People Involved: Remy Kusters
Keywords: Biophysics; Cytoskeleton; Data Science; Machine learning
During its lifecycle, a cell and its intracellular components constantly adapt their shape, transport intracellular cargo throughout the cell and intimately interact with the environment. Key in both the mechanical properties and the intracellular transport in the cell is the cytoskeleton, a fibrous network composed of proteins with widely varying mechanical properties, aided by a wealth of helper and motor proteins. This skeleton is a dynamic and adaptive structure whose structural and chemical components are in constant flux with the environment. Spatially heterogeneous organization of the cell and its cytoskeleton, combined with coordinated transportation mechanisms, serve to initiate and develop function throughout the cell, leading to large scale organization both at the (intra)cellular and tissue level. Deciphering how growth of these cytoskeletal features can initiate and further develop localized structures requires information on the mechanics and transport mechanisms of the cell. To date, most of the proteins required to assemble the cytoskeleton are identified, but explaining how even simple quantitative features such as size and shape are determined remains challenging.
Here at CRI, I will develop a classical bottom-up modeling approach to distill how active intracellular forces give rise to structural organization in the cell and how this organization alters the transport of intracellular cargo. In symbiosis with this bottom-up approach I will create a data-driven inference platform to extract the underlying physical equations or patterns from experimental (bio)physical data. This allows us to infer and test biophysical models of cytoskeletal mechanics in order to characterize the mechanical response of the cellular environment and investigate its relationship with intracellular transport, both in-vitro as well as in reconstituted systems. Alongside this cytoskeletal application, the developed algorithm will be further expanded into a platform allowing the scientific community to validate existing models for transport, mechanics and hydrodynamics in a (bio)physical context. This tool will also provide students the opportunity to infer simple physical laws using experimental input, contributing to an intuitive approach to teach physical laws (diffusion, laws of motion, etc.), based on the analysis of experimental data.