scholarly journals BioDynaMo: a modular platform for high-performance agent-based simulation

Author(s):  
Lukas Breitwieser ◽  
Ahmad Hesam ◽  
Jean de Montigny ◽  
Vasileios Vavourakis ◽  
Alexandros Iosif ◽  
...  

Abstract Motivation Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. Results We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology, and epidemiology. For each use case we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. Availability BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in supplementary information. Supplementary information Available at https://doi.org/10.5281/zenodo.5121618.

2020 ◽  
Author(s):  
Lukas Breitwieser ◽  
Ahmad Hesam ◽  
Jean de Montigny ◽  
Vasileios Vavourakis ◽  
Alexandros Iosif ◽  
...  

AbstractComputer simulation is an indispensable tool for studying complex biological systems. In particular, agent-based modeling is an attractive method to describe biophysical dynamics. However, two barriers limit faster progress. First, simulators do not always take full advantage of parallel and heterogeneous hardware. Second, many agent-based simulators are written with a specific research problem in mind and lack a flexible software design. Consequently, researchers have to spend an unnecessarily long time implementing their simulation and have to compromise either on model resolution or system size.We present a novel simulation platform called BioDynaMo that alleviates both of these problems researchers face in computer simulation of complex biological systems. BioDynaMo features a general-purpose and high-performance simulation engine. The engine simulates cellular elements, their interactions within a 3D physical environment, and their cell-internal genetic dynamics. Cell-internal dynamics can be described in C++ code or using system biology markup language (SBML).We demonstrate BioDynaMo’s wide range of application with three example use cases: soma clustering, neural development, and tumor spheroid growth. We validate our results with experimental data, and evaluate the performance of the simulation engine. We compare BioDynaMo’s performance with a state-of-the-art baseline, and analyze its scalability. We observe a speedup of 20–124× over the state-of-the-art baseline using one CPU core and a parallel speedup between 67× and 76× using 72 physical CPU cores with hyperthreading enabled. Combining these two results, we conclude that, on our test system, BioDynaMo is at least three orders of magnitude faster than the state-of-the-art serial baseline. These improvements make it feasible to simulate neural development with 1.24 billion agents on a single server with 1TB memory, and 12 million agents on a laptop with 16GB memory.BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org.Author summaryComputer simulations of biological systems are crucial to gain insights into complex processes of living organisms. However, the development of increasingly large and complex simulations is a difficult task, partly because a strong background in biology as well as computer science is required. In this paper, we introduce BioDynaMo, an agent-based simulation platform with which life scientists can create simulations that are three orders of magnitude faster than the state-of-the-art baseline. By taking advantage of the latest developments in computing hardware, we build a platform that is highly optimized. This enables the simulation of 1.24 billion agents on a single server and 12 million agents on a laptop. BioDynaMo places a lot of focus on hiding computational complexity and providing an easy-to-use interface, such that the life scientist can concentrate on biological aspects, rather than computational. BioDynaMo helps scientists to translate an idea quickly into a simulation by providing common building blocks, and a modular and extensible software design. We analyze the performance of the platform and demonstrate the capabilities with three example use cases: soma clustering, neural development, and tumor spheroid growth. The results support the view that BioDynaMo will help open up new research opportunities for large-scale biological simulations.


2017 ◽  
Vol 68 ◽  
pp. 59-73 ◽  
Author(s):  
Francisco Borges ◽  
Albert Gutierrez-Milla ◽  
Emilio Luque ◽  
Remo Suppi

2017 ◽  
Vol 898 ◽  
pp. 2076-2080 ◽  
Author(s):  
Xing Qi Huang ◽  
Xiao Rong Li ◽  
Da Wei Zhang ◽  
Chang Jun Xue ◽  
Ai Qin Zhang

Compared with the traditional water reducer, polycarboxylicwater-reducing agent exhibits the advantages of high water-reducing rate, cement paste fluidity and low slump loss, etc. The structure of polycarboxylates water reducing agent molecular is comb type. Water reducing agent can be used in the molecular design because it has high water reducing rate, low dosage, good slump stability, and have great potential in increase strength. In recent years, it has attracted many researchers' attention. Water reducing agent can block or destroy cement granular flocculation structure, through the surface function, complexation, electrostatic repulsion force and stereo repulsive force. Research on water reducing agent based on the application of poly carboxylic acid can realize functional design of water reducing agent, so as to promote the development of high-performance concrete.


2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Joseph R. Wasniewski ◽  
David H. Altman ◽  
Stephen L. Hodson ◽  
Timothy S. Fisher ◽  
Anuradha Bulusu ◽  
...  

The next generation of thermal interface materials (TIMs) are currently being developed to meet the increasing demands of high-powered semiconductor devices. In particular, a variety of nanostructured materials, such as carbon nanotubes (CNTs), are interesting due to their ability to provide low resistance heat transport from device-to-spreader and compliance between materials with dissimilar coefficients of thermal expansion (CTEs), but few application-ready configurations have been produced and tested. Recently, we have undertaken major efforts to develop functional nanothermal interface materials (nTIMs) based on short, vertically aligned CNTs grown on both sides of a thin interposer foil and interfaced with substrate materials via metallic bonding. A high-precision 1D steady-state test facility has been utilized to measure the performance of nTIM samples, and more importantly, to correlate performance to the controllable parameters. In this paper, we describe our material structures and the myriad permutations of parameters that have been investigated in their design. We report these nTIM thermal performance results, which include a best to-date thermal interface resistance measurement of 3.5 mm2 K/W, independent of applied pressure. This value is significantly better than a variety of commercially available, high-performance thermal pads and greases we tested, and compares favorably with the best results reported for CNT-based materials in an application-representative setting.


2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.


2018 ◽  
Vol 35 (13) ◽  
pp. 2338-2339 ◽  
Author(s):  
Hongyang Li ◽  
Shuai Hu ◽  
Nouri Neamati ◽  
Yuanfang Guan

Abstract Motivation Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs. Results Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance. Availability and implementation TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python. Supplementary information Supplementary data are available at Bioinformatics online.


Author(s):  
Igor Chernykh ◽  
Vitaly Vshivkov ◽  
Galina Dudnikova ◽  
Tatyana Liseykina ◽  
Ekaterina Genrikh ◽  
...  

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