scholarly journals Colorectal tumour simulation using agent based modelling and high performance computing

2017 ◽  
Vol 67 ◽  
pp. 397-408 ◽  
Author(s):  
Guiyeom Kang ◽  
Claudio Márquez ◽  
Ana Barat ◽  
Annette T. Byrne ◽  
Jochen H.M. Prehn ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Colorectal Tumour ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Performance Computing

scholarly journals Agent-based Robust Grid Scheduling Framework for High Performance Computing

2012 ◽  
Vol 1 ◽  
pp. 554-560 ◽  
Author(s):  
Syed Nasir Mehmood Shah ◽  
Nazleeni Haron ◽  
M Nordin B. Zakaria ◽  
Ahmad Kamil Bin Mahmood
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Grid Scheduling ◽  
Agent Based ◽  
Performance Computing

scholarly journals High performance computing for three-dimensional agent-based molecular models

2016 ◽  
Vol 68 ◽  
pp. 68-77 ◽  
Author(s):  
G. Pérez-Rodríguez ◽  
M. Pérez-Pérez ◽  
F. Fdez-Riverola ◽  
A. Lourenço
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Three Dimensional ◽  
Molecular Models ◽  
Agent Based ◽  
Performance Computing

scholarly journals Learning-accelerated Discovery of Immune-Tumour Interactions

2019 ◽  
Author(s):  
Jonathan Ozik ◽  
Nicholson Collier ◽  
Randy Heiland ◽  
Gary An ◽  
Paul Macklin
Keyword(s):  
Open Source ◽  
High Performance Computing ◽  
High Performance ◽  
Spatial Dynamics ◽  
Simulation Models ◽  
Scale Model ◽  
Agent Based ◽  
Extreme Scale ◽  
Cancer Immunotherapies ◽  
Performance Computing

We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour-immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.

scholarly journals Model refinement through high-performance computing: an agent-based HIV example

2010 ◽  
Vol 6 (Suppl 1) ◽  
pp. S3 ◽  
Author(s):  
Dimitri Perrin ◽  
Heather J Ruskin ◽  
Martin Crane
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Model Refinement ◽  
Agent Based ◽  
Performance Computing

scholarly journals Sepsis Reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model

2017 ◽  
Author(s):  
Chase Cockrell ◽  
Gary An
Keyword(s):  
Boundary Conditions ◽  
High Performance Computing ◽  
High Performance ◽  
System Behavior ◽  
Agent Based Model ◽  
Physical Sciences ◽  
Clinical Sepsis ◽  
Agent Based ◽  
Data Sparseness ◽  
Performance Computing

AbstractObjectives: Sepsis affects nearly 1 million people in the United States per year, has a mortality rate of 28–50m% and requires more than $20 billion a year in hospital costs. Over a quarter century of research has not yielded a single reliable diagnostic test or a directed therapeutic agent for sepsis. Central to this insufficiency is the fact that sepsis remains a clinical/physiological diagnosis representing a multitude of molecularly heterogeneous pathological trajectories. Advances in computational capabilities offered by High Performance Computing (HPC) platforms call for an evolution in the investigation of sepsis to attempt to define the boundaries of traditional research (bench, clinical and computational) through the use of computational proxy models. We present a novel investigatory and analytical approach, derived from how HPC resources and simulation are used in the physical sciences, to identify the epistemic boundary conditions of the study of clinical sepsis via the use of a proxy agent-based model of systemic inflammation. Design: Current predictive models for sepsis use correlative methods are limited by patient heterogeneity and data sparseness. We address this issue by using an HPC version of a system-level validated agent-based model of sepsis, the Innate Immune Response ABM (IIRBM), as a proxy system in order to identify boundary conditions for the possible behavioral space for sepsis. We then apply advanced analysis derived from the study of Random Dynamical Systems (RDS) to identify novel means for characterizing system behavior and providing insight into the tractability of traditional investigatory methods. Results: The behavior space of the IIRABM was examined by simulating over 70 million sepsis patients for up to 90 days for the following parameters: cardio-respiratory-metabolic resilience; microbial invasiveness; microbial toxigenesis; and degree of nosocomial exposure. In addition to using established methods for describing parameter space, we developed two novel methods for characterizing the behavior of a RDS: Probabilistic Basins of Attraction (PBoA) and Stochastic Trajectory Analysis (STA). Computationally generated behavioral landscapes demonstrated attractor structures around stochastic regions of behavior that could be described in a complementary fashion through use of PBoA and STA. The stochasticity of the boundaries of the attractors highlights the challenge for correlative attempts to characterize and classify clinical sepsis. Conclusions: HPC simulations of models like the IIRABM can be used to generate approximations of the behavior space of sepsis to both establish “boundaries of futility” with respect to existing investigatory approaches and apply system engineering principles to investigate the general dynamic properties of sepsis to provide a pathway for developing control strategies. The issues that bedevil the study and treatment of sepsis, namely clinical data sparseness and inadequate experimental sampling of system behavior space, are fundamental to nearly all biomedical research, manifesting in the “Crisis of Reproducibility” at all levels. HPC-augmented simulation-based research offers an investigatory strategy more consistent with that seen in the physical sciences (which combine experiment, theory and simulation), and an opportunity to utilize the leading advances in HPC, namely deep machine learning and evolutionary computing, to form the basis of an iterative scientific process to meet the full promise of Precision Medicine (right drug, right patient, right time).

Agent-Based Modeling and High Performance Computing

2016 ◽  
pp. 79-111 ◽  
Author(s):  
Maksudul Alam ◽  
Vida Abedi ◽  
Josep Bassaganya-Riera ◽  
Katherine Wendelsdorf ◽  
Keith Bisset ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Performance Computing
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