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  • 1
    ISSN: 1432-0983
    Keywords: Key wordsPhanerochaete chrysosporium ; Differential display ; Cellulose-binding domain ; Differential gene expression
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract Cellulose-binding domains (CBDs) are present in the majority of fungal cellulases studied to-date. This work describes the use of targeted differential display, employing degenerate primers designed to anneal to variants of a region conserved in fungal CBDs, each in combination with an oligo-dT primer, to PCR-amplify cDNA sequences containing regions coding for such domains from Phanerochaete chrysosporium. After growth on either Avicel or carboxymethyl cellulose (CMC), five distinct, abundantly expressed cDNA sequences were obtained. Two of these originated from transcripts of the previously characterised cbhI.1 and cbhI.2 genes, whereas three were from novel genes. One of the latter was isolated only after growth on CMC. No such sequences were obtained after growth on xylan, suggesting that the expression of sequences containing such regions is down-regulated on this substrate. The use of targeted differential display both for isolating novel sequences and for studying the expression of known genes within a family is discussed.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Neural computing & applications 6 (1997), S. 229-237 
    ISSN: 1433-3058
    Keywords: Genetic algorithms ; Mixed programming ; Simulation optimisation ; Simulation modelling ; Steelworks operation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A steelworks model is selected as representative of the stochastic and unpredictable behaviour of a complex discrete event simulation model. The steel-works has a number of different entity or object types. Using the number of each entity type as parameters, it is possible to find better and worse combinations of parameters for various management objectives. A simple real-coded genetic algorithm is presented that optimises the parameters, demonstrating the versatility that genetic algorithms offer in solving hard inverse problems.
    Type of Medium: Electronic Resource
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