1 research outputs found
Computational model of axon guidance
Axon guidance (AG) towards their target during embryogenesis or after injury
is an important issue in the development of neuronal networks. During their
growth, axons often face complex decisions that are difficult to understand
when observing just a small part of the problem. In this work we propose a
computational model of AG based on activity-independent mechanisms that takes
into account the most important aspects of AG.
The model includes the main elements (neurons, with soma, axon and growth
cone; glial cells acting as guideposts) and mechanisms (attraction/repulsion
guidance cues, growth cone adaptation, tissue-gradient intersections, axonal
transport, changes in the growth cone complexity and a range of responses for
each receptor). The growth cone guidance is defined as a function that maps the
receptor activation by ligands into a repulsive or attractive force. This force
is then converted into a turn- ing angle using spherical coordinates. A
regulatory network between the receptors and the intracellular proteins is
considered, leading to more complex and realistic behaviors. The ligand
diffusion through the extracellular environment is modeled with linear or
exponential functions. Concerning experimentation, it was developed the first
computational model and a new theoretical model of the midline crossing of
Drosophila axons that focus all the decision points. The computational model
created allows describing to a great extent the behaviors that have been
reported in the literature, for three different pathfinding scenarios: (i)
normal, (ii) comm mutant and (iii) robo mutant. Moreover, this model suggests
new hypotheses, being the most relevant the existence of an inhibitory link
between the DCC receptor and the Comm protein that is Netrin-mediated or
mediated by a third unknown signal.Comment: Master research thesi