Abstract: In recent years, highly parallelized simulations of blood flow resolving individual blood cells have been demonstrated. Simulating such dense suspensions of deformable particles in flow often involves a partitioned fluid-structure interaction (FSI) algorithm, with separate solvers for Eulerian fluid and Lagrangian cell grids, plus a solver - e.g., immersed boundary method - for their interaction. Managing data motion in parallel FSI implementations is increasingly important, particularly for inhomogeneous systems like vascular geometries. In this study, we evaluate the influence of Eulerian and Lagrangian halo exchanges on efficiency and scalability of a partitioned FSI algorithm for blood flow. We describe an MPI+OpenMP implementation of the immersed boundary method coupled with lattice Boltzmann and finite element methods. We consider how communication and recomputation costs influence the optimization of halo exchanges with respect to three factors: immersed boundary interaction distance, cell suspension density, and relative fluid/cell solver costs.

Abstract: Many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions. Previously, we have proposed a modeling framework, referred to as Evo-Lexis, that provides insight to some fundamental questions about evolving hierarchical systems. The predictions of the Evo-Lexis model should be tested using real data from evolving systems in which the outputs can be well represented by sequences. In this paper, we investigate the time series of iGEM synthetic DNA dataset sequences, and whether the resulting iGEM hierarchies exhibit the qualitative properties predicted by the Evo-Lexis framework. Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset. Although this results in development of less cost-efficient and less deep Lexis-DAGs, the dataset exhibits a bias in reusing specific nodes more often than others. This results in the Lexis-DAGs to take the shape of an hourglass with relatively high H-score values and stable set of core nodes. Despite the reuse bias and stability of the core set, the dataset presents a high amount of diversity among the targets which is in line with modeling of Evo-Lexis.

Abstract: Helices appear in nature at many scales, ranging from molecules to tendrils in plants. Organisms take advantage of the helical shape to fold, propel and assemble. For this reason, several applications in micro and nanorobotics, drug delivery and soft-electronics have been suggested. On the other hand, biomolecules can form complex tertiary structures made with helices to accomplish many different functions. A particular well-known case takes place during cell division when DNA, a double helix, is packaged into a super-helix – i.e., a helix made of helices – to prevent DNA entanglement. DNA super-helix formation requires auxiliary histone molecules, around which DNA is wrapped, in a "beads on a string" structure. The idea of creating superstructures from simple elastic filaments served as the inspiration to this work. Here we report a method to produce ribbons with complex shapes by periodically creating strains along the ribbons. Ribbons can gain helical shapes, and their helicity is ruled by the asymmetric contraction along the main axis. If the direction of the intrinsic curvature is locally changed, then a tertiary structure results, similar to the DNA wrapped structure. In this process, auxiliary structures are not required and therefore new methodologies to shape filaments, of interest to nanotechnology and biomolecular science, are proposed.

Abstract: In this paper, we model influenza propagation in the Russian setting using a spatially explicit model and a detailed human agent database as its input. The aim of the research is to assess the applicability of this modeling method using influenza incidence data for 2010-2011 epidemic outbreak in Saint Petersburg and to compare the simulation results with the output of the compartmental SEIR model for the same outbreak. For this purpose, a synthetic population of Saint Petersburg was built and used for the simulation via FRED open source modeling framework. The parameters related to the outbreak (background immunity level and effective contact rate) are assessed by calibrating the compartmental model to incidence data. We show that the current version of synthetic population allows the agent-based model to reproduce real disease incidence.