An extension of our physics-informed deep learning algorithm using systems of ordinary differential equations (ODEs) has been recently submitted to bioRxiv. In this systems-biology-informed framework (found here), the system of ODEs is encoded into the loss function, which enables us to use few scattered and noisy measurements to infer the dynamics of unobserved species, systematic forcing and the unknown model parameters.