Motor proteins are able to transform the chemical energy of ATP hydrolysis into mechanical work that is essential for a variety of tasks in living cells. Major advances in single molecule nanomanipulation have made it possible to measure the displacements of these proteins along linear tracks, and therefore to obtain an approximate description of their kinetics. Discrete and continuous stochastic models are particularly suited to the interpretation of experimental data in this field, since both allow a complicated mechano-chemical process to be coarse grained in relatively few parameters. However both of them present advantages and shortcomings, though usually one model succeeds where the other fails. We have recently developed a coarse graining procedure, based on a renormalization group approach, that accounts for the force dependence of transition rates in discrete models, bridging a gap between the two stochastic approaches. We discuss the main results obtained with this procedure and possible future directions of investigation.

Coarse grained models: the kinetics of motor proteins

LATTANZI, GIANLUCA;
2004

Abstract

Motor proteins are able to transform the chemical energy of ATP hydrolysis into mechanical work that is essential for a variety of tasks in living cells. Major advances in single molecule nanomanipulation have made it possible to measure the displacements of these proteins along linear tracks, and therefore to obtain an approximate description of their kinetics. Discrete and continuous stochastic models are particularly suited to the interpretation of experimental data in this field, since both allow a complicated mechano-chemical process to be coarse grained in relatively few parameters. However both of them present advantages and shortcomings, though usually one model succeeds where the other fails. We have recently developed a coarse graining procedure, based on a renormalization group approach, that accounts for the force dependence of transition rates in discrete models, bridging a gap between the two stochastic approaches. We discuss the main results obtained with this procedure and possible future directions of investigation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/9525
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