Modeling Gene Regulation for Long-Term Plasticity

描述（由申请人提供）：该项目将建立一个基于数学模型的框架，a)描述突触可塑性从调节基因转录的分子过程中产生的机制，b)测试机制假设，例如特定蛋白激酶的拟议作用。该项目建立在我们之前的模型的基础上，该模型描述了软体动物长期突触促进（LTF）和长期记忆（LTM）形成的基因和蛋白质网络的各个方面。该模型基于Ca2*/cAMP反应元件结合蛋白（CREB，在大洋洲称为ApCREB1）和相关转录因子的转录调控。我们将扩展这一模型，纳入最近被证明对LTF至关重要的基因调控的其他元素。此外，我们将开发一个类似的模型来模拟脊椎动物长期突触增强（LTP）诱导的生化事件。LTF和LTP都被认为在LTM的形成中起着至关重要的作用，并且LTF的诱导和LTP的诱导表现出机制上的相似性，例如依赖于MAP激酶的激活。因此，能够模拟LTF和LTP归纳的建模框架可能会显著增加对学习机制的理解。

DESCRIPTION (provided by applicant): This project wilt develop a framework based on mathematical modeling that a) describes the mechanism by which synaptic plasticity emerges from molecular processes regulating gene transcription, and b) tests mechanistic hypotheses, such asproposed roles of specific protein kinases. The project builds upon our previous model describing aspects of the gene and protein network responsible for long-term synaptic facilitation (LTF) and the formation of longterm memory (LTM) in the mollusk Aplysia. This model is based on transcriptional regulation by Ca2*/cAMP response element - binding protein (CREB, termed ApCREB1 in Aplysia) and related transcription factors. We will extend this model to incorporate additional elements of gene regulation recently demonstrated to be essential for LTF. In addition, we will develop an analogous model to simulate biochemical events underlying the induction of long-term synaptic potentiation (LTP) in vertebrates. Both LTF and LTP are thought to play essential roles in the formation of LTM, and the LTF induction and LTP induction exhibit mechanistic similarities, such as dependence on MAP kinase activation. Therefore, a modeling framework that can simulate aspects of both LTF and LTP induction is likely to significantly increase the understanding of learning mechanisms. The LTF model variant will incorporate additional transcriptional regulators essential for LTF, such as ApCREB2, and ApC/EBP. Bifurcation analysis and pre-programmed integrations will identify key control parameters which are plausible sites of physiological regulation and which, when varied, have important effects on the dynamics of the model. We will then use the model to simulate the results of experimental protocols in which alterations are made in the activity of the transcriptional regulators listed above. A minimal set of variations in key control parameters will be identified that allows simulation of data from these protocols. This approach is likely to help identify the key mechanisms that determine the amount of LTF induced by different training protocols. The LTP model variant will be used to simulate three common stimulus protocols that induce hippocampal late LTP. These protocols are high-frequency (tetanic) stimulation, theta-burst stimulation, and stimulation by forskolin. Parameters will be optimized to fit experimental time courses of nuclear [Ca 2+] and of kinase and transcription factor activities. The model will then be used to test the hypothesis that CREB kinases other than protein kinase A, such as ribosomal $6 kinase 2, are primarily responsible for CREB phosphorylation and LTP induction.