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.
Chronic viral infections such as human immunodeficiency virus (HIV-1), hepatitis C virus (HCV) and human T cell leukemia virus (HTLV-1) are marked by huge between-individual variation in outcome. Some people infected with HIV-1 will develop AIDS in less than 5 years others will remain healthy for 10 years or more. In HCV infection, some individuals spontaneously clear the virus others develop persistent infection and subsequent risk of liver failure. Similarly in HTLV-1 infection, some individuals remain lifelong healthy carriers of the virus whilst others will develop an aggressive, rapidly fatal leukemia. Full Abstract
14:20
Omer Dushek
(Invited Speakers)
Control of T cell responses by accessory receptors revealed by phenotypic modelling
T cells are important immune cells that are routinely being exploited for a number of different therapies. They are activated to respond when ligands bind to various receptors on their surface. This binding initiates signalling pathways that ultimately induce responses important for clearing infections and cancers. A key open question is how ligation of different surface receptors quantitatively control their responses. To address this, we have been systematically stimulating T cells with different combinations of ligands (input) and measuring their responses (output). Using systematic mathematical inference algorithms, we identify effective pathway models that intuitively explain how inputs are converted to outputs (‘causal inference’). Here, we show that T cell response outputs to constant antigen ligand input induces perfect adaptation and that ligation of different accessory receptors (CD2, CD28, LFA-1, CD27, 4-1BB, GITR, and OX40) control this phenotype differently. Initial results with the inference algorithm suggest that an incoherent feedforward coupled to a digital switch can explain perfect adaptation along with the different phenotypes observed by the different receptors. The work offers a new way to infer effective signalling pathways directly from quantitative cellular response data. Full Abstract
14:40
Jonathan Wagg
(Invited Speakers)
Application of Artificial Neural Networks to Infer Pharmacological Molecular-Level Mechanisms of Drug Evoked Clinical Responses
The Roche Clinical Pharmacology Disease Modelling Group (CPDMG) aims to better understand the biological basis of observed inter-patient variability of the clinical responses to drugs administered both as monotherapies and in combination. Administration of drugs to human subjects drives widespread and diverse changes in their biology. However, the majority of these changes are NOT clinically relevant, generate noise and are a common source of false positive predictors of clinical response. Full Abstract
