Pseudo-marginal approximation to the free energy in a micro-macro Markov chain Monte Carlo method.

We introduce a generalized micro-macro Markov chain Monte Carlo (mM-MCMC) method with pseudo-marginal approximation to the free energy that is able to accelerate sampling of the microscopic Gibbs distributions when there is a time-scale separation between the macroscopic dynamics of a reaction coordinate and the remaining microscopic degrees of freedom. The mM-MCMC method attains this efficiency by iterating four steps: (i) propose a new value of the reaction coordinate, (ii) accept or reject the macroscopic sample, (iii) run a biased simulation that creates a microscopic molecular instance that lies close to the newly sampled macroscopic reaction coordinate value, and (iv) microscopic accept/reject step for the new microscopic sample. In the present paper, we eliminate the main computational bottleneck of earlier versions of this method: the necessity to have an accurate approximation of free energy.

Coarse-grained molecular dynamics simulations of polymerization with forward and backward reactions.

We develop novel parallel algorithms that allow molecular dynamics simulations in which byproduct molecules are created and removed because of the chemical reactions during the molecular dynamics simulation. To prevent large increases in the potential energy, we introduce the byproduct molecules smoothly by changing the non-bonded interactions gradually. To simulate complete equilibrium reactions, we allow the byproduct molecules attack and destroy created bonds.

Reverse mapping method for complex polymer systems.

We present a comprehensive approach for reverse mapping of complex polymer systems in which the connectivity is created by the simulation of chemical reactions at the coarse-grained scale. Within the work, we use a recently developed generic adaptive reverse mapping procedure that we adapt to handle the varying connectivity structure resulting from the chemical reactions. The method is independent of the coarse-grained and fine-grained force-fields by design and relies only on a single control parameter.

A historical perspective of nutrient change impact on an infectious disease in Daphnia.

Changes in food quality can play a substantial role in the vulnerability of hosts to infectious diseases. In this study, we focused on the genetic differentiation of the water flea Daphnia magna towards food of different quality (by manipulating C:N:P ratios) and its impact on the interaction with a virulent infectious disease, "White Fat Cell Disease (WFCD)". Via a resurrection ecology approach, we isolated two Daphnia subpopulations from different depths in a sediment core, which were exposed to parasites and a nutrient ratio gradient in a common garden experiment.

Generic Adaptive Resolution Method for Reverse Mapping of Polymers from Coarse-Grained to Atomistic Descriptions.

In this paper, we propose a new generic approach for reverse mapping from coarse-grained to atomistic scale based on the adaptive resolution scheme (AdResS). In AdResS simulation, two spatial domains, modeled at two different scales, are brought together in a concurrent simulation by defining a hybrid region where particles can switch representation from one model to another. We use AdResS as a central part of a reverse mapping algorithm from a different perspective by treating the whole simulation box as a hybrid region and changing the resolution as a function of time during the course of a molecular dynamics simulation.

Towards an efficient multiscale modeling of low-dimensional reactive systems: study of numerical closure procedures.

We study a numerical closure approach for systems of chemically reacting systems [corrected] on lattices with low-dimensional support, for which a mean-field approximation is insufficiently accurate because of lateral interaction on the lattice. We introduce a hierarchy of macroscopic state variables, taking particle clusters into account, whose time evolution is obtained via microscopic (kinetic Monte Carlo) simulation. The macroscopic state variables are chosen such that they can be [corrected] straightforwardly conserved during reconstruction of a microscopic configuration (the so-called lifting step).

Equation-free multiscale computation: algorithms and applications.

In traditional physicochemical modeling, one derives evolution equations at the (macroscopic, coarse) scale of interest; these are used to perform a variety of tasks (simulation, bifurcation analysis, optimization) using an arsenal of analytical and numerical techniques. For many complex systems, however, although one observes evolution at a macroscopic scale of interest, accurate models are only given at a more detailed (fine-scale, microscopic) level of description (e.g.