High energy-charged cell factory for heterologous protein synthesis

Overexpression of gluconeogenic phosphoenolpyruvate carboxykinase (PCK) under glycolytic conditions enables Escherichia coli to maintain a greater intracellular ATP concentration and, consequently, to up-regulate genes for amino acid and nucleotide biosynthesis. To investigate the effect of a high intracellular ATP concentration on heterologous protein synthesis, we studied the expression of a foreign gene product, enhanced green fluorescence protein (eGFP), under control of the T7 promoter in E. coli BL21(DE3) strain overexpressing PCK. This strain was able to maintain twice as much intracellular ATP and to express two times more foreign protein than the control strain. These results indicate that a high energy-charged cell can be beneficial as a protein-synthesizing cell factory. The potential uses of such a cell factory are discussed

Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.Comment: Appearing in CVPR-2016 (oral presentation