This model was built and optimized to reproduce the variability inherent to many industrial cell-culture processes. Classically, fed-batch Chinese Hamster Ovary (CHO) cell cultures will initially produce lactate in the early phase of culture before switching to lactate consumption. However, some processes may revert to lactate production in the late stage of culture, driving up osmolarity while reducing viable cell density, and ultimately lowering process performance. This phenomenon may occur in only some runs of a manufacturing processes and even may differ among runs with similar initial conditions and trajectories, leading to longstanding questions about the mechanisms driving this switch. By simulating cultures which were exposed to different amounts of stress before the production bioreactor we show that similar starting conditions in the bioreactor environment can lead to variability in metabolic shift. We provide this model as a tool to demonstrate this metabolic variability and provide a platform for hypothesis testing, in silico bioprocess optimization, and simulation of reactor scale-up and scale-down.

Chinese hamster Ovary (CHO) cell lines are the dominant industrial workhorses for therapeutic recombinant protein production. The availability of the genome sequence of Chinese hamster and CHO cells will spur further genome and RNA sequencing of producing cell lines. However, the mammalian genomes assembled using shot-gun sequencing data still contain regions of uncertain quality due to assembly errors. Identifying high confidence regions in the assembled genome will facilitate its use for cell engineering and genome engineering. This dataset includes two genome annotation files that identify the 'high confidence regions' shared by the genome assemblies in comparison. The potential use of these files are to find locations in the publically available genome which are likely to be assembled correctly. These regions can be used confidently for genome engineering.