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Alloimmunization to donor blood group antigens remains a significant problem in transfusion medicine. A proposed method to overcome donor-recipient blood group incompatibility is to mask the blood group antigens by the covalent attachment of poly(ethylene glycol) (PEG) to the red blood cell (RBC) membrane. Despite much work in the development of PEG-coating of RBCs, there is a paucity of data on the optimization of the PEG-coating technique; it is the aim of this study to determine the optimum conditions for PEG coating using a cyanuric chloride reactive derivative of methoxy-PEG as a model polymer. Activated PEG of molecular mass 5 kDa was covalently attached to human RBCs under various reaction conditions. Inhibition of binding of a blood-type specific antiserum (anti-D) was employed to evaluate the effect of the PEG-coating, quantified by hemocytometry and flow-cytometry. RBC morphology was examined by light and scanning electron microscopy. Statistical analysis of experimental design together with microscopy results showed that the optimum PEGylation conditions are pH = 8.7, temperature = 14 degrees C, and reaction time = 30 min. An optimum concentration of reactive PEG could not be determined. At high polymer concentrations (>25 mg/mL) a predominance of type III echinocytes was observed, and as a result, a concentration of 15 mg/mL is the highest recommended concentration for a linear PEG of molecular mass 5 kDa.

Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.