Modeling Microtubule Dynamics in Mitosis

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DESCRIPTION (provided by applicant): In cell division the actions of many molecules are integrated to mechanically segregate two complete genomes from each other via motions directly coupled to the dynamics of kinetochore microtubules. The extent of molecular-level-information relevant to cell division has increased substantially with the convergence of molecular biology and high-resolution digital light microscopy of living fluorescent protein- transfected cells. A major challenge now is to develop an understanding of cellular-level mechanisms from the vast amounts of quantitative molecular-level data. Importantly, the dynamic behavior of individual kinetochore microtubules remains to be determined. Fortunately, advances in high-speed computing make it increasingly practical to model complex processes such as microtubule dynamic instability and associated chromosome motions. The objective of this project will be to develop computer-based models to test hypotheses for the mechanisms controlling kinetochore microtubule dynamics in mitosis. Specifically, we will develop computational models to predict the separate and combined effects of motor-based polar ejection forces, stable chemical gradients, and mechanical tension in budding yeast mitosis. We will also develop reaction-diffusion models that generate the hypothesized chemical gradients, develop a theory for microtubule behavior in such gradients, and test the theory in LLCPK cells. In addition, we will develop an integrated mechanochemical model of microtubules embedded in the budding yeast kinetochore and test how mechanical force on the kinetochore can affect microtubule stability. In all cases models will be developed in ongoing collaborations with cell biologists, and the predictions compared directly to experimental observations. To facilitate these quantitative comparisons, we will implement models of high- resolution light microscopy to produce synthetic digital images of the fluorescent molecules in living cells and directly compare the model-predicted statistics to those obtained experimentally.