微组织工程神经元网络(micro-TENNs)中轴突双向生长的计算模型。

Q2 Medicine
Toma Marinov, Haven A López Sánchez, Liang Yuchi, Dayo O Adewole, D Kacy Cullen, Reuben H Kraft
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引用次数: 0

摘要

微组织工程神经网络(Micro-TENNs)是一种活体三维构造物,旨在复制大脑白质通路的神经解剖结构,目前正被开发为轴突束重建的可植入微组织,或作为解剖学相关的体外实验平台。微型神经网由离散的神经元聚集体组成,这些神经元聚集体由微型管状水凝胶中长轴突束连接而成。为了帮助设计和优化微型天网的性能,我们创建了一个新的计算模型,其中包括几何和功能特性。该模型建立在三维扩散方程的基础上,包含大规模单向和双向生长,可模拟真实的神经元形态。该模型捕捉到了三维轴突束发育的独特特征,而这些特征在平面轴突生长中并不明显,它可能对大脑发育过程中白质通路的形成过程具有启发意义。每个神经元的轴突生长、分支、转向和聚集/捆绑过程都是通过基于浓度方程的函数和分布在各生长区段的生长时间来描述的。开发完成后,我们进行了多项参数研究,以探索该方法的适用性,并通过与一系列生长条件下实验生长的微神经元进行比较,进行了初步验证。利用这一框架,该模型可通过尖峰网络或分区网络建模来研究微天牛的生长过程和功能特征。该模型可用于提高我们对轴突束发育和功能的理解,以及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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A computational model of bidirectional axonal growth in micro-tissue engineered neuronal networks (micro-TENNs).

A computational model of bidirectional axonal growth in micro-tissue engineered neuronal networks (micro-TENNs).

A computational model of bidirectional axonal growth in micro-tissue engineered neuronal networks (micro-TENNs).

A computational model of bidirectional axonal growth in micro-tissue engineered neuronal networks (micro-TENNs).

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.

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来源期刊
In Silico Biology
In Silico Biology Computer Science-Computational Theory and Mathematics
CiteScore
2.20
自引率
0.00%
发文量
1
期刊介绍: The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.
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