The speed at which new drugs and therapies have been developed in the last years has put immense pressure in the biopharmaceutical industry to constantly rethink and improve its manufacturing processes for a large variety of molecules in order to be able to meet market demand. To face this growing challenge, companies need to fully embrace the principles of Industry 4.0, and take advantage of these new tools to increase process efficiency. Bioreactor Digital twins are examples of such tools that use mathematical models to replicate the bioreactor environment virtually. The virtual reactor can be used to dynamically and autonomously assess and deploy new control strategies to improve cell culture in real time, while allowing for new insights into the cell culture mechanisms. As such, the development of a working digital twin is contingent on development of an accurate mathematical model that adequately captures the complex functional relationships underlying biological systems. This study develops a novel genome-scale hybrid modeling methodology with application to a HEK293 cell line. Specifically, a hybrid modeling approach is developed that blends deep neural networks with a genome scale model (hybrid GEM) to fully describe intracellular processes as function of measurable and/or manipulable process parameters. The hybrid GEM uses state-of-the-art deep learning algorithms such as adaptive moment estimation (Adam). The HEK293 hybrid GEM is trained with a relatively low amount of experiments, matching the performance of deep learning neural networks. The results show that the trained hybrid GEM is able to make accurate predictions of the metabolic fluxes and cellular growth rate from changes in the concentrations of biochemical species in the culture media. The establishment of a novel, functional, hybrid deep metabolic model opens the door for the implementation of cell line digital twins in the biopharma industry. This study is a step forward to the realization of “Biopharma 4.0”, with potential impact in process efficiency, robustness and transparency.