Investigators and Projects
Immunologists spend a lot of their time working with conceptual models of the immune response. Simply put, these comprise an individual scientist's understanding of the paradigms that describe the immune system, and their application to the problem in hand.
Another approach to modelling is to build a more rigorous structure. This is often done using mathematical tools which require concrete quantitative parameters and return explicit numerical behaviour that can be compared with the result of experiment.
These explicit models might involve describing a particular process mathematically, for example using a set of differential equations. Or they may consist of building a simulation model, also known as an agent based model, in which a computer program defines the rules that govern the behaviour of different entities and then tracks their behaviour through virtual time and space. An example of the animations that we generate from these models can be found here.
Models may be simple, based on a small number of general assumptions, or complex and attempt to capture many different interactions in great detail.
These approaches compliment each other and have their own strengths and weaknesses.
There is a good review of this discussing how many different modelling approaches can synergise in understanding a fundamental cell biology process here:
Mogilner, A., Allard, J. & Wollman, R. Cell Polarity: Quantitative Modeling as a Tool in Cell Biology. Science 336, 175-179, doi:10.1126/science.1216380 (2012).
In our work we have spent some time studying simple models of the immune response. We have used a number of approaches including agent based modelling and ODE modelling. We take the view that although very elementary, these constructs may nevertheless capture fundamental constraints, that govern successful and unsuccessful immune responses. We hope that these studies may produce useful insights into the nature of autoimmune disease.
Blyuss, K.B. & Nicholson, L.B. (2012) The role of tunable activation thresholds in the dynamics of autoimmunity.
Journal of Theoretical Biology 308, 45-55.
Nicholson, D., Kerr, E., Jepps, O. & Nicholson, L.B. (2012) Modelling experimental uveitis: barrier effects in autoimmune disease
Inflammation Research doi:10.1007/s00011-012-0469-z.
Nicholson, D. and Nicholson, L.B. (2008) A simple immune system simulation reveals optimal movement and cell density parameters for successful target clearance. Immunology 123, 519-527 doi:10.1111/j.1365-2567.2007.02721.x