Biomedical and Bioinformatics Challenges for Computer Science (BBC) Session 2

Time and Date: 15:45 - 17:25 on 11th June 2018

Room: M7

Chair: Rodrigo Weber dos Santos

383 Development of Octree-Based High-Quality Mesh Generation Method for Biomedical Simulation [abstract]
Abstract: This paper proposes a robust high-quality finite element mesh generation method which is capable of modeling problems with complex geometries and multiple materials and suitable for the use in biomedical simulation. The previous octree-based method can generate a high-quality mesh with complex geometries and multiple materials robustly allowing geometric approximation. In this study, a robust mesh optimization method is developed combining smoothing and topology optimization in order to correct geometries guaranteeing element quality. Through performance measurement using sphere mesh and application to HTO tibia mesh, the validity of the developed mesh optimization method is checked.
Keisuke Katsushima, Kohei Fujita, Tsuyoshi Ichimura, Muneo Hori and Lalith Maddegedara
258 1,000x Faster than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators [abstract]
Abstract: Logistic regression as implemented in PLINK is a powerful and commonly used framework for assessing gene-gene (GxG) interactions. However, fitting regression models for each pair of markers in a genome-wide dataset is a computationally intensive task. Performing billions of tests with PLINK takes days if not weeks, for which reason pre-filtering techniques and fast epistasis screenings are applied to reduce the computational burden. Here, we demonstrate that employing a combination of a Xilinx UltraScale KU115 FPGA and an Nvidia Tesla P100 GPU leads to runtimes of only minutes for logistic regression GxG tests on a genome-wide level. In particular, a dataset with 53,000 samples genotyped at 130,000 SNPs was analyzed in 8 minutes, resulting in a speedup of more than 1,000 when compared to PLINK v1.9 using 32 threads on a server-grade computing platform. Furthermore, on-the-fly calculation of test statistics, p-values and LD-scores in double precision make commonly used pre-filtering strategies obsolete.
Lars Wienbrandt, Jan Christian Kässens, Matthias Hübenthal and David Ellinghaus
280 Combining Molecular Dynamics Simulations and Informatics to Model Nucleosomes and Chromatin [abstract]
Abstract: Nucleosomes are the fundamental building blocks of chromatin, the biomaterial that houses the genome in all higher organisms. A nucleosome consist of 145-147 base pairs of double strained DNA wrapped approximately 1.7 times around eight histones. There are almost 100 atomic resolution structures of the nucleosome available from the protein data bank. Collectively they explore histone mutations, species variations, binding of drugs and ionic effects, but only three sequences of DNA. Given a four-letter code (A, C, G, T) for DNA there are on the order of 4^147 ~ 10^88 possible sequences of DNA that can form a nucleosome. Exhaustive studies are not possible. Fortunately, next generation sequencing enables researchers to identify a single nucleosome of interest, and today’s super computing resources enable simulation ensembles representing different realizations of the nucleosome to be accumulated overnight as a means of investigating its structure and dynamics. Here we present a workflow that integrates molecular simulation and genome browsing to manage such efforts. The workflow is exploited to study nucleosome positioning in atomic detail and its relation to chromatin folding in coarse-grained detail. The exchange of data between physical and informatics models is bidirectional. This allows cross validation of simulation and experiment and the discovery of structure‑function relationships. All simulation and analysis data from the studies are available on the TMB-iBIOMES server:
Ran Sun, Zilong Li and Thomas Bishop
169 A Stochastic Model to Simulate the Spread of Leprosy in Juiz de Fora [abstract]
Abstract: The Leprosy, also known as Hansen's disease, is an infectious disease in which the main etiological agent is the Mycobacterium leprae. The disease mainly affects the skin and peripheral nerves and can cause physical disabilities. For this reason, represents a global public health concern, especially in Brazil, where more than twenty-five thousand of new cases were reported in 2016. This work aims to simulate the spread of Leprosy in a Brazilian city, Juiz de Fora, using the SIR model and considering some of its pathological aspects. SIR models divide the studied population into compartments in relation to the disease, in which S, I and R compartments refer to the groups of susceptible, infected and recovered individuals, respectively. The model was solved computationally by a stochastic approach using the Gillespie algorithm. Then, the results obtained by the model were validated using the public health records database of Juiz de Fora.
Vinícius Clemente Varella, Aline Mota Freitas Matos, Henrique Couto Teixeira, Angélica Da Conceição Oliveira Coelho, Rodrigo Santos and Marcelo Lobosco