Reference : A mathematical model of adult subventricular neurogenesis.
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/122388
A mathematical model of adult subventricular neurogenesis.
English
Ashbourn, JM [> > > >]
Miller, JJ [> > > >]
Reumers, V [> > > >]
Baekelandt, V [> > > >]
Geris, Liesbet mailto [Université de Liège - ULg > Département d'aérospatiale et mécanique > Génie biomécanique]
2012
Journal of the Royal Society, Interface
Royal Society
Yes (verified by ORBi)
International
1742-5689
1742-5662
[en] Neurogenesis has been the subject of active research in recent years and many authors have explored the phenomenology of the process, its regulation and its purported purpose. Recent developments in bioluminescent imaging (BLI) allow direct in vivo imaging of neurogenesis, and in order to interpret the experimental results, mathematical models are necessary. This study proposes such a mathematical model that describes adult mammalian neurogenesis occurring in the subventricular zone and the subsequent migration of cells through the rostral migratory stream to the olfactory bulb (OB). This model assumes that a single chemoattractant is responsible for cell migration, secreted both by the OB and in an endocrine fashion by the cells involved in neurogenesis. The solutions to the system of partial differential equations are compared with the physiological rodent process, as previously documented in the literature and quantified through the use of BLI, and a parameter space is described, the corresponding solution to which matches that of the rodent model. A sensitivity analysis shows that this parameter space is stable to perturbation and furthermore that the system as a whole is sloppy. A large number of parameter sets are stochastically generated, and it is found that parameter spaces corresponding to physiologically plausible solutions generally obey constraints similar to the conditions reported in vivo. This further corroborates the model and its underlying assumptions based on the current understanding of the investigated phenomenon. Concomitantly, this leaves room for further quantitative predictions pertinent to the design of future proposed experiments.
http://hdl.handle.net/2268/122388
10.1098/rsif.2012.0193

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