Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25448
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Authors: Scott, Erin
Nicol, James
Coulter, Jonathan
Hoyle, Andrew
Shankland, Carron
Contact Email: ces@cs.stir.ac.uk
Title: Process Algebra with Layers: Multi-scale Integration Modelling applied to Cancer Therapy
Editors: Bracciali, A
Caravagna, G
Gilbert, D
Tagliaferri, R
Citation: Scott E, Nicol J, Coulter J, Hoyle A & Shankland C (2017) Process Algebra with Layers: Multi-scale Integration Modelling applied to Cancer Therapy In: Bracciali A, Caravagna G, Gilbert D, Tagliaferri R (ed.) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016, Cham, Switzerland: Springer. CIBB2016: 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics, 1.9.2016 - 3.9.2016, Stirling, UK, pp. 118-133.
Issue Date: 17-Oct-2017
Series/Report no.: Lecture Notes in Computer Science, 10477
Conference Name: CIBB2016: 13th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics
Conference Dates: 2016-09-01T00:00:00Z
Conference Location: Stirling, UK
Abstract: We present a novel Process Algebra designed for multi-scale integration modelling: Process Algebra with Layers (PAL). The unique feature of PAL is the modularisation of scale into integrated layers: Object and Population. An Object can represent a molecule, organelle, cell, tissue, organ or any organism. Populations hold specific types of Object, for example, life stages, cell phases and infectious states. The syntax and semantics of this novel language are presented. A PAL model of the multi-scale system of cell growth and damage from cancer treatment is given. This model allows the analysis of different scales of the system. The Object and Population levels give insight into the length of a cell cycle and cell population growth respectively. The PAL model results are compared to wet laboratory survival fractions of cells given different doses of radiation treatment [1]. This comparison shows how PAL can be used to aid in investigations of cancer treatment in systems biology.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: Publisher policy allows this work to be made available in this repository. Published In: Bracciali A., Caravagna G., Gilbert D., Tagliaferri R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science, vol 10477. Springer, Cham. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-67834-4_10
URL: https://link.springer.com/chapter/10.1007/978-3-319-67834-4_10

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