Modeling Gene Regulation for Long-Term Plasticity

描述(申请人提供)：这个项目将开发一个基于数学模型的框架，a)描述从调节基因转录的分子过程中出现突触可塑性的机制，以及b)测试机械假说，如提出的特定蛋白激酶的作用。该项目建立在我们之前的模型基础上，描述了软体动物海兔中负责长期突触促进(LTF)和长期记忆(LTM)形成的基因和蛋白质网络的各个方面。该模型基于钙/cAMP反应元件结合蛋白(CREB，在海兔中称为ApCREB1)和相关转录因子的转录调控。我们将扩展这一模型，以纳入最近被证明对LTF至关重要的基因调控的额外元素。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)诱导下的生化事件。LTF和LTP都被认为在LTM的形成中起重要作用，LTF诱导和LTP诱导表现出相似的机制，如依赖于MAP激酶的激活。因此，一个可以模拟LTF和LTP诱导的建模框架可能会显著增加对学习机制的理解。 LTF模型变体将包括LTF所必需的额外转录调控因子，如ApCREB2和APC/EBP。分叉分析和预编程积分将确定关键的控制参数，这些参数是生理调节的合理位置，并且当变化时，对模型的动力学有重要影响。然后，我们将使用该模型来模拟实验协议的结果，在这些实验协议中，上面列出的转录调控因子的活动发生了变化。将确定关键控制参数的最小变化集，以允许模拟来自这些协议的数据。这种方法可能有助于确定决定不同训练方案诱导的长期转铁量的关键机制。LTP模型变体将被用来模拟三种常见的刺激方案，它们诱导海马区晚期LTP。这些方案是高频(强直)刺激、theta-Burst刺激和Forsklin刺激。参数将被优化，以适应核[Ca2+]以及激酶和转录因子活性的实验时间进程。然后，该模型将被用来检验这样的假设，即CREB蛋白激酶A以外的其他酶，如核糖体$6激酶2，主要负责CREB的磷酸化和LTP的诱导。

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.