CRCNS: Computational Model for Neural Stem Cell Divisions in the Adult Brain

CRCNS：成人大脑神经干细胞分裂的计算模型

• 批准号: 8111273
• 负责人: GRIGORI N ENIKOLOPOV
• 金额: $ 33.45万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111273
• 关键词: Adopted; Adult; Affect; Age; Aging; Antidepressive Agents; Award; Biology; Brain; Brain region; Cell Differentiation process; Cell Proliferation; Cells; Cellular Morphology; Computer Simulation; Computing Methodologies; Data; Dependence; Environment; Event; Fluoxetine; Gene Expression; Gene Expression Regulation; Genes; Goals; Hippocampus (Brain); Impaired cognition; Incidence; Investigation; Learning; Life; Memory; Mental Depression; Methods; Modeling; Molecular Profiling; Monitor; Moods; Neurobiology; Neurons; Output; Pharmacotherapy; Postdoctoral Fellow; Process; Production; Property; Prozac; Public Health; Research; Research Methodology; Science; Societies; Staging; Stem cells; Stimulus; Synapses; Systems Biology; Techniques; Testing; Time; Training; Transcriptional Regulation; Woman; adult neurogenesis; age effect; base; cell type; computer framework; computer studies; dentate gyrus; genome-wide; granule cell; men; nerve stem cell; neurogenesis; neuromechanism; neuronal circuitry; novel; novel strategies; research study; stem; stem cell differentiation; stem cell division

DESCRIPTION (provided by applicant): Adult brain retains the ability to produce new neurons throughout life. New neurons are generated from stem cells through a cascade of events which include symmetric and asymmetric divisions and continuous changes of cell morphology. The cascade culminates with the young neurons establishing connections with other cells and becoming integrated into the pre-existing neuronal circuitry. Persistent neurogenesis is observed only in a number of the adult brain regions. In one of them, the hippocampal dentate gyrus, new neurons may be important for learning, memory, and mood. Adult neurogenesis is a dynamic process that responds to a wide range of stimuli which can enhance or suppress its output and may affect any step of the differentiation cascade. For instance, an antidepressant drug fluoxetine (Prozac) enhances, whereas aging decreases, hippocampal neurogenesis. However, the steps of the differentiation cascade affected by pro- or anti-neurogenic factors and the mechanisms of their action are not known. The main goal of this collaborative project is to develop a computational model of adult neurogenesis. Towards this goal, we will develop a novel research method that integrates computational and experimental techniques for a quantitative investigation of the steps that define adult neurogenesis. A new approach developed in Enikolopov lab (experimental collaborator) uses a set of genetically encoded markers to monitor the progression of progenitor cells through the differentiation cascade. This approach allows to determine the abundances of different cell types as a function of time as the cells divide and differentiate. The abundances are evaluated only for cells that were dividing in the beginning of the experiment and correspond to the pair wise correlation function for different cell types. To convert the abundances as a function of time into the rates of division and differentiation of various cell types we will use the computational model developed by the group of Dr. Koulakov. This computational model will also be used to determine the effects of aging and antidepressants on the parameters of division and differentiation cascade. We will investigate the changes occurring in the genome-wide gene expression profiles as a function of stage of the differentiation cascade. We will monitor the dependence of gene expression on both aging and antidepressants and will elucidate the underlying gene regulation dynamics. Finally, we will study theoretically the putative computational properties of the adult neurogenesis. The specific aims (SA) of this proposal include: SA 1: To develop a computational model for the stem cell division and differentiation cascade in the adult hippocampus. This aim will allow inferring the division diagram and transition rates from the experimental data and will allow to study the changes induced by aging and antidepressants. SA 2: To dissect the transcription regulation network controlling adult hippocampal neurogenesis. We will investigate changes of gene expression associated with aging and antidepressant drugs and will uncover potential regulatory network mechanisms. SA 3: To study the unique computational properties of neural stem cells. Here we will calculate the rate of learning as a function of sparseness of representation and will argue that cell-based learning rules adopt to new stimuli faster than conventional synapse based Hebb rules. Intellectual merit: The proposed research will contribute to systems biology on two levels. First, we will elucidate the mechanisms of neural stem differentiation leading to the production of new neurons. Second, we will develop methods for determining division and differentiation rates from time-dependent data of cell abundances. This computational framework may become standard in the studies of stem cell differentiation in other fields of biology. Broader impacts: This project is based on the synergy between theoretical sciences, novel computational methods, and cutting-edge experiments in neurobiology. The award will provide a unique crossdisciplinary environment for training of young neuroscientists. We expect that two postdoctoral fellows, specializing in theoretical and in experimental approaches, will receive training through this award. To broader society: Our studies will help to elucidate the mechanisms of cognitive decline associated with aging and to determine the targets of antidepressant drug therapies. Because the lifetime incidence of depression in the US is more than 12% in men and 20% in women, our studies may substantially contribute to public health.

描述（由申请人提供）：成人大脑终生保留产生新神经元的能力。新的神经元是通过一系列事件从干细胞产生的，这些事件包括对称和不对称分裂以及细胞形态的连续变化。随着年轻神经元与其他细胞建立连接并整合到预先存在的神经元回路中，该级联达到顶峰。仅在许多成人大脑区域中观察到持续的神经发生。其中之一是海马齿状回，新的神经元可能对学习、记忆和情绪很重要。成体神经发生是一个动态过程，对多种刺激作出反应，这些刺激可以增强或抑制其输出，并可能影响分化级联的任何步骤。例如，抗抑郁药氟西汀（百忧解）可增强海马神经发生，而衰老则可降低海马神经发生。然而，受促神经源性因子或抗神经源性因子影响的分化级联的步骤及其作用机制尚不清楚。该合作项目的主要目标是开发成人神经发生的计算模型。为了实现这一目标，我们将开发一种新颖的研究方法，整合计算和实验技术，对定义成人神经发生的步骤进行定量研究。 Enikolopov 实验室（实验合作者）开发的一种新方法使用一组基因编码标记来监测祖细胞通过分化级联的进展。这种方法可以确定不同细胞类型的丰度作为细胞分裂和分化时时间的函数。仅评估在实验开始时分裂的细胞的丰度，并且对应于不同细胞类型的成对相关函数。为了将作为时间函数的丰度转换为各种细胞类型的分裂和分化率，我们将使用 Koulakov 博士小组开发的计算模型。该计算模型还将用于确定衰老和抗抑郁药物对分裂和分化级联参数的影响。我们将研究全基因组基因表达谱中发生的变化作为分化级联阶段的函数。我们将监测基因表达对衰老和抗抑郁药物的依赖性，并阐明潜在的基因调控动态。最后，我们将从理论上研究成人神经发生的假定计算特性。该提案的具体目标（SA）包括： SA 1：开发成人海马干细胞分裂和分化级联的计算模型。这一目标将允许从实验数据推断分裂图和转换率，并将允许研究衰老和抗抑郁药物引起的变化。 SA 2：剖析控制成人海马神经发生的转录调控网络。我们将研究与衰老和抗抑郁药物相关的基因表达变化，并揭示潜在的调控网络机制。 SA 3：研究神经干细胞独特的计算特性。在这里，我们将计算学习率作为表示稀疏性的函数，并认为基于细胞的学习规则比传统的基于突触的 Hebb 规则更快地适应新的刺激。 智力价值：拟议的研究将在两个层面上对系统生物学做出贡献。首先，我们将阐明神经干分化导致新神经元产生的机制。其次，我们将开发根据细胞丰度的时间依赖性数据确定分裂和分化率的方法。这种计算框架可能成为其他生物学领域干细胞分化研究的标准。 更广泛的影响：该项目基于理论科学、新颖的计算方法和神经生物学前沿实验之间的协同作用。该奖项将为培养年轻神经科学家提供独特的跨学科环境。我们预计两名专门从事理论和实验方法的博士后研究员将通过该奖项接受培训。对更广泛的社会：我们的研究将有助于阐明与衰老相关的认知能力下降的机制，并确定抗抑郁药物治疗的目标。由于美国抑郁症的终生发病率男性超过 12%，女性超过 20%，因此我们的研究可能会对公共健康做出重大贡献。