- Jonathan Wagg, Roche
- Omer Dushek, University of Oxford
- Becca Asquith, Imperial College London
- Tim Elliott, Professor of Experimental Medicine
The immune system poses special challenges to a systems level understanding. Its ability to recognise specific antigens is characterised by massive combinatorial complexity; played out in a system that at the population level is highly polymorphic; with multiple cell-types that form ad hoc functional assemblies in response to potential threats to life. However, the introduction of computationally-intensive approaches like machine learning and systems modelling to experimental immunological data, gathered at various levels of complexity, is bringing us closer to predicting high-order function from measurements of components belonging to interdependent systems.
We will gather world leaders from diverse backgrounds to discuss the scientific progress that is being made integrating a wide variety of biological data to increase our understanding of immunological processes at all levels, as well as the challenges we face in the future.
|Immune cell dynamics & control of persistent virus infection
|Control of T cell responses by accessory receptors revealed by phenotypic modelling
|Application of Artificial Neural Networks to Infer Pharmacological Molecular-Level Mechanisms of Drug Evoked Clinical Responses