Agent-based modeling is an approach that can be used to develop hypotheses and make predictions about a biological system. Like many of the tools of systems biology, it benefits from a multiscale approach. However, this creates an extremely dense dataset that can be unwieldy and difficult to integrate into a functional workflow.
Thomas Deisboeck, M.D., associate professor, radiology, Massachusetts General Hospital, Harvard Medical School, will present his work on a hybrid discrete and continuum agent-based cancer model. It simulates each cancer cell, equipped with cell-signaling pathways, and the cells interact on three-dimensional lattices that resemble microenvironments in tissue. This cross-scale technique allows validation of biomarkers and discovery of novel molecular targets.
Dr. Deisboeck has been able to observe in silico patterns that emerge in multicellular populations over time, in brain and lung cancer, which can then be compared to histology samples and patient imaging data. Using a patient’s MRI as a starting point, Dr. Deisboeck simulates tumor growth, and has begun to predict patient-specific cancer progression. “We were able to predict, in a patient case study, tumor recurrence earlier than it was visible on MRI,” says Dr. Deisboeck, referring to a retrospective case study of an individual with a brain tumor.
Normally, the resolution limits of conventional imaging technology would not allow physicians to study the cancer on a single-cell level. Dr. Deisboeck’s agent-based modeling uses simulation techniques to push the resolution beyond the natural limits of the instrument, providing a personalized computational model of an individual cancer. Another limitation exists on the computational side of the experiment, where extremely dense datasets strain the capacity of the systems.
While still at a nascent stage, Dr. Deisboeck notes that the use of multiscale, multiresolution modeling will allow the scientists to simulate selectively at various levels of granularity, choosing higher resolution for areas of interest such as the margins of the tumor where growth and invasion are more likely to occur and lower resolution for areas that are less likely to change quickly such as the center of the tumor. This makes the dataset more manageable and less computationally costly in an effort to simulate progression across multiple scales up to clinically relevant tumor sizes.