Abstract

            Background
            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.
          
          
            Results
            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.
          
          
            Conclusion
            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.