[en] Differentiation of chondrocytes towards hypertrophy is a natural process whose control is essential in endochondral bone formation. It is additionally thought to play a role in several pathophysiological processes, with osteoarthritis being a prominent example. We perform a dynamic analysis of a qualitative mathematical model of the regulatory network that directs this phenotypic switch to investigate the influence of the individual factors holistically. To estimate the stability of a SOX9 positive state (associated with resting/proliferation chondrocytes) versus a RUNX2 positive one (associated with hypertrophy) we employ two measures. The robustness of the state in canalisation (size of the attractor basin) is assessed by a Monte Carlo analysis and the sensitivity to perturbations is assessed by a perturbational analysis of the attractor. Through qualitative predictions, these measures allow for an in silico screening of the effect of the modelled factors on chondrocyte maintenance and hypertrophy. We show how discrepancies between experimental data and the model's results can be resolved by evaluating the dynamic plausibility of alternative network topologies. The findings are further supported by a literature study of proposed therapeutic targets in the case of osteoarthritis.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Kerkhofs, Johan
Leijten, Jeroen
Bolander, Johanna
Luyten, Frank P.
Post, Janine N.
Geris, Liesbet ; Université de Liège > Département d'aérospatiale et mécanique > Génie biomécanique
Language :
English
Title :
A Qualitative Model of the Differentiation Network in Chondrocyte Maturation: A Holistic View of Chondrocyte Hypertrophy.
Publication date :
2016
Journal title :
PLoS ONE
eISSN :
1932-6203
Publisher :
Public Library of Science, United States - California
Volume :
11
Issue :
8
Pages :
e0162052
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
FP7 - 279100 - BRIDGE - Biomimetic process design for tissue regeneration: from bench to bedside via in silico modelling
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