Skip to Main content Skip to Navigation

Modelling and characterizing axon growth from in vivo data

Agustina Razetti 1
1 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : How the brain wires up during development remains an open question in the scientific community across disciplines. Fruitful efforts have been made to elucidate the mechanisms of axonal growth, such as pathfinding and guiding molecules. However, recent evidence suggests other actors to be involved in neuron growth in vivo. Notably, axons develop in populations and embedded in mechanically constrained environments. Thus, to fully understand this dynamic process, one must take into account collective mechanisms and mechanical interactions within the axonal populations. However, techniques to directly measure this from living brains are today lacking or heavy to implement. This thesis emerges from a multidisciplinary collaboration, to shed light on axonal development in vivo and how adult complex axonal morphologies are attained. Our work is inspired and validated from images of single wild type and mutated Drosophila y axons, which we have segmented and normalized. We first proposed a mathematical framework for the morphological study and classification of axonal groups. From this analysis we hypothesized that axon growth derives from a stochastic process, and that the variability and complexity of axonal trees result from its intrinsic nature, as well as from elongation strategies developed to overcome the mechanical constraints of the developing brain. We designed a mathematical model of single axon growth based on Gaussian Markov Chains with two parameters, accounting for axon rigidity and attraction to the target field. We estimated the model parameters from data, and simulated the growing axons embedded in spatially constraint populations to test our hypothesis. We dealt with themes from applied mathematics as well as from biology, and unveiled unexplored effects of collective growth on axonal development in vivo.
Complete list of metadata

Cited literature [118 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Wednesday, September 5, 2018 - 12:59:07 PM
Last modification on : Wednesday, October 14, 2020 - 4:22:39 AM
Long-term archiving on: : Thursday, December 6, 2018 - 4:53:52 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01868324, version 1



Agustina Razetti. Modelling and characterizing axon growth from in vivo data. Signal and Image processing. COMUE Université Côte d'Azur (2015 - 2019), 2018. English. ⟨NNT : 2018AZUR4016⟩. ⟨tel-01868324⟩



Record views


Files downloads