Small systems in a thermodynamic medium --- like colloids in a suspension or
the molecular machinery in living cells --- are strongly affected by the
thermal fluctuations of their environment. Physicists model such systems by
means of stochastic processes. Stochastic Thermodynamics (ST) defines entropy
changes and other thermodynamic notions for individual realizations of such
processes. It applies to situations far from equilibrium and provides a unified
approach to stochastic fluctuation relations. Its predictions have been studied
and verified experimentally.
This thesis addresses the theoretical foundations of ST. Its focus is on the
following two aspects: (i) The stochastic nature of mesoscopic observations has
its origin in the molecular chaos on the microscopic level. Can one derive ST
from an underlying reversible deterministic dynamics? Can we interpret ST's
notions of entropy and entropy changes in a well-defined
information-theoretical framework? (ii) Markovian jump processes on finite
state spaces are common models for bio-chemical pathways. How does one quantify
and calculate fluctuations of physical observables in such models? What role
does the topology of the network of states play? How can we apply our abstract
results to the design of models for molecular motors?
The thesis concludes with an outlook on dissipation as information written to
unobserved degrees of freedom --- a perspective that yields a consistency
criterion between dynamical models formulated on various levels of description.Comment: Ph.D. Thesis, G\"ottingen 2014, Keywords: Stochastic Thermodynamics,
Entropy, Dissipation, Markov processes, Large Deviation Theory, Molecular
Motors, Kinesi
