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Chinese hamster ovary (CHO) cells are used for industrial production of protein-based therapeutics (i.e., "biologics"). Here we describe a method for combining systems-level kinetic models with a synthetic biology platform for multigene overexpression to rationally perturb N-linked glycosylation. Specifically, we sought to increase galactose incorporation on a secreted Immunoglobulin G (IgG) protein. We rationally design, build, and test a total of 23 transgenic cell pools that express single or three-gene glycoengineering cassettes comprising a total of 100 kilobases of engineered DNA sequence. Through iterative engineering and model refinement, we rationally increase the fraction of bigalactosylated glycans five-fold from 11.9% to 61.9% and simultaneously decrease the glycan heterogeneity on the secreted IgG. Our approach allows for rapid hypothesis testing and identification of synergistic behavior from genetic perturbations by bridging systems and synthetic biology.

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Mucin-type O-glycans have profound effects on the structure and stability of glycoproteins. O-Glycans on the cell surface proteins also modulate the cell's interactions with the surrounding environments and other cells. The synthetic pathway of O-glycans involves a large number of enzymes with diverse substrate specificity. The expression pattern of these enzymes is cell and tissue-specific, thus making the pathway highly diverse. To facilitate pathway analysis in a cell and tissuespecific fashion, we developed an integrated platform of RING (Rule Input Network Generator) and O-GlycoVis. RING uses an English-like reaction language to describe the substrate specificity of enzymes and additional constraints on the formation of the glycan products. Using this information, the RING generates a list of possible glycans, which is used as input into O-Glycovis. O-GlycoVis displays the glycan distribution in the pathway and potential reaction paths leading to each glycan. With the input glycan data, O-GlycoVis also traces all possible reaction paths leading to each glycan and outputs pathway maps with the relative abundance levels of glycans overlaid. O-Glycan profiles from two breast cancer cell lines, MCF7 and T47d, human umbilical vascular endothelium cells, Chinese Hamster Ovary cells were generated based on transcriptional data and compared with experimentally observed O-glycans. This RING-based program allows rules to be added or subtracted for network generation and visualization of networks of O-glycosylation network of different tissues and species.