Pseudomonas aeruginosa amidase: Aggregation in recombinant Escherichia coli

The effect of cultivation parameters such as temperature incubation, IPTG induction and ethanol shock on the production of Pseudomonasaeruginosa amidase (E.C.3.5.1.4) in a recombinant Escherichia coli strain in LB ampicillin culture medium was investigated. The highest yield of solubleamidase, relatively to other proteins, was obtained in the condition at 37 degrees C using 0.40 mM IPTG to induce growth, with ethanol. Our results demonstrate the formation of insoluble aggregates containing amidase, which was biologically active, in all tested growth conditions. Addition of ethanol at 25 degrees C in the culture medium improved amidase yield, which quantitatively aggregated in a biologically active form and exhibited in all conditions an increased specific activity relatively to the soluble form of the enzyme. Non-denaturing solubilization of the aggregated amidase was successfully achieved using L-arginine. The aggregates obtained from conditions at 37 degrees C by Furier transform infrared spectroscopy (FTIR) analysis demonstrated a lower content of intermolecular interactions, which facilitated the solubilization step applying non-denaturing conditions. The higher interactions exhibited in aggregates obtained at suboptimal conditions compromised the solubilization yield. This work provides an approach for the characterization and solubilization of novel reported biologically active aggregates of this amidase.

Symbolic Knowledge Extraction from Trained Neural Networks Governed by Lukasiewicz Logics

This work describes a methodology to extract symbolic rules from trained neural networks. In our approach, patterns on the network are codified using formulas on a Lukasiewicz logic. For this we take advantage of the fact that every connective in this multi-valued logic can be evaluated by a neuron in an artificial network having, by activation function the identity truncated to zero and one. This fact simplifies symbolic rule extraction and allows the easy injection of formulas into a network architecture. We trained this type of neural network using a back-propagation algorithm based on Levenderg-Marquardt algorithm, where in each learning iteration, we restricted the knowledge dissemination in the network structure. This makes the descriptive power of produced neural networks similar to the descriptive power of Lukasiewicz logic language, minimizing the information loss on the translation between connectionist and symbolic structures. To avoid redundance on the generated network, the method simplifies them in a pruning phase, using the "Optimal Brain Surgeon" algorithm. We tested this method on the task of finding the formula used on the generation of a given truth table. For real data tests, we selected the Mushrooms data set, available on the UCI Machine Learning Repository.