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Model checking cancer automata

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BHI34JBASv2.pdf (272.6Kb)
Date
18/04/2016
Author
Bowles, Juliana Kuster Filipe
Silvina, Agastya
Keywords
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
QA75 Electronic computers. Computer science
NDAS
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Abstract
Cancer is a chronic disease where cells grow and multiply in an uncontrollable manner ultimately spreading and invading surrounding tissue, and metastasising in other parts or organs of the body. Automata can be used to capture cancer evolving through a (discrete finite) sequence of progressive stages called phenotypes. Automata consist of states (known as hallmarks of cancer) and transitions between states, indicating a progression or regression of the cancer. We explore extensions and combinations of different variants of timed automata and associated tools to model and analyse a model of the disease in different ways. We combine patient information and comorbidities with the cancer automaton through composition. The goal of this work is to use model checking as an analysis technique to provide further insights into the effectiveness of treatment plans for a given patient, and how these could potentially inhibit or slow down the progression of cancer.
Citation
Bowles , J K F & Silvina , A 2016 , Model checking cancer automata . in IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 . , 7455913 , Institute of Electrical and Electronics Engineers Inc. , pp. 376-379 , 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 , Las Vegas , United States , 24/02/16 . https://doi.org/10.1109/BHI.2016.7455913
 
conference
 
Publication
IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
DOI
https://doi.org/10.1109/BHI.2016.7455913
Type
Conference item
Rights
© 2016, Publisher / the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at ieeexplore.ieee.org /
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/8290

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