This project is developing a computational and mathematical model of amyotrophic lateral sclerosis (ALS). This is the first multi-factorial in silico network model that fully recapitulates the ALS pathophysiology. Our model framework enables high throughput testing of combination treatments using mechanistic modulatory feedback analysis and stability as a criterion for successful treatment. Dynamic meta-analysis (DMA) is a method that provides temporal, dynamic, and biological relationships by using ordinary differential equations to account for changes with time. A system of ordinary differential equations can be constructed to model and describe the time-dependent behavior of seven highly interrelated pathophysiological categorical disturbances that have been proven to influence ALS disease onset and progression. Using this, our treatments can provide critical insight for experimental development and testing of translational ALS treatments and etiological hypotheses.