Cells, as complex systems, consist of diverse interacting biomolecules arranged in dynamic hierarchical modules. Recent advances in deep learning methods now allow one to encode this rich existing knowledge in the architecture of the learning procedure, thus providing the models with the knowledge that is absent in the training data. By encoding biological networks in the architecture, one can develop flexible deep models that propagate information through the molecular networks to successfully classify cell states. Moreover, this flexibility in the architecture can be harnessed to model the hierarchical structure of real biological systems, efficiently converting gene-level data to pathway-level information with an ultimate impact on cell phenotype. Furthermore, such models could require fewer training samples, are more generalizable across diverse biological contexts, and can make predictions that are more consistent with the current understanding on the inner-working of biological systems.