Modelling intermediate filament networks

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The intermediate filament (IF) network is part of the cytoskeleton, an intracellular structure made of proteins polymerized in filaments forming networks in the cytoplasm. The IF network is mainly involved in the stabilization and mechanical resilience of the cell, cell migration and signal transduction. The organization of cytoskeletal networks is the main determinant of their mechanical properties and so their functions in the cell. Defects in the assembly or the transport of cytoskeletal proteins, resulting in network misorganizations, are linked to the pathogenesis of many diseases. Combining experimental and mathematical modelling approaches allows a rigorous validation of model hypotheses, thereby constituting a very powerful and inexpensive tool of investigation in cell biology. Working in close collaboration with experimentalists, I propose to develop mathematical and computational frameworks to extensively investigate the assembly and organization of IFs at the individual filament level, but also at the level of the entire network. Mathematical and computational models will be designed by taking into account both the biochemical and physical properties of IFs. Models will be quantitatively and qualitatively studied to extract transient and asymptotic behaviour. For model calibration and validation, model responses will be compared with experimental data. Model selection will be used to identify the best models amongst competing models. Using this approach, my research will provide experimentalists with an analytical framework allowing 1) the quantification of the assembly dynamics of different types of IFs, 2) the in silico investigation of IF network formation and their viscoelastic properties and 3) the characterization of movements of IF material in cells. Understanding the organization mechanisms of the IF networks in cells and the link between network architectures and their viscoelastic properties is essential to elucidate the IF functions in cells and could lead to new therapeutic strategies.