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

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          Abstract

          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.

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          Modelling and analysis of gene regulatory networks.

          Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction. By understanding the dynamics of these networks we can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated. Accurate prediction of the behaviour of regulatory networks will also speed up biotechnological projects, as such predictions are quicker and cheaper than lab experiments. Computational methods, both for supporting the development of network models and for the analysis of their functionality, have already proved to be a valuable research tool.
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            Hypertrophic chondrocytes can become osteoblasts and osteocytes in endochondral bone formation.

            According to current dogma, chondrocytes and osteoblasts are considered independent lineages derived from a common osteochondroprogenitor. In endochondral bone formation, chondrocytes undergo a series of differentiation steps to form the growth plate, and it generally is accepted that death is the ultimate fate of terminally differentiated hypertrophic chondrocytes (HCs). Osteoblasts, accompanying vascular invasion, lay down endochondral bone to replace cartilage. However, whether an HC can become an osteoblast and contribute to the full osteogenic lineage has been the subject of a century-long debate. Here we use a cell-specific tamoxifen-inducible genetic recombination approach to track the fate of murine HCs and show that they can survive the cartilage-to-bone transition and become osteogenic cells in fetal and postnatal endochondral bones and persist into adulthood. This discovery of a chondrocyte-to-osteoblast lineage continuum revises concepts of the ontogeny of osteoblasts, with implications for the control of bone homeostasis and the interpretation of the underlying pathological bases of bone disorders.
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              Fuzzy logic = computing with words

              L.A. Zadeh (1996)
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                Author and article information

                Contributors
                Journal
                Front Bioeng Biotechnol
                Front Bioeng Biotechnol
                Front. Bioeng. Biotechnol.
                Frontiers in Bioengineering and Biotechnology
                Frontiers Media S.A.
                2296-4185
                13 November 2018
                2018
                : 6
                : 165
                Affiliations
                [1] 1Prometheus, Division of Skeletal Tissue Engineering Leuven, KU Leuven , Leuven, Belgium
                [2] 2Biomechanics Section, KU Leuven , Leuven, Belgium
                [3] 3Biomechanics Research Unit, GIGA in silico Medicine, University of Liège , Liège, Belgium
                Author notes

                Edited by: Eric Farrell, Erasmus University Rotterdam, Netherlands

                Reviewed by: Janine Nicole Post, University of Twente, Netherlands; Andrew Anthony Pitsillides, Royal Veterinary College (RVC), United Kingdom

                *Correspondence: Liesbet Geris liesbet.geris@ 123456uliege.be

                †Present Address: Johan Kerkhofs, Red Cross Flanders, Mechelen, Belgium

                This article was submitted to Tissue Engineering and Regenerative Medicine, a section of the journal Frontiers in Bioengineering and Biotechnology

                Article
                10.3389/fbioe.2018.00165
                6243751
                29404323
                4debb157-2fda-41d3-b970-cb2f59fca799
                Copyright © 2018 Lesage, Kerkhofs and Geris.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 March 2018
                : 22 October 2018
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 90, Pages: 16, Words: 13508
                Funding
                Funded by: Fonds Wetenschappelijk Onderzoek 10.13039/501100003130
                Funded by: FP7 Ideas: European Research Council 10.13039/100011199
                Award ID: 279100
                Funded by: H2020 Marie Skłodowska-Curie Actions 10.13039/100010665
                Award ID: 721432
                Categories
                Bioengineering and Biotechnology
                Review

                in silico modeling,gene regulatory network,network inference,chondrocyte,differentiation,regenerative medicine

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