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On the personalised modelling of cancer signalling
Author(s)
Date Issued
2016-10-12
Date Available
2017-02-03T17:27:29Z
Abstract
Dynamic modelling has long been used to understand fundamental principles of cell signalling and its dysregulation in cancer. More recently these models have also been used to understand the individual risks of cancer patients, and predict their survival probabilities. However, the current methodologies for integrating tumour data and generating patient-specific simulations suffer from the lack of general applicability; they only work for cell signalling models in which only posttranslational protein modifications are considered, so that the total protein concentrations are conserved. Here, we present novel, generally applicable method. The method is based on a simple theoretical framework for modelling gene-regulation, and the indirect estimation of patient-specific parameters from tumour data. Because our method does not require time-invariance of the total-protein concentrations, it can be applied to models of any nature, including the many cancer signalling models involving gene-regulation.
Sponsorship
European Commission - Seventh Framework Programme (FP7)
Type of Material
Journal Article
Publisher
Elsevier
Journal
IFAC-PapersOnLine
Volume
49
Issue
26
Start Page
312
End Page
317
Copyright (Published Version)
2016 IFAC (International Federation of Automatic Control)
Language
English
Status of Item
Peer reviewed
Conference Details
6th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE 2016), Magdeburg, Germany, 9-12 October 2016
This item is made available under a Creative Commons License
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Fey_2016_(published_version)_IFAC_on_personalised_modelling.pdf
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