Penn State Computational Biomechanics Group, Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA.
The Laboratory of Physicochemistry and Engineering of Proteins, Department of Biochemistry, Facultad de Medicina, National Autonomous University of Mexico, Mexico.
In Silico Biol. 2020;14(1-2):85-99. doi: 10.3233/ISB-180172.
Micro-Tissue Engineered Neural Networks (Micro-TENNs) are living three-dimensional constructs designed to replicate the neuroanatomy of white matter pathways in the brain and are being developed as implantable micro-tissue for axon tract reconstruction, or as anatomically-relevant in vitro experimental platforms. Micro-TENNs are composed of discrete neuronal aggregates connected by bundles of long-projecting axonal tracts within miniature tubular hydrogels. In order to help design and optimize micro-TENN performance, we have created a new computational model including geometric and functional properties. The model is built upon the three-dimensional diffusion equation and incorporates large-scale uni- and bi-directional growth that simulates realistic neuron morphologies. The model captures unique features of 3D axonal tract development that are not apparent in planar outgrowth and may be insightful for how white matter pathways form during brain development. The processes of axonal outgrowth, branching, turning and aggregation/bundling from each neuron are described through functions built on concentration equations and growth time distributed across the growth segments. Once developed we conducted multiple parametric studies to explore the applicability of the method and conducted preliminary validation via comparisons to experimentally grown micro-TENNs for a range of growth conditions. Using this framework, the model can be applied to study micro-TENN growth processes and functional characteristics using spiking network or compartmental network modeling. This model may be applied to improve our understanding of axonal tract development and functionality, as well as to optimize the fabrication of implantable tissue engineered brain pathways for nervous system reconstruction and/or modulation.
微组织工程神经网络 (Micro-TENNs) 是一种三维活体构建体，旨在复制大脑白质通路的神经解剖结构，并作为可植入的微组织用于轴突束重建，或作为与解剖相关的体外实验平台。Micro-TENNs 由离散的神经元聚集物组成，通过微型管状水凝胶中的长投射轴突束连接。为了帮助设计和优化 Micro-TENN 的性能，我们创建了一个新的计算模型，包括几何和功能特性。该模型基于三维扩散方程构建，并结合了大规模的单向和双向生长，模拟了真实的神经元形态。该模型捕捉到了 3D 轴突束发育的独特特征，这些特征在平面外生中并不明显，对于了解大脑发育过程中白质通路的形成可能具有启发性。通过基于浓度方程和分布在生长段上的生长时间的函数，描述了来自每个神经元的轴突生长、分支、转向和聚集/束集的过程。一旦开发完成，我们就进行了多次参数研究，以探索该方法的适用性，并通过与针对一系列生长条件的实验生长的 Micro-TENNs 进行初步验证来进行验证。使用这个框架，该模型可以应用于使用尖峰网络或分室网络建模来研究 Micro-TENN 的生长过程和功能特性。该模型可用于改善我们对轴突束发育和功能的理解，并优化用于神经系统重建和/或调节的可植入组织工程脑通路的制造。
