Fluctuations in molecular motors: thermodynamics of F1-ATPase

In the last decades, stochastic thermodynamics has proved to be a useful framework to study nanoscale systems, such as molecular motors. It allows to infer hidden information about the system dynamics from its observable fluctuations. In this work, new strategies for thermodynamic inference are proposed, and explored in the F1ATP-ase molecular motor. The strategies are based in recent applications of martingale theory in the field of stochastic thermodynamics, in particular, in fluctuation theorems at stopping times, and in bounds for maximal excurssions of stochastic processes. These theoretical results are exploited to gain information about the dynamics of the motor, otherwise unaccessible or difficult to be measured in experiments. The evolution of the motor is described by a continuous-time markovian dynamics in a discrete state space, which is simulated using the Gillespie algorithm. Simulations are first used to test several theoretical results of interest for this work, and then to assess the accuracy and feasability of our proposals as experimental strategies.

Master Thesis by Adrian Nadal Rosa defended on 26/09/2024.