Participants:
BIU, ISI.
Objectives:
· Understand the way B and T lymphocytes are selected and the mechanisms leading to autoimmunity.
· Produce tools to differentiate regular from autoimmune based on B lymphocyte proliferation properties.
· Asses the interplay between spatial structure, Ig sequence mutation and B lymphocyte kinetics in primary and secondary repertoire generation
Description of work:
We will use the general framework developed within the AB model to study the effects of localization in cellular systems, mainly in the context of B lymphocytes in mice. We will collaborate with Labs mainly in the US, supplying a large amount kinetic (Brdu and CASP staining, population size and properties as a function of time) and genomic (Ig sequence) data. We will build simple models to explain the observed results. We will attempt to use a simple toy model to explain the robustness of processes such as affinity maturation and clonal selection to varying conditions. The project will involve the development of new models and their application to biological systems, while maintaining the models simple enough to be tractable. The comparison between simple model and complex results are a direct field validation of our results regarding the emergence of a global behaviour from microscopic noise.
Deliverables:
D5.1 Bioinformatics Tool to assess mutation, division and deletion rate from localized B cell micro-dissections clonal data. Month 9.
D5.2 Development of a coherent model of B cell selection through the interplay between activation and deletion. Month 24.
D5.3 Development of a simulation tool and analytical methods to translate lymphocite kinetic data (such as BrdU and Casp staining) into estimates of division rate and selection forces for the direct use of immunologists. Month 36.
D5.4 Proposed strategies to prevent the emergence of high affinity IgG autoimmune B cell clones. Month 48.
Milestones and expected result:
This WP is heavily based on immunological data supplied by a number of collaborating labs in Yale, UPenn and Chicago University. We will assess the validity of our model through its capacity to explain the large amount of kinetic and genomic data that we currently have. At the end of the first year, we will check with our collaborators if the framework we have developed is rich enough to explain all the observed data, and if we should proceed with the concept of emerging properties, or move to more complex (yet less tractable) models.