System Level Mathematical Analysis of Mitosis

Mitotic spindle goes through distinct morphological states characterized by increasing spindle length and distances between chromosomes. A complete picture of how the spindle assembles is still lacking. We performed an In Silico model screen to identify all potential mechanisms of spindle self-organization. We trained' the computer to assemble a set of models and screened the models in a multi-dimensional parameter space. To identify models that fit experimental data we used stochastic optimization and genetic algorithms. We found multiple models quantitatively describing the spindle in which the timing of force activity must be fine tuned, in contrast to the kinetic and mechanical parameters that show robustness to change.

Alex Mogilner graduated in 1985 with an M.Eng. in Engineering Physics from the Ural Polytechnic Institute in Sverdlovsk (then USSR), followed by a Ph.D. in Physics from the USSR Academy of Sciences in 1990. He then obtained his second Ph.D. in Applied Mathematics at the University of British Columbia in 1995. His main fields of expertise are mathematical biology and cell and molecular biophysics. Specifically, he investigates biological movements on the molecular, cellular, and multicellular levels.