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Modeling neuron growth using isogeometric collocation based phase field method.

Kuanren Qian1, Aishwarya Pawar2, Ashlee Liao1

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, 15213, USA.

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|May 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational framework for modeling neuron growth, simulating key stages like neurite outgrowth and axon differentiation using tubulin dynamics. The open-source software package accurately reproduces neuron morphologies, aiding future research into neural networks.

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Area of Science:

  • Computational neuroscience
  • Biophysics
  • Developmental biology

Background:

  • Neuron morphology is highly variable, complicating mathematical modeling of neuron growth.
  • Understanding neuron growth is crucial for neuroscience and developmental biology.

Purpose of the Study:

  • To develop a novel computational framework for simulating neuron growth.
  • To model various stages of neuron development, including neurite outgrowth and differentiation.
  • To investigate the role of tubulin dynamics in neuron morphology.

Main Methods:

  • Utilized a phase field method combined with isogeometric collocation.
  • Developed an open-source software package named "NeuronGrowth_IGAcollocation".
  • Modeled stages: lamellipodia formation, initial neurite outgrowth, axon differentiation, and dendrite formation.

Main Results:

  • Successfully simulated different stages of neuron growth, including the effect of tubulin.
  • Demonstrated qualitatively and quantitatively similar reproduction of neuron morphologies compared to experimental data.
  • The framework allows for extension towards modeling neurite network formation.

Conclusions:

  • The proposed phase field model with isogeometric collocation provides a robust computational framework for neuron growth simulation.
  • The "NeuronGrowth_IGAcollocation" software package offers a valuable tool for researchers studying neuronal development.
  • This approach can be extended to simulate complex neurite network formation and function.