
Building protein biosensors requires proteins that recognise the biological signal we are interested in. Currently we either look for something which nature has already made or screen huge random libraries. Neither of these involves rational design and in both cases it is a black box– we might get something which works but we won't know why. I plan to use computational methods for the rational design of new proteins and then screen those designs for incorporation into novel biosensors for next-generation diagnostics.
Using technology developed in the Alexandrov Lab at the Institute for Molecular Bioscience at UQ, we are able to screen more designs than ever before, bringing us closer to biosensors for high-priority targets such as general viral infection, inflammation, Zikka virus, prostate cancer and others.
My project focuses on developing binders for indicators of inflammation. Inflammation is a general bodily response to injury and a cheap, simple test would allow healthcare professionals to quickly determine the next step for treatment. In addition, specific inflammation markers, or combinations of them, could be used to diagnose particular disease states– such as viral infection– in a point of care diagnostic. Reaching this goal would increase patient outcomes while reducing wait-times and the cost of healthcare.
How The Funds Will Be Used
Funds raised through this campaign will be used to visit the lab of Sarel Fleishman at the Weizmann Institute in Israel. The Fleishman lab is one of the world's top labs for computational protein engineering. Time spent in Israel will be used to learn cutting-edge computational techniques for protein design. These skills will be brought back to Australia where ongoing computational work will be performed while testing the designs produced.
Any additional funds raised will be used to increase the number of designs produced and tested. Targets for further development will be a selection of biomarkers for inflammation: interferon-alpha, interleukin-6, C-reactive protein, and serum amyloid A. By expanding the number of inflammation biomarkers available, we will be better able to detect a disease state early, consistently and help healthcare professionals provide a more accurate diagnosis with reduced wait-times. This money will be used to purchase supercomputing time and on experiments for the screening and validation of the biosensor designs.
Together, with your help, we can help build better systems for designing improved, more affordable and accurate diagnostics.
There are no challenges associated with the lab placement in Israel provided it can be funded. An agreement in principle to host me has been made and the project planned. Science, on the other hand, is hard. There is no guarantee that any of my designs will work, and if they do, they might not work well in the systems subsequently built. That said, I have at my disposal the expertise of world class scientists, both at the Fleischman lab in Israel and the Institute for Molecular Bioscience in Brisbane, Queensland. I truly believe that these rational computational design methods are the way to go and that together we can create the future of diagnostics.