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.
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Models for Survival Data from Cancer Prevention Studies
We propose a new parametric survival model for cancer prevention studies. The formulation of the model is in the spirit of stochastic modelling of the occurrences of tumours through two stages: initiation of an undetected tumour and promotion of the tumour to a detectable cancer. Several novel properties of the model proposed are derived. In addition, we examine the relationship of our model with the existing lagged regression model of Zucker and Lakatos. Also, we bridge the difference between two distinct stochastic modelling methods for cancer data, one used primarily for cancer therapeutic trials and the other used for cancer prevention trials.
more » « less- NSF-PAR ID:
- 10404447
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Volume:
- 64
- Issue:
- 3
- ISSN:
- 1369-7412
- Page Range / eLocation ID:
- p. 467-477
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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