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A Computational Model of Bidirectional Axonal Growth in Micro-Tissue Engineered Neuronal Networks (micro-TENNs)

Toma Marinov, Liang Yuchi, Dayo O. Adewole, View ORCID ProfileD. Kacy Cullen, View ORCID ProfileReuben H. Kraft
doi: https://doi.org/10.1101/369843
Toma Marinov
1Penn State Computational Biomechanics Group, Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
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Liang Yuchi
1Penn State Computational Biomechanics Group, Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
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Dayo O. Adewole
3Center for Brain Injury & Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
4Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
5Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
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D. Kacy Cullen
3Center for Brain Injury & Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
4Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA
5Center for Neurotrauma, Neurodegeneration & Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
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Reuben H. Kraft
1Penn State Computational Biomechanics Group, Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA
2Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, USA
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Abstract

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 microtissue 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, this 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 16, 2018.
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A Computational Model of Bidirectional Axonal Growth in Micro-Tissue Engineered Neuronal Networks (micro-TENNs)
Toma Marinov, Liang Yuchi, Dayo O. Adewole, D. Kacy Cullen, Reuben H. Kraft
bioRxiv 369843; doi: https://doi.org/10.1101/369843
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A Computational Model of Bidirectional Axonal Growth in Micro-Tissue Engineered Neuronal Networks (micro-TENNs)
Toma Marinov, Liang Yuchi, Dayo O. Adewole, D. Kacy Cullen, Reuben H. Kraft
bioRxiv 369843; doi: https://doi.org/10.1101/369843

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