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

Toma Marinov, Haven A. López Sánchez, Liang Yuchi, Dayo O. Adewole, D. Kacy Cullen, Reuben H. Kraft

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)15-29
Number of pages15
JournalIn Silico Biology
Issue number1-2
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics


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