Applications of Bayesian Inference for Modelling Dynamic Instability in Neuronal Dendrite Morphogenesis

Authors

Daniel Fridman, Yale University

Abstract

Neurons are complex biological systems which develop intricate morphologies and whose dendrites are essential in receiving and integrating input signals from neighboring neurons. While much research has been done on the role of dendrites in neuronal development, a further understanding of dendrite dynamics can provide insight into neural development and the cellular basis of neurological diseases such as schizophrenia, Down’s syndrome, and autism. The Jonathon Howard lab hypothesizes that microtubules are a primary driving force in dendrite dynamics. Since it is known that microtubules display dynamic instability, rapidly transitioning between growth, paused, and shrinking states, the Howard lab proposes a similar 3-state transition model for dendrite dynamics. However, this model remains to be rigorously evaluated on dendrite branch data. In this paper, I develop a novel implementation of the Gibbs sampling algorithm for parameterization of the proposed 3-state mixture model, improving upon prior parameterization methods such as least squares fitting. Furthermore, I apply the algorithm on a confocal microscopy dataset of measured dendrite branch velocities from Class IV dendritic arbors in Drosophila melanogaster, demonstrating a good fit of the model to the data.

Recommended Citation

Fridman, Daniel (2020) "Applications of Bayesian Inference for Modelling Dynamic Instability in Neuronal Dendrite Morphogenesis," The Yale Undergraduate Research Journal: Vol. 1: Iss. 1, Article 12.
Available at: https://elischolar.library.yale.edu/yurj/vol1/iss1/12