A Qualitative Model of the Differentiation Network in Chondrocyte Maturation: A Holistic View of Chondrocyte Hypertrophy

Abstract

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.

Use of Computational Modeling to Study Joint Degeneration: A Review

“…Since these regulatory network graphs are sometimes constructed on the basis of experimental results published in literature (knowledge-based models), conflicting opinions in literature can lead to problems in decision making from the perspective of the model developer. Additionally, most of these (G) RN models are either qualitative or semi-quantitative in nature (Kerkhofs and Geris, 2015;Kerkhofs et al, 2016), which makes it difficult to be integrated with quantitative methods like the FEM. Also, the ability for quantitative model predictions is limited.…”

The Third Era of Tissue Engineering: Reversing the Innovation Drivers

“…process steps) that exchange information with their physical counterpart through sensors or historic data and that can be used to unable understanding and optimization of the physical process they represent. Intracellular gene regulatory models [32,33] are capable of reducing the in vitro large-scale screening to optimize culture medium composition. A digital twin of the bioprinting process [34] or the This paper has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction.…”

In silico methods – Computational alternatives to animal testing

“…Liesbet Geris (University of Liège, KU Leuven, Belgium) showed translational applications of in silico methods when combined with different aspects of tissue engineering. She presented the modelling of differentiation processes of mesenchymal stem cells or chondrocytes based on gene regulation by using Boolean models (Kerkhofs and Geris, 2015;Kerkhofs et al, 2016) and explained that prediction models can support the development or validation of new carriers for bone formation (Kerckhofs et al, 2016) or support the design and optimization of cell culture bioreactors Papantoniou et al, 2014). The ongoing work and importance of the Virtual Physiological Human Initiative 1 towards individualized physiology-based computer simulations that aims to revolutionize human medicine was also presented.…”