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

项目总结/摘要 这项提议将检验一个假设，即学习和记忆可以通过计算来改善。 设计训练方案，优化激酶级联和转录因子之间的相互作用 参与长期记忆（LTM）的诱导。将使用三种模型系统：长期致敏， 恐惧条件反射和消退，以及抑制性回避学习。这个假设是基于我们之前的 工作表明，计算设计的协议，最大限度地提高蛋白质之间的活动重叠 激酶A（PKA）和丝裂原活化蛋白激酶（MAPK）亚型称为细胞外信号调节激酶。 细胞外信号调节激酶（ERK）增强长时程突触易化（LTF）和LTM的致敏作用，以及获得 和恐惧学习的消失。这项建议有两个关键的创新方面。首先，我们利用一种新颖的，多- 学科策略，以加强学习和提高不同类型的记忆检索。药理 改善学习和记忆以及挽救记忆缺陷的干预措施一直在进行， 几十年来，但这些方法依赖于试错法，并且高度非特异性。相比之下，我们的战略 结合生物学上现实的计算模型和经验方法，使我们能够 有效和系统地探索不同类型的长时程突触的分子过程， 可塑性，并预测个人训练方案，以优化学习和记忆。第二，据我们所知， 我们的研究小组是第一个开发出一个计算模型来描述婴儿的潜在机制的。 学习和与婴儿健忘症相关的明显快速遗忘。我们的模拟显示， 调节幼年动物的激酶和转录因子的基础活性有助于快速忘记 幼稚的记忆通过模拟和体内实验检验的具体假设包括： 并且用于致敏的LTM可以通过新颖的计算设计的训练方案延长长达7天; 目的2）基于杏仁核激酶动力学的计算设计的方案可以增强 条件性恐惧记忆的获得和消退;以及目标3）婴儿的明显快速遗忘 在抑制性回避范例中观察到的记忆可以通过计算设计的协议来克服 基于海马激酶的动力学。我们认为，这些预测，结合并发 实证测试，将提供一个有效的策略，以加强学习和改善记忆的原则证明 检索我们的研究可能对旨在促进记忆形成的干预措施具有临床意义， 一系列与人类认知障碍相关的精神疾病，以及用于改善 焦虑相关疾病患者的预防治疗。

PROJECT SUMMARY/ABSTRACT This proposal will test the hypothesis that learning and memory can be improved by using computationally designed training protocols that optimize the interactions among kinase cascades and transcription factors involved in the induction of long-term memory (LTM). Three model systems will be used: long-term sensitization, fear conditioning and extinction, and inhibitory avoidance learning. This hypothesis is based on our previous work demonstrating that computationally designed protocols maximizing the overlap of activities between protein kinase A (PKA) and the mitogen-activated protein kinase (MAPK) isoform termed extracellular signal-regulated kinase (ERK) enhance long-term synaptic facilitation (LTF) and LTM for sensitization, as well as the acquisition and extinction of fear learning. This proposal has two key innovative aspects. First, we utilize a novel, multi- disciplinary strategy to enhance learning and improve different types of memory retrieval. Pharmacological interventions to improve learning and memory, and rescue memory deficits, have been ongoing for many decades, but these approaches rely on trial-and-error and are highly nonspecific. In contrast, the strategy we have developed, combining biologically realistic computational models with empirical approaches, enables us to efficiently and systematically explore the molecular processes that underlie different types of long-term synaptic plasticity, and predict individual training protocols to optimize learning and memory. Second, to our knowledge, our groups are the first to develop a computational model describing the possible mechanism underlying infantile learning and the apparent rapid forgetting associated with infantile amnesia. Our simulations suggest that altered regulation of basal activities of kinases and transcription factors in infant animals contributes to fast forgetting of infantile memory. Specific hypotheses to be tested by simulation and in vivo experiments include: Aim 1) LTF and LTM for sensitization can be prolonged up to 7 days by novel computationally designed training protocols; Aim 2) Computationally designed protocols based on the dynamics of amygdala kinases can enhance the acquisition and extinction of conditioned fear memories; and Aim 3) The apparent rapid forgetting of infantile memory observed in an inhibitory avoidance paradigm can be overcome by computationally designed protocols based on the dynamics of hippocampal kinases. We believe that these predictions, combined with concurrent empirical tests, will provide a proof of principle for an efficient strategy to enhance learning and improve memory retrieval. Our study may have clinical relevance for interventions aiming at facilitating memory formation in a series of psychiatric disorders associated with cognitive impairment in humans, as well as for improving extinction-based therapies in patients suffering from anxiety-related disorders.