Neurological diseases are complex network-level processes where many aspects of subcellular function undergo depletion, stress, and sometimes catastrophic decline. Conventional single-molecule studies have had limited success in uncovering these highly interconnected events. Our study utilizes network-level modeling with data from mass spectrometry and targeted data from proximity ligation assay to study processes in various synapses of neurological disorders. We aim to measure synaptic signaling across multiple readouts, stimuli, and genetic/aging contexts and use this to build an atlas and models of the synaptic function of young, healthy aging, and diseased-aging synapses.Our project will use three model systems: 1) in vivo normal and Familial Alzheimer’s Disease mice at 6, 12 and 18 months to model normal and dementia-related aging, in vitro neuronal lines including human IPSCs from controls and patients with early-onset aging pathologies; and in-silico models of synaptic signaling, 2) two experimental approaches: phospho-proteomics for unbiased measurements of stimulus-triggered signaling events; and high-throughput Proximity Ligation Assays to monitor specific molecular interactions. To enrich synaptic/dendritic signaling we will employ synaptoneurosomes and synapse-specific imaging and 3) in-silico data analysis, mechanistic signaling models, and machine-learning models to put these data together. The models and datasets will serve as a major resource or atlas for the functioning and mechanisms in aging, collectively called AgeSim for Aging Simulations. As a corollary outcome, comparisons of our in-vivo aging readouts with our in-vitro data will let us predict which cell culture readouts might be used as proxies for aging.