Understanding Chemotaxis Towards Self-Generated Gradients Using Computational Models, Model Organisms and T cells

Cell migration is essential to processes throughout biology, especially embryonic development and immune function. Migration must be steered to be physiologically effective.

Steering cues are not well understood; we know a lot about how cells interpret them, but relatively little about how they are generated.

The basic premise of this work is that the cells often generate their own cues, by breaking down attractants that are widely present (and thus initially give no steering information) into local gradients. Attractant breakdown and migration happen simultaneously.

This mechanism - chemotaxis up self-generated gradients (SGGs) - is hard to dissect because it is complex, and based on positive feedback loops.

We therefore propose a four-pronged, iterative approach, in which we combine less challenging components to create an understanding of the underlying biology.

Key goals are: (1) explore possible mechanisms and new extensions using computational models; (2) test outcomes using chemotactic Dictyostelium in custom microfluidic devices; (3) verify these data by establishing cultured T cells chemotaxing to CCL19 as a model SGG; (4) combine findings from parts 1-3 to make a 3D, SGG-based model of a lymph node.

Together they will illuminate chemotactic steering in general, by focussing on one physiologically important system.