This project investigates uses computational and mathematical modelling to test if gene expression noise can explain brain synapses fluctuations over time.
Memories can last a lifetime, yet recent research shows that individual synapses, where memory-related information is stored, fluctuate in size within hours or days. These fluctuations are much faster than the years-long stability of long-term memories. This surprising discovery challenges traditional theories and raises a fundamental question: how do we maintain stable memories when our underlying synapses are so unstable?
In this project, we are investigating a novel explanation for these fluctuations: gene expression noise. All cells, including neurons, rely on turning genes on and off to produce proteins necessary for their function. However, because this process occurs at the molecular level, it is inherently variable, leading to 'noise' in protein production that may drive synapse size changes. Gene expression noise has been well-studied in simpler cells like bacteria, but we will explore, for the first time, whether it can explain synaptic instability in neurons.
Our approach includes mathematical modelling, computer simulations, and analysis of existing datasets. We will adapt stochastic models of gene expression to the unique structure of neurons, testing whether gene expression noise can plausibly account for observed synapse fluctuations. By identifying the key factors influencing fluctuation size and duration, we will generate specific predictions, which we will then compare against real-world data provided by our collaborators.
If successful, this project will not only enhance our understanding of synaptic fluctuations but also offer new insights into how the brain can store long-term memories despite inherent cellular instability, potentially opening up new directions for neuroscience research.