High performance model exploration of mutation patterns in an agent-based model of colorectal cancer
Year: 2018
Type: article
Abstract: Agent-based models (ABMs) integrate multiple scales of behavior and data to produce higher-order dynamic phenomena and are increasingly used in the study of cancer.However, the complexity of ABMs provides numerous challenges to their effective use, mostly related to the relatively high computational cost in carrying out the simulation experiments by which ABMs are developed, calibrated and used.Highperformance computing (HPC) platforms can address some of these computational constraints.We have developed a framework, called Extreme-scale Model Exploration with Swift/T (EMEWS), that can leverage the computing capabilities of HPC parallel architectures by integrating model exploration (ME) modules such as machine learning and evolutionary computing methods to augment the performance of large-scale simulation experiments.EMEWS can be used to aid in the calibration, parameter estimation and model exploration of any simulation-model.Herein we provide a use case examining the factors and patterns of mutational events of oncogenesis in population level simulations of a mechanism-based ABM of colorectal cancer (CRC).
Institution: Argonne National Laboratory
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4294255490