Research Fellows Directory
Professor Jon Timmis
University of York
My research cuts across two areas: modelling of biological systems using computers and developing ways that collections (or swarms) of robots can work together to solve a task that a single robot can not do, even when robots in that swarm go wrong. These two topics share a common thread: the immune system.
Being able to control large numbers of robotic units (swarms) to try and achieve a common goal, is a challenging engineering problem. Many aspects of operation can go wrong. In order to allow these swarms to continue operating, mechanisms of what is called ‘self-healing’ are required, where the robotic swarm cannot only identify what is wrong with one, or more, units in the swarm, but also take some form of corrective action to either repair the failure, or reconfigure the operation of the swarm to allow the task to be at least partially completed. The potential of this work is that we may well be able to deploy a number of robotic systems, for example underwater or in space, where humans are not able to control them, for long periods of time as they will be able to “repair” themselves and continue working. This is especially useful in areas such as exploration and environmental monitoring.
The natural immune system is composed of many types of cells that interact to protect and maintain the host. Undertaking experiments is a complex and expensive process, and can be assisted by computer modelling and simulation. Modelling aspects of the immune system can help us to understand how we might best make use of certain drugs and when best to provide interventions with drugs that give the best benefit to the patient. In work we have done this year, using a computer simulation we have been able to show that in some cases intervention with a drug is counterproductive unless that drug is very effective (which is often not the case). In the long term, this work has the potential to help inform the development of effective strategies for dosage requirements.
Interests and expertise (Subject groups)