Stochastic modeling of motor proteins

University dissertation from Stockholm : KTH

Abstract: Motor proteins are microscopic biological machines that convert chemical energy into mechanical motion and work. They power a diverse range of biological processes, for example the swimming and crawling motion of bacteria, intracellular transport, and muscle contraction. Understanding the physical basis of these processes is interesting in its own right, but also has an interesting potential for applications in medicine and nanotechnology.The ongoing rapid developments in single molecule experimental techniques make it possible to probe these systems on the single molecule level, with increasing temporal and spatial resolution. The work presented in this thesis is concerned with physical modeling of motor proteins on the molecular scale, and with theoretical challenges in the interpretation of single molecule experiments.First, we have investigated how a small groups of elastically coupled motors collaborate, or fail to do so, when producing strong forces. Using a simple model inspired by the motor protein PilT, we ?nd that the motors counteract each other if the density becomes higher than a certain threshold, which depends on the asymmetry of the system.Second, we have contributed to the interpretation of experiments in which the stepwise motion of a motor protein is followed in real time. Such data is naturally interpreted in terms of ?rst passage processes. Our main conclusions are (1) Contrary to some earlier suggestions, the stepping events do not correspond to the cycle completion events associated with the work of Hill and co-workers. We have given a correct formulation. (2) Simple kinetic models predict a generic mechanism that gives rise to correlations in step directions and waiting times. Analysis of stepping data from a chimaeric ?agellar motor was consistent with this prediction. (3) In the special case of a reversible motor, the chemical driving force can be extracted from statistical analysis of stepping trajectories.