Mathematical modeling of therapeutic neural stem cell migration in mouse brain with and without brain tumors

Abstract

<abstract><p>Neural stem cells (NSCs) offer a potential solution to treating brain tumors. This is because NSCs can circumvent the blood-brain barrier and migrate to areas of damage in the central nervous system, including tumors, stroke, and wound injuries. However, for successful clinical application of NSC treatment, a sufficient number of viable cells must reach the diseased or damaged area(s) in the brain, and evidence suggests that it may be affected by the paths the NSCs take through the brain, as well as the locations of tumors. To study the NSC migration in brain, we develop a mathematical model of therapeutic NSC migration towards brain tumor, that provides a low cost platform to investigate NSC treatment efficacy. Our model is an extension of the model developed in Rockne et al. (PLoS ONE 13, e0199967, 2018) that considers NSC migration in non-tumor bearing naive mouse brain. Here we modify the model in Rockne et al. in three ways: (i) we consider three-dimensional mouse brain geometry, (ii) we add chemotaxis to model the tumor-tropic nature of NSCs into tumor sites, and (iii) we model stochasticity of migration speed and chemosensitivity. The proposed model is used to study migration patterns of NSCs to sites of tumors for different injection strategies, in particular, intranasal and intracerebral delivery. We observe that intracerebral injection results in more NSCs arriving at the tumor site(s), but the relative fraction of NSCs depends on the location of injection relative to the target site(s). On the other hand, intranasal injection results in fewer NSCs at the tumor site, but yields a more even distribution of NSCs within and around the target tumor site(s).</p></abstract>

In vivo imaging in transgenic songbirds reveals superdiffusive neuron migration in the adult brain

“…Our simulation revealed that diffusion-like migration is sufficient to populate HVC and nearby regions with new neurons across biologically relevant timescales. This generative model is part of a growing class of quantitative models that simulate cellular dynamics and pattern formation in tissue development [70][71][72][73][74] , including neuronal migration [75][76][77][78] .…”

Brain Tumor Segmentation Based on Bendlet Transform and Improved Chan-Vese Model

“…When working with cross-sectional images, one of the most complex problems is segmenting out some specific tissues. Segmentation helps doctors more accurately locate the lesion and assess the severity of the lesion, and is an essential and critical process in disease treatment [ 1 ]. Manual localization and segmentation of tumor regions by physicians is an expensive, time-consuming, and tedious task, and the segmentation results are not reproducible.…”