Yetişkin Hipokampal Nörogenezinin Yarı-stokastik Nümerik Bir Modeli

Year 2019, Volume: 23 Issue: 1, 195 - 203, 01.04.2019

Pınar Öz

https://doi.org/10.19113/sdufenbed.471807

Abstract

Dentat girusta (DG) görülen yetişkin nörogenezinin
hipokampal bellek ağlarındaki işleve önemli bir katkı sunduğu kabul
edilmektedir. Sunulan ayrık sayısal model DG'de bulunan nöral progenitör hücre
(NPH) popülasyonlarında ve bu süreçlerin ürünlerindeki (immatür nöron, astrosit
ve oligodendrosit popülasyonları) temporal değişimleri modellemeyi
amaçlamaktadır. Süreçler toplam hacimde bir limitin olmadığı ve nörogenezi
yönlendiren tüm kimyasal ve fiziksel düzenleyicilerin devamlı ulaşılabilir
olduğu ideal bir ortamda tanımlanmıştır. Sistem üç temel seviyede bağımsız
olarak çalışmaktadır. Her seviye nörogenez süreçlerindeki bir aşama olarak
tanımlanmıştır ve popülasyonlar üç temel hücre tipinden oluşmaktadır: Tip I
(radyal glia), tip II (geçici çoğalan hücre) ve tip III (nöroblast). Hücre
kaderi, her hücre tipi için bir popülasyon limiti olan yarı-stokastik bir süreç
(bir seçim) olarak sisteme eklenmiştir. Sunulan model, ayrık süreçlere
dayanmasına ve basitleştirilmiş bir yaklaşım izlemesine rağmen, yetişkin
nörogenezinin sayısal bir taslağını başarılı şekilde üretmektedir ve farklı
modülasyonlarla bir hipokampal trisinaptik devre ağına yerleştirilebilir.

Keywords

Yetişkin nörogenezi, Subgranüler zon, Hesaplamalı model

A Semi-stochastic Numerical Model of Adult Hippocampal Neurogenesis

Abstract

Adult neurogenesis in dentate gyrus (DG) is a prominent
contributor in the dynamics of hippocampal memory networks. This discrete model
aims to estimate the temporal changes in the neural progenitor cell (NPC)  populations in DG, together with the products
of differentiation – immature neurons, astrocytes and oligodendrocytes. The
dynamics are described in an ideal environment, where there is no limit for the
total volume and all required chemical and physical cues that direct
neurogenesis are continuously available. The system works independently on
three levels. Each level is defined as the dynamics in a stage of neurogenesis
with three types of NPCs: type I cell (radial glia), type II cell (transiently
amplifying cells) and type III cell (neuroblasts). Cell fate was introduced as
a semi-stochastic process (a choice) with a population limit for each cell
type. Although it is based on discrete processes and has a rather simplistic
approach, the simulations successfully provide a numerical template for adult
neurogenesis, which can be further modified and implemented in a hippocampal
trisynaptic loop network.

Keywords

Adult neurogenesis, Subgranular zone, Computational model

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