Systems biology informed deep learning for inferring parameters and hidden dynamics - Yazdani, Alireza; Lu, Lu; Raissi, Maziar; Karniadakis, George Em (PLOS Computational Biology, 2020)
Author summary The dynamics of systems biological processes are usually modeled using ordinary differential equations (ODEs), which introduce various unknown parameters that need to be estimated efficiently from noisy measurements of concentration for a few species only. In this work, we present a new “systems-informed neural network” to infer the dynamics of experimentally unobserved species as well as the unknown parameters in the system of equations. By incorporating the system of ODEs into the neural networks, we effectively add constraints to the optimization algorithm, which makes the method robust to noisy and sparse measurements.