Soutenance de thèse: Enrico Lorenzetti

Quand

05/11/2024    
14:00 - 16:00

LPTMS (100% online seminar)
LPTMS - Bâtiment Pascal n° 530 rue André Rivière - Université Paris-Saclay, Orsay, 91405

Type d’évènement

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Modelling And Data Analysis of Protein Dynamics applied to a Fission Yeast Mechanosensor

 

Enrico Lorenzetti (Ladhyx, LPTMS)

 

Intracellular dynamics is fundamental for cells to maintain homeostasis and respond to environmental stimuli. Among these, mechanical forces can be a potential source of damage as they can compromise the integrity of the cell. To cope with this risk, living organisms are endowed with mechanosensors, i.e. receptor at the sub-cellular level able to trigger a biological pathway by a mechanical signal. In fission yeast, a mechanosensor, Wsc1 protein, perceives excessive stress on the the cell wall and activates the glucan synthesis to keep this layer reinforced. More interestingly, Wsc1 concentration in- creases in the compressed region of the cell wall for- ming clusters. This work investigates this mechanosensitive clustering behaviour advancing models and inference method for
experimental data of protein dynamics.
By setting a mathematical framework based on deterministic partial differential equations, I describe the Wsc1 dynamics along the cell wall. In this model, I consider two possible protein recruitment mechanisms for shaping clusters, either from the sides due to diffusion along the cell wall and from the cytoplasm by exocytotosis. Moreover, following chemical considerations, I suppose an affinity
between the cell wall and the protein that increases with the cell wall com- pression. The resulting reaction-diffusion equations obtained by this model are able to reproduce the clustering behaviour after cell wall compression. In addition, the model correctly predicts a longer time-scale of the dynamics in the compressed region of the cell wall. This result is in agreement with the outcomes of FRAP (Fluorescence Recovery After Photobleaching) experiment, whose analysis is based on the study of time-lapse images that reflects the spatial-temporal concentration of the molecule. However, it is not clear yet if the protein recruitment is due to diffusion, exchange with cytoplasm, or both. For this reason, in my work I also develop a new inference method for FRAP experiment capable of discerning different types of dynamics. My analysis aims at quantifying the dynamical parameters, such as diffusion coefficient and exchange rate, by minimising the distance between the reaction-diffusion model prediction and actual data. The specificity of my approach is the use of dimensional reduction to efficiently perform computation without having knowledge of the initial bleached profile. This new method is then tested and validated on artificial data. The results show that this analysis is flexible since it can work with imperfect data, where the signal-to-noise ratio is low, the number of frames is reduced and the spatial window is restricted.
Moreover, this approach can be potentially generalised to complex geometries, for instance curved surface. This versatility is wellsuited for studying protein dynamics in the fission yeast cell wall. The inference method is applied to experimental data of another mechanosensor in the cell wall, Mtl2, yielding reasonable values of diffusion coefficient. Nevertheless, it still needs to be tested on real
data of Wsc1 protein.
Overall, this study offers novel methodologies for quantifying and understanding intricate protein dynamics within cells and tissues.

Localization: amphithéâtre Becquerel – École Polytechnique.

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