There is an urgent need in the field of soft tissue engineering (STE) to develop biomaterials exhibiting a high degree of biological and mechanical functionality as well as modularity so that they can be tailored according to patient-specific requirements. Recently, biomimetic soft network composites (SNC) consisting of a water-swollen hydrogel matrix and a reinforcing fibrous network fabricated by melt electrospinning writing technology have demonstrated exceptional mechanical and biological properties, thus becoming strong candidates for STE applications. However, there is a lack of design approaches to tailor and optimize their properties in a non-empirical way. To address this challenge, we propose a numerical model-based approach for the rational design of patient-specific SNC for tissue engineering applications. The approach is rooted in an in silico design library that allows for the selection of biomaterial and architecture combinations for the target application, resulting in reduced time, manpower and costs. To demonstrate the validity of the design strategy, a multiphasic SNC with predefined zone-specific properties that captured the complex zonal mechanical and compositional features of articular cartilage was developed based on the natural design of the native tissue.

在软组织工程学（STE）领域迫切需要开发具有高度生物学和机械功能以及模块化的生物材料，以便可以根据患者的特定要求对其进行定制。近来，由水溶胀的水凝胶基质和通过熔融电纺丝书写技术制造的增强纤维网络组成的仿生软网络复合材料（SNC）已显示出优异的机械和生物学性能，因此成为STE应用的强力候选者。但是，缺乏以非经验的方式定制和优化其性能的设计方法。为了应对这一挑战，我们提出了一种基于数值模型的方法，用于组织工程应用中特定于患者的SNC的合理设计。该方法植根于在silico设计库中，可以为目标应用选择生物材料和架构的组合，从而减少了时间，人力和成本。为了证明设计策略的有效性，基于天然组织的自然设计，开发了一种具有预定义的区域特定属性的多相SNC，该属性捕获了关节软骨的复杂的带状力学和成分特征。