Synaptic strength instability from stochastic gene expression in neurons

记忆可以持续一生。几十年来，神经科学家一直认为，我们长期记忆的稳定性是由于存储信息的底层大脑回路的稳定性。在过去的10-15年里，这一情况发生了巨大的变化，当时实验神经科学家使用尖端的显微镜和分子生物学方法来跟踪突触的大小-神经元之间的连接被认为是存储信息-在动物大脑中纵向数天和数周。事实证明，单个突触的大小在小时-天的时间尺度上不断波动，比长期记忆的年时间尺度快得多。这种矛盾对我们理解记忆提出了深刻的挑战，尽管理论神经科学家对如何解决它有一些想法，但它基本上仍然是一个谜。第二个混乱的来源是波动的起源。除了普遍认为“生物学是嘈杂的”之外，还不清楚是什么驱动了突触大小的自发变化。了解它们的来源将是重要的，原因有三：1）它将告诉我们哪些细胞特性调节波动，哪些不调节; 2）它将为更高层次的记忆理论模型提供约束; 3）它可能为波动的潜在有益作用提供线索。在这个项目中，我们将使用数学建模，计算机模拟和数据分析来建立和测试一个新的突触波动理论，该理论基于一个假设，即突触波动来自基因表达噪声。所有细胞都通过基因的开启和关闭来实现细胞功能，神经元也不例外。当一个细胞打开一个基因时，它会触发一个生化信号级联反应，导致更多相应的蛋白质被制造出来。然而，因为这个过程发生在单分子随机扩散的水平上，所以它有点不可靠，并且制造的蛋白质的量以部分不受控制的方式随时变化。这种“基因表达噪音”在细菌、酵母和一些哺乳动物神经元等简单细胞中得到了大量研究。在这里，我们将第一次问，它是否可以合理地解释神经元中突触大小的波动。我们将调整现有的随机基因表达的数学模型，为简单的细胞，并适应他们的神经元。神经元与大多数细胞不同，因为它们具有延伸的树状形状，这使数学分析变得复杂，但可能会引入一些反直觉的效果。首先，我们将分析这些“简单”的数学模型，以深入了解控制波动大小和时间尺度的关键因素。然后，我们将对添加更多生物学细节的更复杂版本的模型进行详细的计算机模拟。这将产生难以量化的预测。最后，我们将用国际合作者提供的三个先前记录的数据集来测试模型的预测。如果成功，这个跨学科项目将为突触波动的研究开辟新的途径，并为解决大脑如何在不稳定的成分下储存稳定的长期记忆的难题提供线索。

Memories can last a lifetime. For many decades, neuroscientists assumed that the stability of our long-term memories was due to stability in the underlying brain circuitry where the information is stored. This picture has changed dramatically over the past 10-15 years, when experimental neuroscientists used cutting edge microscopy and molecular biology methods to track the sizes of synapses - the connections between neurons where information is thought to be stored - longitudinally in the brains of animals over days and weeks. It turned out that individual synapses continually fluctuate in size on a timescale of hours-days, much faster than the years timescale of long-term memories. This contradiction poses a deep challenge for our understanding of memory, and although theoretical neuroscientists have some ideas for how it may be resolved, it basically remains a mystery.A second source of confusion is the origin of the fluctuations. Beyond a general belief that "biology is noisy", it is not clear what is driving these spontaneous changes in synapse size. An understanding of their sources will be important, for three reasons: 1) it will tell us which cellular properties regulate the fluctuations and which don't; 2) it will give constraints for higher level theoretical models of memory; 3) it may give clues into potential beneficial roles for the fluctuations. In this project we will use mathematical modelling, computer simulations, and data analysis to build and test a new theory of synaptic fluctuations, based on the hypothesis that they arise from gene expression noise.All cells turn genes on and off to implement cellular functions, and neurons are no exception. When a cell turns a gene 'on', it triggers a biochemical signalling cascade that results in more of its corresponding protein being manufactured. However because this process happens at the level of single molecules diffusing randomly, it is somewhat unreliable, and the amount of protein that gets manufactured varies from moment to moment in a partially uncontrolled way. This 'gene expression noise' is heavily studied in simple cells like bacteria, yeast and some mammalian neurons. Here we will ask, for the first time, if it can plausibly explain synapse size fluctuations in neurons. We will adapt existing mathematical models of stochastic gene expression developed for simple cells, and adapt them for neurons. Neurons are unlike most cells because of their extended tree-shapes, which complicates the mathematical analysis somewhat but can introduce some counter-intuitive effects. First we will analyse these 'simple' mathematical models to get an insight into the key components controlling fluctuation size and timescale. Then we will run detailed computer simulations of more complicated versions of the models with more biological details added. These will produce hard quantitative predictions. Finally, we will test the model's predictions against three previously recorded datasets, provided by our international collaborators.If successful, this interdisciplinary project will open up new avenues of research on synaptic fluctuations, and give clues for solving the puzzle of how brains can store stable long-term memories despite their unstable components.