ArticleScientific reports2022
Modeling neuron growth using isogeometric collocation based phase field method.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 29 citations in OpenAlex.
- Neurons in a Dish: A Review of In Vitro Cell Models for Studying Neurogenesis.Journal of neurochemistry · 2026Review
- Nascent actin dynamics and the disruption of calcium dynamics by actin arrest in developing neural cell networks.Communications biology · 2025Article
- Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.Journal of mathematical biology · 2024Article
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
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Authors and funding
8 authors at 3 institutions in 2 countries.
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Abstract
We present a new computational framework of neuron growth based on the phase field method and develop an open-source software package called "NeuronGrowth_IGAcollocation". Neurons consist of a cell body, dendrites, and axons. Axons and dendrites are long processes extending from the cell body and enabling information transfer to and from other neurons. There is high variation in neuron morphology based on their location and function, thus increasing the complexity in mathematical modeling of neuron growth. In this paper, we propose a novel phase field model with isogeometric collocation to simulate different stages of neuron growth by considering the effect of tubulin. The stages modeled include lamellipodia formation, initial neurite outgrowth, axon differentiation, and dendrite formation considering the effect of intracellular transport of tubulin on neurite outgrowth. Through comparison with experimental observations, we can demonstrate qualitatively and quantitatively similar reproduction of neuron morphologies at different stages of growth and allow extension towards the formation of neurite networks.
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