Studying cancer-cell populations by programmable models of networks

We draw the basic lines for an approach to build mathematical and programmable network models, to be applied in the study of populations of cancer-cells at different stages of disease development. The methodology we propose uses a stochastic Concurrent Constraint Programming language, a flexible sto...

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Bibliographic Details
Published in:Network modeling and analysis in health informatics and bioinformatics (Wien) Vol. 1; no. 3; pp. 117 - 133
Main Authors: Bortolussi, Luca, Policriti, Alberto
Format: Journal Article
Language:English
Published: Vienna Springer Vienna 01.09.2012
Springer Nature B.V
Subjects:
Ablation
Androgens
Applications of Graph Theory and Complex Networks
Bioinformatics
Cell culture
Computational Biology/Bioinformatics
Computer Science
Health Informatics
Mathematical analysis
Mathematical models
Ordinary differential equations
Original Article
Population studies
Populations
Programming languages
Prostate cancer
Semantics
Stochastic models
Variables
Computational systems biology
Prostate tumor
Stochastic process algebras
Tumor growth modeling
ISSN:2192-6662, 2192-6670
Online Access:Get full text
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Summary:We draw the basic lines for an approach to build mathematical and programmable network models, to be applied in the study of populations of cancer-cells at different stages of disease development. The methodology we propose uses a stochastic Concurrent Constraint Programming language, a flexible stochastic modelling language employed to code networks of agents. It is applied to (and partially motivated by) the study of differently characterized populations of prostate cancer cells. In particular, we prove how our method is suitable to systematically reconstruct and compare different mathematical models of prostate cancer growth—together with interactions with different kinds of hormone therapy—at different levels of refinement. Moreover, we show our technique at work in analysing the nature of noise and in the possible presence of competing mechanisms in the models proposed.
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ISSN:2192-6662
2192-6670
DOI:10.1007/s13721-012-0010-x