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The overwhelming success of the Web as a mechanism for facilitating information retrieval and for conducting business transactions has led to an increase in the deployment of complex enterprise applications. These applications typically run on Web Application Servers, which assume the burden of managing many tasks, such as concurrency, memory management, database access, etc., required by these applications. The performance of an Application Server depends heavily on appropriate configuration. Configuration is a difficult and error-prone task due to the large number of configuration parameters and complex interactions between them. We formulate the problem of finding an optimal configuration for a given application as a black-box optimization problem. We propose a Smart Hill-Climbing algorithm using ideas of importance sampling and Latin Hypercube Sampling (LHS). The algorithm is efficient in both searching and random sampling. It consists of estimating a local function, and then, hill-climbing in the steepest descent direction. The algorithm also learns from past searches and restarts in a smart and selective fashion using the idea of importance sampling. We have carried out extensive experiments with an online brokerage application running in a WebSphere environment. Empirical results demonstrate that our algorithm is more efficient than and superior to traditional heuristic methods.

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BackgroundLignocellulolytic enzymes are the main enzymes to saccharify lignocellulose from renewable plant biomass in the bio-based economy. The production of these enzymes is transcriptionally regulated by multiple transcription factors. We previously engineered Penicillium oxalicum for improved cellulase production via manipulation of three genes in the cellulase expression regulatory network. However, the potential of combinational engineering of multiple regulators and their targets at protein abundance and activity levels has not been fully explored.ResultsHere, we verified that a point mutation XlnRA871V in transcription factor XlnR enhanced the expression of lignocellulolytic enzymes, particularly hemicellulases, in P. oxalicum. Then, overexpression of XlnRA871V with a constitutive PDE_02864 promoter was combined with the overexpression of cellulase transcriptional activator ClrB and deletion of carbon catabolite repressor CreA. The resulted strain RE-7 showed 8.9- and 51.5-fold increased production of cellulase and xylanase relative to the starting strain M12, respectively. Further overexpression of two major cellulase genes cbh1-2 and eg1 enabled an additional 13.0% improvement of cellulase production. In addition, XlnRA871V led to decreased production of β-glucosidase and amylase, which could be attributed to the reduced transcription of corresponding enzyme-encoding genes.ConclusionsThe results illustrated that combinational manipulation of the involved transcription factors and their target genes was a viable strategy for efficient production of lignocellulolytic enzymes in filamentous fungi. The striking negative effect of XlnRA871V mutation on amylase production was also highlighted.Electronic supplementary materialThe online version of this article (doi:10.1186/s13068-017-0783-3) contains supplementary material, which is available to authorized users.

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