Li Biao, Sierra Amanda, Deudero Juan Jose, Semerci Fatih, Laitman Andrew, Kimmel Marek, Maletic-Savatic Mirjana
Departments of Bioengineering and Statistics, Rice University, Houston, Texas, 77005, USA.
Department of Pediatrics, Baylor College of Medicine, Houston, Texas, 77030, USA.
BMC Syst Biol. 2017 Oct 3;11(Suppl 5):90. doi: 10.1186/s12918-017-0468-3.
Adult hippocampal neurogenesis, the process of formation of new neurons, occurs throughout life in the hippocampus. New neurons have been associated with learning and memory as well as mood control, and impaired neurogenesis has been linked to depression, schizophrenia, autism and cognitive decline during aging. Thus, understanding the biological properties of adult neurogenesis has important implications for human health. Computational models of neurogenesis have attempted to derive biologically relevant knowledge, hard to achieve using experimentation. However, the majority of the computational studies have predominantly focused on the late stages of neurogenesis, when newborn neurons integrate into hippocampal circuitry. Little is known about the early stages that regulate proliferation, differentiation, and survival of neural stem cells and their immediate progeny.
Here, based on the branching process theory and biological evidence, we developed a computational model that represents the early stage hippocampal neurogenic cascade and allows prediction of the overall efficiency of neurogenesis in both normal and diseased conditions. Using this stochastic model with a simulation program, we derived the equilibrium distribution of cell population and simulated the progression of the neurogenic cascade. Using BrdU pulse-and-chase experiment to label proliferating cells and their progeny in vivo, we quantified labeled newborn cells and fit the model on the experimental data. Our simulation results reveal unknown but meaningful biological parameters, among which the most critical ones are apoptotic rates at different stages of the neurogenic cascade: apoptotic rates reach maximum at the stage of neuroblasts; the probability of neuroprogenitor cell renewal is low; the neuroblast stage has the highest temporal variance within the cell types of the neurogenic cascade, while the apoptotic stage is short.
At a practical level, the stochastic model and simulation framework we developed will enable us to predict overall efficiency of hippocampal neurogenesis in both normal and diseased conditions. It can also generate predictions of the behavior of the neurogenic system under perturbations such as increase or decrease of apoptosis due to disease or treatment.
成体海马神经发生，即新神经元形成的过程，在海马体中终生存在。新神经元与学习、记忆以及情绪控制有关，而神经发生受损与抑郁症、精神分裂症、自闭症以及衰老过程中的认知衰退有关。因此，了解成体神经发生的生物学特性对人类健康具有重要意义。神经发生的计算模型试图获取生物学相关知识，而这很难通过实验实现。然而，大多数计算研究主要集中在神经发生的后期，即新生神经元融入海马回路时。对于调节神经干细胞及其直接后代的增殖、分化和存活的早期阶段，我们了解甚少。
在此，基于分支过程理论和生物学证据，我们开发了一个计算模型，该模型代表海马神经发生早期级联反应，并能够预测正常和患病条件下神经发生的整体效率。使用这个带有模拟程序的随机模型，我们推导了细胞群体的平衡分布，并模拟了神经发生级联反应的进程。通过在体内进行BrdU脉冲追踪实验来标记增殖细胞及其后代，我们对标记的新生细胞进行了量化，并将模型与实验数据进行拟合。我们的模拟结果揭示了一些未知但有意义的生物学参数，其中最关键的是神经发生级联反应不同阶段的凋亡率：凋亡率在成神经细胞阶段达到最高；神经祖细胞更新的概率较低；成神经细胞阶段在神经发生级联反应的细胞类型中具有最高的时间方差，而凋亡阶段较短。
在实际层面，我们开发的随机模型和模拟框架将使我们能够预测正常和患病条件下海马神经发生的整体效率。它还可以生成神经发生系统在诸如疾病或治疗导致的凋亡增加或减少等扰动下的行为预测。
