On the personalised modelling of cancer signalling

Files in This Item:
 File SizeFormat
DownloadFey_2016_(published_version)_IFAC_on_personalised_modelling.pdf414.55 kBAdobe PDF
Title: On the personalised modelling of cancer signalling
Authors: Fey, DirkKuehn, AxelKholodenko, Boris N.
Permanent link: http://hdl.handle.net/10197/8317
Date: 12-Oct-2016
Online since: 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.
Funding Details: 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)
Keywords: Systems biologyOrdinary differential equationsParameter estimation
DOI: 10.1016/j.ifacol.2016.12.145
Language: en
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: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:SBI Research Collection
Medicine Research Collection

Show full item record

SCOPUSTM   
Citations 50

2
Last Week
0
Last month
checked on Sep 11, 2020

Page view(s) 50

1,486
Last Week
4
Last month
15
checked on Aug 14, 2022

Download(s)

241
checked on Aug 14, 2022

Google ScholarTM

Check

Altmetric


If you are a publisher or author and have copyright concerns for any item, please email research.repository@ucd.ie and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.