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Molecular Signal Processing
Bioorganic Chemistry
Biochemistry of Plant Interactions
Cell and Metabolic Biology
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Publications - Cell and Metabolic Biology
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring substances is an important task to investigate new potentially useful chemical compounds. Multi-dimensional NMR-spectra are relational objects like documents, but consists of continuous multi-dimensional points called peaks instead of words. We develop several mappings from continuous NMR-spectra to discrete text-like data. With the help of those mappings any text retrieval method can be applied. We evaluate the performance of two retrieval methods, namely the standard vector space model and probabilistic latent semantic indexing (PLSI). PLSI learns hidden topics in the data, which is in case of 2D-NMR data interesting in its owns rights. Additionally, we develop and evaluate a simple direct similarity function, which can detect duplicates of NMR-spectra. Our experiments show that the vector space model as well as PLSI, which are both designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI is able to find meaningful ”topics” in the NMR-data.
Books and chapters
Searching and mining nuclear magnetic resonance (NMR)-spectra of naturally occurring products is an important task to investigate new potentially useful chemical compounds. We develop a set-based similarity function, which, however, does not sufficiently capture more abstract aspects of similarity. NMR-spectra are like documents, but consists of continuous multi-dimensional points instead of words. Probabilistic semantic indexing (PLSI) is an retrieval method, which learns hidden topics. We develop several mappings from continuous NMR-spectra to discrete text-like data. The new mappings include redundancies into the discrete data, which proofs helpful for the PLSI-model used afterwards. Our experiments show that PLSI, which is designed for text data created by humans, can effectively handle the mapped NMR-data originating from natural products. Additionally, PLSI combined with the new mappings is able to find meaningful ”topics” in the NMR-data.
Publications
In the recent past, through advances in development of genetic tools, the budding yeast Kluyveromyces lactis has become a model system for studies on molecular physiology of so-called “Nonconventional Yeasts.” The regulation of primary carbon metabolism in K. lactis differs markedly from Saccharomyces cerevisiae and reflects the dominance of respiration over fermentation typical for the majority of yeasts. The absence of aerobic ethanol formation in this class of yeasts represents a major advantage for the “cell factory” concept and large-scale production of heterologous proteins in K. lactis cells is being applied successfully. First insight into the molecular basis for the different regulatory strategies is beginning to emerge from comparative studies on S. cerevisiae and K. lactis. The absence of glucose repression of respiration, a high capacity of respiratory enzymes and a tight regulation of glucose uptake in K. lactis are key factors determining physiological differences to S. cerevisiae. A striking discrepancy exists between the conservation of regulatory factors and the lack of evidence for their functional significance in K. lactis. On the other hand, structurally conserved factors were identified in K. lactis in a new regulatory context. It seems that different physiological responses result from modified interactions of similar molecular modules.
This page was last modified on 27 Jan 2025 .
