Computational Modeling and Reverse Engineering to Reveal Dominant Regulatory Interactions Controlling Osteochondral Differentiation: Potential for Regenerative Medicine

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. Understanding the molecular regulation of this process might help to identify key regulatory factors that can serve as potential therapeutic targets, or that might improve the development of qualitative and robust skeletal tissue engineering approaches. However, each gene involved in this process is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, making the prediction of what will happen if one of these genes defaults or is targeted with drugs, challenging. Computer modeling provides a tool to simulate this intricate interplay from a network perspective. This paper aims to give an overview of the current methodologies employed to analyze cell differentiation in the context of skeletal tissue engineering in general and osteochondral differentiation in particular. In network modeling, a network can either be derived from mechanisms and pathways that have been reported in the literature (knowledge-based approach) or it can be inferred directly from the data (data-driven approach). Combinatory approaches allow further optimization of the network. Once a network is established, several modeling technologies are available to interpret dynamically the relationships that have been put forward in the network graph (implication of the activation or inhibition of certain pathways on the evolution of the system over time) and to simulate the possible outcomes of the established network such as a given cell state. This review provides for each of the aforementioned steps (building, optimizing, and modeling the network) a brief theoretical perspective, followed by a concise overview of published works, focusing solely on applications related to cell fate decisions, cartilage differentiation and growth plate biology. Particular attention is paid to an in-house developed example of gene regulatory network modeling of growth plate chondrocyte differentiation as all the aforementioned steps can be illustrated. In summary, this paper discusses and explores a series of tools that form a first step toward a rigorous and systems-level modeling of osteochondral differentiation in the context of regenerative medicine

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

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

A computational Model to Assess the Contribution of Growth Factors to Phenotype Stability in Chondrocytes

Cell-based tissue engineering constructs are an interesting expansion of the surgeon’s toolkit in treating long bone defects. However, the outcome of interventions with these constructs suffers from high variability barring their regular appearance in the clinic, in no small part due to the inter-patient variability in cell behaviour. In the paradigm of ‘developmental engineering’ a solution to this problem is envisioned by mimicking robust developmental processes in combination with a rigorous analysis thereof through the construction of computational models. From our knowledge of developmental biology we can form a computational model to facilitate understanding of how growth factors and transcription factors influence cell fate decisions in the growth plate and consequently answer the question whether – and how – they can boost bone healing. The model presented in this study includes 46 factors and 146 interactions between them. The dynamics of the system were simulated in a simplified manner that differentiates between slow and fast interactions. Through a Monte Carlo approach the importance of each factor in the stability of chondrocytic phenotypes (proliferating, hypertrophic) is assessed. The hypertrophic state was found to be more stable than that of the proliferating chondrocyte. This higher stability in random initial conditions seems to be conferred by faster reactions that favor the hypertrophic phenotype. Overall, the model allows the importance of several important factors in the fate decision of chondrocytes to be quantitatively assessed and can make suggestions as to how an in vitro bone forming process could be steered.Bridg

The search for a core functionality network in chondrocyte differentiation using heuristic and genetic algorithms

Introduction: In the growth plate a continuing process where cartilage is replaced by bone provides the fuel for bone growth until its closure towards the end of puberty. At the cellular level the growth rate is maintained by proliferation and enlargement of maturing cells (hypertrophy). Mature cartilage cells (hypertrophic chondrocytes) secrete Ihh, a growth factor that induces expression of PTHrP, another growth factor, in immature proliferating chondrocytes. Since PTHrP in turn inhibits chondrocyte maturation, Ihh secretion limits the number of maturing chondrocytes through a negative feedback loop, striking a balance between proliferation and hypertrophy [Kronenberg, 2003]. Materials and methods: A gene network centering on the control of Ihh, PTHrP and transcription factors Sox9 and Runx2, which are the master regulators of early and late chondrocyte differentiation respectively, was manually constructed from literature. The dynamics of this network are simulated in a discrete framework that divides reactions into two speed classes. In this framework all interactions are considered additive, and each interaction is associated with a weight. Starting from the observation that the gene network must activate Runx2 in the presence of Ihh and Sox9 in the presence of Ihh and PTHrP, we investigate which edges are vital in achieving this. To this end, we employ both a heuristic and a genetic algorithm where the weights attached to the edges function as variables. In the heuristic algorithm weights are uniformly distributed in [0,1] and the means (based on 450 samples) of the weights that satisfy the above mentioned observations are contrasted with those that do not. If the difference of the means passes a certain threshold, the weight of the corresponding edge is fixed at 1. Results and discussion: Preliminary results from the heuristic algorithm show that fixing 14 weights (out of 147) is sufficient to match the biological observations in about 22% of cases, all other weights being selected randomly. The selected edges show that the BMP pathway is crucial in effecting a switch between hypertrophy in the absence of PTHrP and proliferation in its presence. This observation can be substantiated by earlier findings that BMP signalling plays a crucial role in prehypertrophic cells that are on the verge of hypertrophy [Yoon, 2006]. References: Kronenberg, 2003, Nature, 423:332-336; Yoon et al., 2006, Development, 133:4667-4678

: Développement des modèles numériques de la différentiation cartilagineuse dans la plaque de croissance

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. This PhD work centers on the development of computational models to study the molecular regulation of this process. First, we investigate how individual genes and their defects contribute to the overall change in functionality of the growth plate, where chondrogenic differentiation fuels bone growth. As each gene is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, predicting what will happen if one of these genes defaults is challenging. Therefore, we constructed a qualitative model, focusing on the process of bone formation, that simulates how the intricate interplay between the genes results in a functional growth plate morphology. This model allows the effect of gene knockouts or overexpression to be evaluated from a network perspective, and hence relates this genetic deficiency to the impairment of the gross bone formation on a tissue level. This knowledge can be of great assistance in the design and control of \textit{in vitro} bone tissue engineering processes. A framework with increased temporal and quantitative resolution is then used to study chondrocyte hypertrophy in an expanded network. Chondrocyte hypertrophy, a process in which cartilage cells enlarge and change their secretion profile to attract bone forming cells and blood vessels, is orchestrated on a molecular level by a switch between two ‘genetic programs’. In this switch, one set of transcription factors that represents chondrocyte proliferation competes with, and is ultimately replaced by, another set that represents hypertrophy. Since hypertrophy plays a vital role not only in the development the skeleton, but is also thought to be involved in several bone-related diseases, it has been studied extensively. We combine information of how individual factors that prevent or contribute to the hypertrophic switch interact in a computational model to develop a more global view of the regulatory network underlying hypertrophy. Through simulations of this regulatory network model we can perform an in silico screening for factors that greatly impact, positively or negatively, the decision to undergo hypertrophy. The results of this screening are checked for consistency using an ensemble approach. Specifically, a genetic algorithm is used to generate an ensemble of models, differing only in parameter values, whose qualitative dynamics match those observed in the growth plate. The range of behaviour exhibited by individual factors throughout this ensemble is mostly consistent. Additionally, a subset of the network topology is compared to that obtained by inference from growth plate expression profiles. Understanding how individual factors contribute to the hypertrophic switch in the context of the regulatory network has important repercussions in both cartilage and bone tissue engineering. Our approach further suggests several putative targets for intervention in disease processes where hypertrophy plays a role. In summary, this PhD offers and explores a series of tools that form a first step to a rigorous and systems-level understanding of chondrogenic differentiation.BRIDG

Chondrogenic Differentiation in the growth Plate: a Computational Modelling Approach

The specialization of cartilage cells, or chondrogenic differentiation, is an intricate and meticulously regulated process that plays a vital role in both bone formation and cartilage regeneration. This PhD work centers on the development of computational models to study the molecular regulation of this process. First, we investigate how individual genes and their defects contribute to the overall change in functionality of the growth plate, where chondrogenic differentiation fuels bone growth. As each gene is influenced by a myriad of feedback mechanisms that keep its expression in a desirable range, predicting what will happen if one of these genes defaults is challenging. Therefore, we constructed a qualitative model, focusing on the process of bone formation, that simulates how the intricate interplay between the genes results in a functional growth plate morphology. This model allows the effect of gene knockouts or overexpression to be evaluated from a network perspective, and hence relates this genetic deficiency to the impairment of the gross bone formation on a tissue level. This knowledge can be of great assistance in the design and control of \textit{in vitro} bone tissue engineering processes. A framework with increased temporal and quantitative resolution is then used to study chondrocyte hypertrophy in an expanded network. Chondrocyte hypertrophy, a process in which cartilage cells enlarge and change their secretion profile to attract bone forming cells and blood vessels, is orchestrated on a molecular level by a switch between two ‘genetic programs’. In this switch, one set of transcription factors that represents chondrocyte proliferation competes with, and is ultimately replaced by, another set that represents hypertrophy. Since hypertrophy plays a vital role not only in the development the skeleton, but is also thought to be involved in several bone-related diseases, it has been studied extensively. We combine information of how individual factors that prevent or contribute to the hypertrophic switch interact in a computational model to develop a more global view of the regulatory network underlying hypertrophy. Through simulations of this regulatory network model we can perform an in silico screening for factors that greatly impact, positively or negatively, the decision to undergo hypertrophy. The results of this screening are checked for consistency using an ensemble approach. Specifically, a genetic algorithm is used to generate an ensemble of models, differing only in parameter values, whose qualitative dynamics match those observed in the growth plate. The range of behaviour exhibited by individual factors throughout this ensemble is mostly consistent. Additionally, a subset of the network topology is compared to that obtained by inference from growth plate expression profiles. Understanding how individual factors contribute to the hypertrophic switch in the context of the regulatory network has important repercussions in both cartilage and bone tissue engineering. Our approach further suggests several putative targets for intervention in disease processes where hypertrophy plays a role. In summary, this PhD offers and explores a series of tools that form a first step to a rigorous and systems-level understanding of chondrogenic differentiation.nrpages: 298status: publishe