Computational modelling of cancer development and growth : modelling at multiple scales and multiscale modelling
MetadataShow full item record
Altmetrics Handle Statistics
Altmetrics DOI Statistics
In this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of a three-gene repressilator is given) and an actual gene regulatory network, the NF- κB pathway. The second model is a force-based individual-based model of the development of a solid avascular tumour with specific application to tumour cords, i.e. a mass of cancer cells growing around a central blood vessel. In each case, we compare our computational simulation results with experimental data. In the final discussion section, we outline how to take the work forward through the development of a multiscale model focussed at the cell level. This would incorporate key intracellular signalling pathways associated with cancer within each cell (e.g. p53–Mdm2, NF- κB) and through the use of high-performance computing be capable of simulating up to 109 cells, i.e. the tissue scale. In this way, mathematical models at multiple scales would be combined to formulate a multiscale computational model.
Szymanska , Z , Cytowski , M , Mitchell , E , Macnamara , C K & Chaplain , M A J 2018 , ' Computational modelling of cancer development and growth : modelling at multiple scales and multiscale modelling ' , Bulletin of Mathematical Biology , vol. 80 , no. 5 , pp. 1366-1403 . https://doi.org/10.1007/s11538-017-0292-3
Bulletin of Mathematical Biology
© 2017, Society for Mathematical Biology. This work has been 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 https://doi.org/10.1007/s11538-017-0292-3
DescriptionMAJC and CKM gratefully acknowledge support of EPSRC grant no. EP/N014642/1 (EPSRC Centre for Multiscale Soft Tissue Mechanics – With Application to Heart & Cancer).
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Showing items related by title, author, creator and subject.
Meedeniya, Dulani Apeksha (University of St Andrews, 2013-06-26) - ThesisModern software systems have increasingly higher expectations on their reliability, in particular if the systems are critical and real-time. The development of these complex software systems requires strong modelling and ...
Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling. Lynam, Christopher; King, Ruth; Thomas, Len; Buckland, Stephen T. (CREEM, University of St Andrews, 2007) - ReportA sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to ...
Hussain, Bilal Syed (University of St Andrews, 2017-06-21) - ThesisConstraint Programming (CP) is a powerful technique for solving a variety of combinatorial problems. Automated modelling using a refinement based approach abstracts over modelling decisions in CP by allowing users to specify ...