York Computational Immunology Lab: Advancing Immunology Through Computational Modelling

Overview

York Computational Immunology Laboratory brings together the expertise of eight principal investigators spanning the Departments of Electronics, Biology/HYMS, Computer Science and Mathematics in the emerging area of systems immunology, pioneering the application of multi-dimensional, multi-scale (MDMS) modelling to solve immunological problems.

Historically modelling has been used to recreate known biology, in contrast we have utilised simulation modelling to address the unknown and generate novel hypotheses that can be tested experimentally. In this project we will further develop and utilise MDMS modelling technology combining mathematical approaches with “rich” agent based modelling to address key mechanistic questions in immunology including molecular mechanisms driving the generation and resolution of inflammatory pathology.

YCIL will develop a collaborative research environment, will develop and support open tools for modelling and demonstrate leadership through holding an international workshop in 2014 on systems immunology. We will apply computational approaches to understand mechanisms driving immune tissue function, homeostasis and remodelling and test the hypotheses emerging from the models in vivo.

This project builds on the work from several previous awards:

• Client Focussed Demonstration of Immune and Inflammatory Simulations - progressing the technology and business infrastructure of the University spin-out company 'Immune Modelling Solutions'
• Dissecting RNA interactions in disease: from modelling gene networks to future therapies
• Data and simulation of the dynamics of stem cell amplification in prostate disease development and control (Benign Prostatic Hyperplasia)

Read more about the project on the YCIL website.

Ouputs

Publications

• Alden, K.J., Andrews, P.S., Viega-Fernandes, H., Timmis, J., and Coles, M.C. (2014). Utilising a simulation platform to understand the effect of domain model assumptions. Natural Computing, doi: 10.1007/s11047-014-9428-7
• Alden, K. J., Read, M. N., Andrews, P. A. U. L. S., Timmis, J., & Coles, M. C. (2014). Applying spartan to Understand Parameter Uncertainty in Simulations. R Journal.
• Read, M. N., Andrews, P. A. U. L. S., Timmis, J., & Kumar, V. (2014). Modelling biological behaviours with the unified modelling language: an immunological case study and critique. Journal of the Royal Society Interface, 11(99), [20140704]. 10.1098/​rsif.2014.0704

Grants

• Paul Kaye, NC3RS, CRACK IT Challenge, Phase 2: Virtual Infectious Disease Research, £996,464