Relating the Chondrocyte Gene Network to Growth Plate Morphology: From Genes to Phenotype

Abstract

During endochondral ossification, chondrocyte growth and differentiation is controlled by many local signalling pathways. Due to crosstalks and feedback mechanisms, these interwoven pathways display a network like structure. In this study, a large-scale literature based logical model of the growth plate network was developed. The network is able to capture the different states (resting, proliferating and hypertrophic) that chondrocytes go through as they progress within the growth plate. In a first corroboration step, the effect of mutations in various signalling pathways of the growth plate network was investigated.

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

“…Both network‐based and molecular approaches provide useful insights into the pathophysiology of OA, but have not been used as part of a systems‐biology continuum. Kerkhofs et al40 developed a mathematical model to examine the switch from resting/proliferating chondrocytes to hypertrophy 41, 42. The systems approach included a form of Markov chain model to predict the probability of particular factors pushing a chondrocyte toward a proliferative ( Sox9 ) or hypertrophic ( Runx2 ) phenotype.…”

Regenerative orthopaedics: in vitro, in vivo … in silico

“…Gene/protein: gene regulatory network for chondrogenic differentiation [27]. Cell: cell expansion influenced by the mechanical properties of the microbead (color scale indicates mechanical state of cell) [28].…”

“…An example of such a simple network model is a Boolean model, representing variables as either 'on' (active, 1) or 'off' (inactive, 0), allowing to add a dynamical component to the models without increasing the number of parameters. Boolean network models can be used to investigate the robustness and attractor basins of the networks, and to identify missing links (additional variables that need to be added to the network in order to reproduce experimentally observed behaviour) [27,33]. What is missing in these simple network models are the complex biochemical mechanisms that are underlying the activation or inhibition as well as true spatiotemporal and quantitative information.…”

“…The models vary in length scale and information content (cfr previous section), depending on the particular research/clinical question at hand. [27]. Carrier: prediction of calcium dissolution from calcium phosphate based carrier (bottom, unpublished results, courtesy of V. Manhas) and prediction of optimal cell-carrier combinations for various initial cell densities (cm0) [38].…”

“…Mechanistic models using knowledge of specific relevant pathways allow to investigate the dynamics of the cell state when specific growth factors are used during cell culture [42,43]. Yet other models investigate the regulatory networks and the basins of attraction of specific cell states, providing insight in precise culture conditions that push or keep cells in the desired state [27,44]. An example of the latter is the work by Kerkhofs et al [27] who created a large scale literature-based Boolean model of the osteochondral regulatory network (figure 2, bottom right).…”