The CHO VRC01 cell line produces an anti-HIV IgG1 monoclonal antibody containing N-linked glycans on both the Fab (variable) and Fc (constant) regions. Site-specific glycan analysis was used to measure the complex effects of cell culture process conditions on Fab and Fc glycosylation. Experimental data revealed major differences in glycan fractions across the two sites. Bioreactor pH was found to influence fucosylation, galactosylation, and sialylation in the Fab region and galactosylation in the Fc region. To understand the complex effects of process conditions on site-specific N-linked glycosylation, a kinetic model of site-specific N-linked glycosylation was developed. The model parameters provided mechanistic insights into the differences in glycan fractions observed in the Fc and Fab regions. Enzyme activities calculated from the model provided insights into the effect of bioreactor pH on site-specific N-linked glycosylation. Model predictions were experimentally tested by measuring glycosyltransferase-enzyme mRNA-levels and intracellular nucleotide sugar concentrations. The model was used to demonstrate the effect of increasing galactosyltransferase activity on site-specific N-linked glycan fractions. Experiments involving galactose and MnCl 2 supplementation were used to test model predictions. The model is capable of providing insights into experimentally measured data and also of making predictions that can be used to design media supplementation strategies.

Biomass supply chain performance is heavily affected by uncertainties stemming from supply, demand, or unexpected disruptions. Unlike petrochemical plants that use crude oil, biorefineries often have to deal with the uneven spatial-temporal distribution of feedstock supply. The modular production strategy provides more flexibility in chemical manufacturing by allowing fast capacity expansion and unit movement. However, modeling and optimizing modular biomass supply chain under uncertainties becomes challenging due to high-dimensionality and the existence of discrete decisions. This work optimizes the multiperiod biomass supply chain using the rolling horizon planning and two-stage stochastic programming framework. We then applied generalized Benders decomposition to reduce the computational complexity of the stochastic mixed integer nonlinear programming (MINLP) supply chain optimization. Furthermore, the solution of the stochastic programming could be used to quantitatively describe the life-cycle assessment uncertainties of the biomass supply chain performance, demonstrating seasonality and random variability.

In this work, we proposed a two-stage stochastic programming model for a four-echelon supply chain problem considering possible disruptions at the nodes (supplier and facilities) as well as the connecting transportation modes and operational uncertainties in form of uncertain demands. The first stage decisions are supplier choice, capacity levels for manufacturing sites and warehouses, inventory levels, transportation modes selection, and shipment decisions for the certain periods, and the second stage anticipates the cost of meeting future demands subject to the first stage decision. Comparing the solution obtained for the two-stage stochastic model with a multi-period deterministic model shows that the stochastic model makes a better first stage decision to hedge against the future demand. This study demonstrates the managerial viability of the proposed model in decision making for supply chain network in which both disruption and operational uncertainties are accounted for.