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Integrating Approximate Bayesian Computation with Complex Agent-Based Models for Cancer Research

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Andrea Sottoriva

    (University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Department of Oncology)

  • Simon Tavaré

    (University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Department of Oncology and DAMTP)

Abstract

Multi-scale agent-based models such as hybrid cellular automata and cellular Potts models are now being used to study mechanisms involved in cancer formation and progression, including cell proliferation, differentiation, migration, invasion and cell signaling. Due to their complexity, statistical inference for such models is a challenge. Here we show how approximate Bayesian computation can be exploited to provide a useful tool for inferring posterior distributions. We illustrate our approach in the context of a cellular Potts model for a human colon crypt, and show how molecular markers can be used to infer aspects of stem cell dynamics in the crypt.

Suggested Citation

  • Andrea Sottoriva & Simon Tavaré, 2010. "Integrating Approximate Bayesian Computation with Complex Agent-Based Models for Cancer Research," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 57-66, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_5
    DOI: 10.1007/978-3-7908-2604-3_5
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