(575g) Models for Mammalian Cell Cultures Based on Long-Short Term Memory Recurrent Neural Networks

Mammalian cell cultures have become the favored production hosts for MAbs and therapeutic proteins, since microbial systems are not able to carry out the complex post-translational and functional modifications of these proteins, such as glycosylation. Products of these cell cultures have broad applications in vaccination, drug screening and development, and gene therapy. Monoclonal antibodies (MAbs) are significant reagents used extensively in diagnostic assays, therapeutic applications, and in vivo imaging. Chinese Hamster Ovary (CHO) cells and hybridoma cells, which share similar metabolic characteristics, have been popular cell types for production of MAbs. The efficient performance of these cell cultures requires highly specialized culture media to enhance MAb yield for in vitro production in view of substantial cell death and reduced MAb productivity due to the variations in culture conditions. Although production practices have been employed for decades, cell kinetics is still under investigation to obtain quantitatively as well as qualitatively cost-effective production strategies. Creating these strategies requires understanding of cell metabolism affected by process dynamics in culture environments. Kinetic models empower us to illustrate quantitative cell growth and metabolic activity, which allows prediction of different cell phenotypes and provides better understanding of cell physiology, which is important in optimization of MAb production in animal cell cultures. Kinetic descriptions of mammalian cell cultures are difficult due to these complexities. Development of first principles models (FPMs) is tedious. While FPMs provide qualitative understanding of the cell culture processes, their applications in dynamic operations for prediction, optimization and feedback control of mammalian cell cultures is limited due to their rigid structure. A recent focus has been on using statistical methods for modeling mammalian cell cultures. This approach has limited success due to it being restrictive to particular strains. In this work, we introduce the Recurrent Neural Network Long-Short Term Memory (LSTM) method for modeling mammalian cell cultures. The LSTM method uses neural network algorithms to model and analyze sequential data and is capable of handling large and nonlinear experimental databases. This method has been improved in the past decade, and with the availability of large databases and better computing power, we are able to model and analyze mammalian cell cultures. The models developed here used data generated from in silico experiments involving a large number of process inputs and outputs. The model simulations predict the trends observed in batch and fed-batch mammalian cell cultures for key nutrients glucose and glutamine, viable cell density, target product (monoclonal antibody) titer, and inhibitory metabolites lactate and ammonia with high accuracy. The predictions can be improved by further tuning of the modeling platform. The results of this study illustrate the promise of the LSTM method in other bioprocess applications, including optimization of nutrient media for cell cultivation and synthesis of high value target metabolites. The LSTM method, together with reinforcement learning (RL), can be a backbone in control of bioprocesses. Since RL relies on using LSTM in its implementation, RL has immense potential in optimal process control of stochastic dynamic systems.