Kerstin Wolf

Recombinatorial biosynthesis of polyketides

Modular polyketide synthases (PKSs) from Streptomyces and related genera of bacteria produce many important pharmaceuticals. A program called CompGen was developed to carry out in silico homologous recombination between gene clusters encoding PKSs and determine whether recombinants have cluster architectures compatible with the production of polyketides. The chemical structure of recombinant polyketides was also predicted. In silico recombination was carried out for 47 well-characterised clusters. The predicted recombinants would produce 11,796 different polyketide structures. The molecular weights and average degree of reduction of the chemical structures are dispersed around the parental structures indicating that they are likely to include pharmaceutically interesting compounds. The details of the recombinants and the chemical structures were entered in a database called r-CSDB. The virtual compound library is a useful resource for computer-aided drug design and chemoinformatics strategies for finding pharmaceutically relevant chemical entities. A strategy to construct recombinant Streptomyces strains to produce these polyketides is described and the critical steps of mobilizing large biosynthetic clusters and producing new linear cloning vectors are illustrated by experimental data.

Time-Space Multiscale AnalysisTime-Space Multiscale Analysis Multiscale analysis and Its Application to GRACE and Hydrology Data

We present two concepts of a multiresolution analysis Siehe Multiscale analysis of temporal and spatial variations of the Earth’s gravitational potential. First we apply a separated wavelet analysis using Euclidean wavelets in the time and spherical wavelets in the space domain and, second, we realize a tensor product wavelet analysis using Legendre and spherical wavelets for the time and space domain, respectively. Based on the results of the multiresolution analysis we compute the correlation coefficients between satellite and hydrology data which help us to develop a filter for extracting an improved hydrology model from the satellite data of GRACE (Gravity Recovery and Climate Experiment). Such an extraction is finally realized based on the results of the tensor product analysis.