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BIRS Workshop Lecture Videos

Coupling reaction-diffusion to cell shape to unravel emergent cell signalling behaviour Maree, Stan


Pattern formation through reaction-diffusion of proteins is core to establishing functionally distinct domains within cells. In fact, cells are able to be in either a ârest stateâ, in which such proteins are distributed homogeneously along its interior, or in a âpolarisedâ state, in which clear domains establish. This phenomenon of polarization, allows cells to change shape. Animal cells move accordingly to these domains, while plant cells, encased in a rigid cellulose cell wall, use them for cell shape changes and polar transmission of signals. Molecular studies reveal that even though plants and animals diverged 1.6 billion years ago, they still share the a similar core machinery required for cell shape changes. A fascinating similarity between animal and plant cells with respect to the organization of cytoskeletal elements in the regions of active protrusive growth and cell wall extension (the `leading edges'), is paralleled by a striking conserved molecular mechanism responsible for the creation and organization of these `leading edges'. To unravel and understand the interplay and feedbacks which brings about animate cell motility, we have developed a multiscale model of a motile cells, describing how the reaction-diffusion module can be biophysically coupled to the cells' deformation. We then contrast this to the cell shape changes that occur in the pavement cells (PCs) in the leaves: PCs grow multiple lobes, which fit perfectly into the indentations of the neighboring cells, generating interdigitating, jigsaw-like patterns. Finally, we use both systems to show how polarity formation can also be used as an integrator for sensing external cues, and discuss how alterations of this could cause tissue-level disruption. Hence, we argue that part of cell signaling can be seen as an outcome of feedbacks between intracellular reaction-diffusion patterning, cell shape dynamics and external signals. Lastly, I will show how our modelling framework can also be used for segmentation of imaging data, showing examp les that range from complex epithelia to organoids.

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