Improved activity and thermostability of Candida antarctica lipase B by DNA family shuffling

DNA family shuffling was used to create chimeric lipase B proteins with improved activity toward the hydrolysis of diethyl 3-(3',4'-dichlorophenyl)glutarate (DDG). Three homologous lipases from Candida antarctica ATCC 32657, Hyphozyma sp. CBS 648.91 and Crytococcus tsukubaensis ATCC 24555 were cloned and shuffled to generate a diverse gene library. A high-throughput screening assay was developed and used successfully to identify chimeric lipase B proteins having a 20-fold higher activity toward DDG than lipase B from C.antarctica ATCC 32657 and a 13-fold higher activity than the most active parent derived from C.tsukubaensis ATCC 24555. In addition, the stability characteristics of several highly active chimeric proteins were also improved as a result of family shuffling. For example, the half-life at 45 degrees C and melting point (T(m)) of one chimera exceeded those of lipase B from C.antarctica ATCC 32657 by 11-fold and 6.4 degrees C, respectively, which closely approached the stability characteristics of the most thermostable parent derived from Hyphozyma sp. CBS 648.91.     

Efficient elections in chordal ring networks

We study the message complexity of the problem of distributively electing a leader in chordal rings. Such networks consist of a basic ring with additional links, the extreme cases being the oriented ring and the complete graph with a full sense of direction. We present a general election algorithm for these networks, and prove its optimality. As a corollary, we show thatO(logn) chords at each processor suffice to obtain an algorithm that uses at mostO(n) messages; this improves and extends a previous work, where an algorithm, also usingO(n) messages, was suggested for the case where alln-1 chords exist (the oriented complete network).     

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.     

The layout of virtual paths in ATM networks

We study the problem of designing a layout of virtual paths (VPs) on a given ATM network. We first define a mathematical model that captures the characteristics of virtual paths. In this model, we define the general VP layout problem, and a more restricted case; while the general case layout should cater connections between any pair of nodes in the network, the restricted case layout should only cater connections between a specific node to the other nodes. For the latter case, we present an algorithm that finds a layout by decomposing the network into subnetworks and operating on each subnetwork, recursively; we prove an upper bound on the optimality of the resulting layout and a matching lower bound for the problem, that are tight under certain realistic assumptions. Finally, we show how the solution for the restricted case is used as a building block in various solutions to more general cases (trees, meshes, K-separable networks, and general topology networks) and prove a lower bound for some of our results. The results exhibit a tradeoff between the efficiency of the call setup and both the utilization of the VP routing tables and the overhead during recovery from link disconnections