Lesage Raphaelle, Kerkhofs Johan, Geris Liesbet
Prometheus, Division of Skeletal Tissue Engineering Leuven, KU Leuven, Leuven, Belgium.
Biomechanics Section, KU Leuven, Leuven, Belgium.
Front Bioeng Biotechnol. 2018 Nov 13;6:165. doi: 10.3389/fbioe.2018.00165. eCollection 2018.
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.
软骨细胞的特化，即软骨形成分化，是一个复杂且受到精细调控的过程，在骨骼形成和软骨再生中都起着至关重要的作用。了解这一过程的分子调控机制可能有助于识别关键调控因子，这些因子可作为潜在的治疗靶点，或者可能改善高质量且稳健的骨骼组织工程方法的发展。然而，参与这一过程的每个基因都受到无数反馈机制的影响，这些反馈机制将其表达维持在理想范围内，这使得预测如果这些基因中的一个出现缺陷或受到药物作用会发生什么变得具有挑战性。计算机建模提供了一种从网络角度模拟这种复杂相互作用的工具。本文旨在概述当前用于分析骨骼组织工程背景下，特别是骨软骨分化中的细胞分化的方法。在网络建模中，网络既可以从文献中报道的机制和途径推导而来（基于知识的方法），也可以直接从数据中推断得出（数据驱动的方法）。组合方法可进一步优化网络。一旦建立了网络，就有几种建模技术可用于动态解释网络图中提出的关系（某些途径的激活或抑制对系统随时间演变的影响），并模拟已建立网络的可能结果，如给定的细胞状态。本综述针对上述每个步骤（构建、优化和建模网络）提供了简要的理论视角，随后简要概述了已发表的作品，仅关注与细胞命运决定、软骨分化和生长板生物学相关的应用。特别关注了一个内部开发的生长板软骨细胞分化基因调控网络建模示例，因为它可以说明上述所有步骤。总之，本文讨论并探索了一系列工具，这些工具是在再生医学背景下对骨软骨分化进行严格的系统级建模的第一步。
