The project aims at gathering physical, biological and statistical expertise to build a reliable inference procedure for the analysis of SPT experiments.
A funded post-doc position is opened in 201601 January 2016 A funded post-doc position is opened in 2016
Stochastic Dynamical Systems in Biology: Numerical Methods and Applications4 January 2016 – 24 June 2016 Isaac Newton Institute, Cambridge, UK
Transport of macromolecules, organelles and vesicles in living cells is a very complicated process that essentially determines and controls many biochemical reactions, growth and functioning of cells.
The passive thermal diffusion through the overcrowded cytoplasm is combined with the active transport by motor proteins attached to microtubules. This intricate mechanism results in anomalous diffusions that found abundant experimental evidences but no consensus on the physical mechanism and the appropriate mathematical model is achieved so far.
Single-particle tracking (SPT) experiments (video-tracking, optical tweezers, etc.) survey random trajectories of individual tracers inside living cells and can thus provide the missing information on the molecular transport in order to discriminate between different physical mechanisms (e.g., “caging” in the overcrowded cellular environment, visco-elastic properties of the cytoskeleton, hierarchical geometrical structure of the cytoplasm and/or nucleus, etc.) and to identify the appropriate theoretical model of anomalous diffusion (e.g., continuous-time random walks, generalized Langevin equation, diffusion on fractals, etc.).
In SPT, an ensemble average of the quantities of interest (e.g., diffusivity, viscosity, first passage times, etc.) is often unavailable or even undesired, as tracers move in spatially heterogeneous and time evolving media such as living cells. One faces therefore a challenging problem of inferring dynamical, structural and functional properties of living cells from a limited (small) number of individual random realizations of an unknown stochastic process.