Modeling the Molecular Networks that Underlie the Formation and Consolidation of Memory

研究对象是长时记忆(LTM)的诱导和巩固的分子过程 大量的深入研究，研究提供了丰富的经验数据，这些数据将记忆的各个方面与 特定的细胞内信号通路。例如，实证研究正在阐明以下因素所起的作用 细胞外因子(如生长因子)、激酶活性和转录调节在诱导和 记忆的巩固。部分由于这些分子途径的复杂性和非线性特征，它 很难对这些通路对刺激的反应方式有一个直观的理解 方案或药物操作或受到单部位分子损伤的影响。为了提供更好的 为了更好地理解LTM背后的过程，本提案将开发 LTM两种典型模型背后的分子途径：1)长时突触易化(LTF) 和ii)长时程增强(LTP)。参数将受到经验数据的约束。参数 敏感度分析和新的聚类分析将评估模型的稳健性。目标1将我们的模型扩展到 LTF，它描述了PKA和ERK通过转录的磷酸化对转录的调节 因数CREB1和CREB2。扩展模型将包括额外的细胞内和细胞外组件 反馈环(如转化生长因子β和APNT)，一个额外的转录因子(C/EBP)，核糖体S6激酶(RSK) 和p38MAPK。在目标2中，该模型将用于预测刺激方案以及药理学 增强LTF和抢救受损LTF的治疗。Aim 3将扩展我们目前的LTP模型，即 描述了几种激酶通路(如MAPK、PKA、PKC和CaMKII)和组蛋白乙酰化的作用。这个 模型将纳入最近描绘的BDNF正反馈回路，这导致ERK的激活， CREB1的磷酸化，以及LTP巩固所必需的转录诱导。我们会 模拟刺激方案和药物效应，以预测可以挽救记忆受损的治疗方法 Rett综合征的机制，这是由改变转录因子活性的突变引起的 MeCP2，这可以挽救由突变引起的Rubinstein-Taybi综合征的受损机制 在CREB结合蛋白中。这种建议的方法使用模型来预测新的学习范例和/或 恢复正常可塑性的药物治疗是一种创新的方法，最终可能导致 开发治疗认知障碍的新策略。

Molecular processes that underlie the induction and consolidation of long-term memory (LTM) are the subjects of intensive research, and studies are providing a wealth of empirical data that relate aspects of memory to specific intracellular signaling pathways. For example, empirical studies are elucidating the roles played by extracellular factors (e.g. growth factors), kinase activity, and transcriptional regulation in induction and consolidation of memory. Due in part to the complexity and nonlinear features of these molecular pathways, it is difficult to develop an intuitive understanding of the ways in which these pathways respond to stimulus protocols or pharmacological manipulations or are affected by single-site molecular lesions. To provide a better understanding of the processes underlying LTM, the present proposal will develop quantitative models of the molecular pathways that underlie two well-characterized models of LTM: i) long-term synaptic facilitation (LTF) and ii) long-term synaptic potentiation (LTP). Parameters will be constrained by empirical data. Parameter sensitivity analysis and a novel cluster analysis will assess model robustness. Aim 1 will extend our model for LTF, which describes the regulation of transcription by PKA and ERK via phosphorylation of the transcription factors CREB1 and CREB2. The extended model will include components of additional intra- and extracellular feedback loops (e.g., TGFβ, and ApNT), an additional transcription factor (C/EBP), ribosomal s6 kinase (RSK) and p38 MAP kinase. In Aim 2, this model will be used to predict stimulus protocols, as well as pharmacological treatments, that enhance LTF and that rescue impaired LTF. Aim 3 will extend our current model of LTP, which describes roles of several kinase pathways (e.g., MAPK, PKA, PKC, and CAMKII) and histone acetylation. The model will incorporate a recently delineated BDNF positive-feedback loop, which leads to activation of ERK, phosphorylation of CREB1, and induction of transcription necessary for the consolidation of LTP. We will simulate stimulus protocols and drug effects to predict treatments that could rescue impaired memory mechanisms in Rett syndrome, which is caused by mutations that alter the activity of the transcription factor MeCP2, and that can rescue impaired mechanisms in Rubinstein-Taybi syndrome, which is caused by mutations in CREB binding protein. This proposed approach of using models to predict novel learning paradigms and/or drug treatments that restore normal plasticity, is an innovative methodology that could ultimately lead to the development of new strategies for the treatment of cognitive disorders.