Ramon Grima, University of Edinburgh

Spatial stochastic models of intracellular dynamics in dilute and crowded conditions

Stochastic effects in biochemical reaction systems are commonly studied by means of the Reaction-Diffusion Master equation (RDME). The RDME is computationally efficient and has the advantage of being amenable to analysis. However the RDME assumes point particle interactions, i.e. dilute conditions. This presents a problem because the intracellular environment can be highly crowded with up to 40% of its volume being occupied by various macromolecules. In this talk I will discuss our recent work showing how the RDME can be modified to take into account volume-excluded interactions. This can be solved in certain conditions yielding explicit expressions for the dependence of reactant number fluctuations on the available volume fraction of space. I will also discuss how one can use the modified RDME to derive coupled nonlinear partial differential equations which describe molecular movement in highly heterogeneous crowded environments and which hence offer a realistic alternative to the classical diffusion equation. All results will be contrasted with those obtained from Brownian dynamics.

Freddy Bouchet, ENS de Lyon et CNRS

Climate extremes and rare trajectories in astronomy computed using rare event algorithms and large deviation theory

I will discuss a set of recent developments in non-equilibrium statistical mechanics applied to climate and the solar system dynamics. The first application will be extreme heat waves as an example of rare events with huge impacts. Using a large deviation algortihm with a climate model (GCM), we were able to gain two orders of magnitude for the estimation of very rare events, and study phenomena that can not be studied otherwise. The second application will be the study of rare trajectories that change the structure of a planetary system. Their understanding involves large deviation and instanton theory.

Tobias Kramer, ZIB

"What is... efficiently solving hierarchical equations of motion?"

The hierarchical equations of motions provide an exact solution for open quantum system dynamics, also for larger systems. An important application is the description of energy transfer in photosynthetic systems from the antenna to the reaction center and the computation of the corresponding time-resolved spectra. The methods captures non-Markovian effects and strong system-environment interactions, which affect the thermalization and decoherence process. On parallel and distributed computers we provide an efficient implementation of the method [1], which is also available as GPU cloud computing tool at nanohub.org [2]. The obtained data is efficiently compressed by using neural networks and machine learning [3].

[1] T. Kramer, M. Noack, A. Reinefeld, M. Rodriguez, Y. Zelinskyy: Efficient calculation of open quantum system dynamics and time-resolved spectroscopy with Distributed Memory HEOM (DM-HEOM); Journal of Computational Chemistry (2018),
[2] nanoHUB.org: Exciton Dynamics Lab for Light-Harvesting Complexes (GPU-HEOM)
[3] M. Rodriguez, T. Kramer: Machine Learning of Two-Dimensional Spectroscopic Data

Felix Höfling, Freie Universität Berlin

"What is... massively parallel computing?"

In the past two decades, computing architectures have seen a paradigm shift towards on-chip parallelisation, which has been perfected in many-core accelerators (GPUs, MICs). Such processors have boosted the current success of machine learning approaches and, being versatile devices for general computing tasks, find applications in diverse other fields. A single chip can perform several thousand computations simultaneously, at the price of a reduced flexibility of the instruction pipeline. Making this technology accessible to a larger class of applications requires numerical tasks that exhibit a massive parallelism, which may be achieved by rethinking our implementations of standard algorithms.

Michiel Renger, WIAS Berlin

What are... Reaction Fluxes?

As usual in thermodynamics (and many SFB1114 projects), chemical reaction networks can be studied on at least two different scales: a microscopic system of randomly reacting particles, and macroscopic concentrations that follow the path of minimal action. Mathematically, the challenge is to bridge both levels of description, and to study further scaling limits using the action functional. Physically, one can study the action to derive thermodynamic properties of the system. Our main philosophy is that both mathematically and physically, it is beneficial to take more information into account than just the concentrations...

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