The effect of ph and gas composition on the bubble fractionation of proteins

Studies were conducted to establish the effect of the variation of environmental factors on the separation occurring in protein systems, resulting from bubble fractionation in a bioreactor. The measure of separation was selected to be the separation ratio. This is defined to be the ratio of either the top or the middle position concentration in the vessel to the bottom concentration of the vessel. Invertase and α-amylase were the two “model” enzymes considered. It was observed that, under certain conditions, i.e., a combination of the nature of the sparging gas and the medium pH, varying degrees of protein separation were achieved. The pH of the system dramatically influenced the separation. It was found that the best separation occurred at a certain pH, assumed to be at or close to thepI of the protein in question. Furthermore, it was observed that systems sparged with CO₂ exhibited greater separation than systems sparged with air. In fact, in the case of invertase, almost threefold separation was observed at the top port when the solution was sparged with CO₂.     

Measuring agility of networked organizational structures via network entropy and mutual information

While the agility of networked organizational structures is important for organizational performance, studies on how to evaluate it remain scant, probably because the difficulty in measuring network evolution. In this conceptual paper, we propose two measures - network entropy and mutual information - to characterize the agility of networked organizational structure. Rooted in graph theory and information theory, these two measures capture network evolution in a comprehensive and parsimonious way. They indicate the uncertainty (or disorder) at the network level as well as the degree distribution at the individual level. We also propose an algorithm for applying them in the scenario of adding links to a network while holding the number of nodes fixed. Both simulated and real networks are used for demonstration. Implications and areas for future research are discussed in the end.