Development of a computational model of synaptome architecture.

The brain is the most complex organ in biology and its complexity comes from its many layers of organisation Regions within the brain are specialised to serve different functions (e.g some regions handle vision while other underpin cognition and learning and others motor control). Each of these regions comprises neurons which connect to one another forming neuronal networks or circuits. At yet another level each of these neurons connects to its partners using highly specialised and complex molecular complexes known as synapses. The components of these synapses are highly dynamic through the lifespan and are region specific. However, our understanding of how brains work at this chemical level and how these age and region specific changes impact on brain function is very poorly understood. The long term impact of this understanding is vitally important to society as a whole. We need to understand these changes so we can develop new methods to treat the brain when it deteriorates with age, with disease or in response to trauma.

Our project will develop new ways to bring together the information about these chemical synapses to give us a new understanding of this critical level. The data to start addressing these fundamental questions exists in part but is widely scattered and the methods for integrating these data together are in their infancy. This research will address both issues seeking to release a computational model of how these synapses can be predicted from the data available. We will release a public database for other researchers in academia and in the pharmaceutical industry so impact of what we do can be maximised. We will also validate our work by testing a selection of our predictions in the aging brain to confirm how well our computational models and algorithms perform.

While our project is based on understanding the fundamental principles it is also of critical importance in terms of long-term health: These chemical synapses are the site at which drugs act but our understanding of how they vary need to be improved to unlock a new generation of drugs can be targeted to keep the brain healthy.

The molecular composition of neuronal synapses changes dramatically with neuron type, developmental stage, and age. The composition of the synapses within the same neuron even varies along the length of the dendrite. Understanding this is critical as the functional connections between neurons are determined by this molecular architecture.

We will curate and integrate neuronal data for the spatially resolved transcriptome, translatome and degradome. We will release a synapse proteome database that includes all these relevant data (or links to stable online resources) providing cell type and subcellular resolution for available synaptic proteome data.

We will develop a computational model that estimates the most likely abundance level for synaptic proteins in neurons given their spatial gene expression profile, translatome (local translation efficiency) and protein turnover for specific cell types. We first try an ordinary differential equation (ODE) model. Compartmental models for the neuron class will reproduce the neuronal architecture to reflect how the synapses in a cell type vary along the neural processes. To constrain the problem and ensure feasibility we will initially fit the model on core synaptic proteins for which we have access to synapse resolution abundance data in mouse CA1, CA2 and CA3 cells and, on proteins that are indicative markers for synapse function.

We will tune the parameters of the model using data for the core proteins in one cell type (e.g. CA1) and test the model against another cell type (e.g. CA2/CA3). Through the estimated abundances of key receptor subunits, we expect that we will be able to predict major classes of synapse types (inhibitory/excitatory) and how those vary both within the cell and across cell types. We will also use the model to predict synaptome / synaptic type with cell type, brain region and age.