Abstract
Fast and accurate computational biology models offer the prospect of accelerating the development of personalized medicine. A tool capable of estimating treatment success can help prevent unnecessary and costly treatments and potential harmful side effects. A novel high-performance Agent-Based Model (ABM) was adopted to simulate and visualize multi-scale complex biological processes arising in vocal fold inflammation and repair. The computational scheme was designed to organize the 3D ABM sub-tasks to fully utilize the resources available on current heterogeneous platforms consisting of multi-core CPUs and many-core GPUs. Subtasks are further parallelized and convolution-based diffusion is used to enhance the performance of the ABM simulation. The scheme was implemented using a client-server protocol allowing the results of each iteration to be analyzed and visualized on the server (i.e., in-situ) while the simulation is running on the same server. The resulting simulation and visualization software enables users to interact with and steer the course of the simulation in real-time as needed. This high-resolution 3D ABM framework was used for a case study of surgical vocal fold injury and repair. The new framework is capable of completing the simulation, visualization and remote result delivery in under 7 s per iteration, where each iteration of the simulation represents 30 min in the real world. The case study model was simulated at the physiological scale of a human vocal fold. This simulation tracks 17 million biological cells as well as a total of 1.7 billion signaling chemical and structural protein data points. The visualization component processes and renders all simulated biological cells and 154 million signaling chemical data points. The proposed high-performance 3D ABM was verified through comparisons with empirical vocal fold data. Representative trends of biomarker predictions in surgically injured vocal folds were observed.
Highlights
This section starts by examining the overall performance of the Agent-Based Modeling (ABM) simulation for our case study of the 3D Vocal Fold Repair With ABM (VF-ABM), thereby illustrating the scalability of the model with respect to the number of cores available
Section 3.1.3 analyzes the performance of the 3D VF-ABM simulation suite and benchmarks its performance against existing ABM frameworks
This work presents novel 3D ABM implementation techniques to tackle the heterogeneity of time scales in large-scale and multi-scale computational biology modeling
Summary
Agent-based modeling is a widely used approach to quantitatively simulate dynamical systems (Macal, 2016). The popularity of ABMs can be observed in the variety of ABM frameworks developed in the past decade (for reviews, please see An et al, 2009; Gorochowski, 2016; Hellweger et al, 2016; Macal, 2016). Each ABM is defined by a set of autonomous agents whose interactions among themselves and with their environment are governed by a number of stochastic or deterministic rules (Hellweger et al, 2016; Macal, 2016). In contrast to equation-based approaches, ABMs are decentralized. A universal definition of ABMs remains debatable (Macal, 2016), fundamental components of ABM typically include: agent set, agent relationship set, and agents’ environment (Macal and North, 2010)
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