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Marco J. L. de Groot

Researcher at DSM

Publications -  5
Citations -  176

Marco J. L. de Groot is an academic researcher from DSM. The author has contributed to research in topics: Gene & Complementation. The author has an hindex of 5, co-authored 5 publications receiving 173 citations. Previous affiliations of Marco J. L. de Groot include Wageningen University and Research Centre & Delft University of Technology.

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Journal ArticleDOI

The Aspergillus niger D-xylulose kinase gene is co-expressed with genes encoding arabinan degrading enzymes, and is essential for growth on D-xylose and L-arabinose.

TL;DR: Overproduction of XKIA did not reduce the size of the intracellular arabitol and xylitol pools, and therefore had no effect on expression of genes encoding xylan and arabinan degrading enzymes nor on the activity of the enzymes of the catabolic pathway, which supports the suggestion that l-arabitol may be the specific low molecular mass inducer of the genes involved inArabinan degradation.
Journal ArticleDOI

Isolation and characterization of two specific regulatory Aspergillus niger mutants shows antagonistic regulation of arabinan and xylan metabolism.

TL;DR: It is proposed that the araA and araB mutations are localized in positive-acting components of the regulatory system involved in the expression of the arabinanase-encoding genes and the genes encoding the L-arabinose catabolic pathway.
Journal ArticleDOI

Metabolic pathway alignment between species using a comprehensive and flexible similarity measure

TL;DR: This work systematically search for conserved pathways in two species, quantify their similarities, and focus on the variations between them, and presents an efficient framework, Metabolic Pathway Alignment and Scoring (M-PAS), for identifying and ranking conserved metabolic pathways.
Journal ArticleDOI

Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels

TL;DR: A novel algorithm is proposed that significantly reduces computation time for finding large MCSs, compared to a number of state-of-the-art approaches, and is demonstrated in an application predicting the transcriptional response of breast cancer cell lines to different drug-like compounds.
Proceedings ArticleDOI

Metabolic Pathway Alignment (M-Pal) Reveals Diversity and Alternatives in Conserved Networks.

TL;DR: A comparative analysis of metabolic reaction networks between different species is introduced and a clear framework for matching metabolic pathways is presented, and a scoring scheme which combines enzyme functional similarity with protein sequence similarity is proposed.