This workshop focuses on the complexity of dynamics and kinetics in systems from single molecules to cells. Our aim is to bring several key concepts -- such as energy landscapes, state space and conformational networks, probability inverse methods, polymer theory, statistical physics and beyond -- to deepen and integrate fundamental concepts relevant from the level of single molecules to cells.

For instance, current biophysical imaging and spectroscopy methods can probe time (< 10^−6 s), length (10^−9 m) and force (10^−12 N) scales relevant to the life cycle of a cell. Despite the wealth of experimental data, our ability to gain meaningful insight from processes occurring at such small scales is limited by fundamental challenges common to all complex dynamical systems: current experimental and theoretical methods cannot capture complex processes in their full multi-dimensional detail.

At best, they provide a small slit through the curtains of the intricate cellular theatre on display by probing complex processes along just one or a few relevant observable coordinates. Our focus will therefore be on building principled models for complex biological and physical systems directly inspired from experiments and computer simulations as well as generalizing theoretical frameworks relevant to complex biophysical phenomena.

We will bring together experimentalists and theorists from diverse fields to motivate discussions along the following broad topics:

1. What experimental spectroscopy or imaging techniques under current or future development would provide a broader, more complete and multi-dimensional, description of biophysical kinetics?

2. What is a convenient mathematical language for complex kinetics that would help understand, rather than fit, specific in silico, spectroscopy and imaging experiments? How do we maximize the predictive power of such models in a principled fashion while reducing their dependency on adjustable parameters? Related subjects include, for example, memory effects, multiple pathways on energy landscapes, networks and their relations to information processing of molecules, equilibrium and nonequilibrium properties.

3. How can we take full advantage of entire data sets from experiments or simulations in building models such as complex networks or generalized Langevin dynamics in steady-state and nonsteady-state environments?

4. Very broadly: When is a model too complicated in biophysics? When is a model too simple? Do the types of experiments from which we gather data set fixed bounds on what we can or cannot ask?