Marcel Reinders
Marcel Reinders
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- +31 15 27 86424
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- m.j.t.reinders@tudelft.nl
Faculty of Electrical Engineering, Mathematics and Computer Science
Intelligent Systems
Mekelweg 4
2628 CD Delft
The Netherlands
Office: HB 13.070
Intelligent Systems
Mekelweg 4
2628 CD Delft
The Netherlands
Office: HB 13.070
Marcel J.T. Reinders received his MSc degree in Applied Physics and a PhD degree in Elec¬trical Engineering from Delft University of Technology, The Netherlands, in 1990 and 1995, respectively. In 2005, he became a Professor in Bioinformatics within the Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology in which he now heads the ‘Pattern Recognition and Bioinformatics’ section. In 2010 he became one of the scientific directors of the Netherlands Bioinformatics Centre (NBIC). The background of Marcel Reinders is within pattern recognition. Besides studying fundamental issues, he applies pattern recognition techniques to the areas of bioinformatics, computer vision and context-aware recommender systems. His special interest goes towards understanding complex systems (such as biological systems) that are severely under-sampled. He (co-)authored more than 200 scientific papers of which more than 75 in peer-reviewed journals.
Research Highlights
DAGIC: Detecting Aberrant Genes in Interaction Context
Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel framework and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, we identify densely connected components of known and putatively novel cancer genes and show that for different scale levels different functional enrichments and mutual exclusion patterns are observed. Taken together, this demonstrates the importance of analyzing gene mutation in the context of their interaction network in a multi-scale fashion. The source code of this work, DAGIC, for Matlab and R is available on http://bioinformatics.tudelft.nl/users/sepideh-babaei.People involved
Sepideh Babaei, Jeroen de Ridder, Marcel ReindersThe genome sequence of a yeast for modern industrial biotechnology
Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.Related publications
- De novo sequencing, assembly and analysis of the genome of the laboratory strain Saccharomyces cerevisiae CEN.PK113-7D, a model for modern industrial biotechnology
- Integrating genome assemblies with MAIA
People involved
Jurgen Nijkamp, Dick de Ridder, Marcel van den Broek, Marcel ReindersMateriomics
It is increasingly recognized that material surface topography is able to evoke specific cellular responses, endowing materials with instructive properties that were formerly reserved for growth factors. This opens the window to improve upon, in a cost-effective manner, biological performance of any surface used in the human
body. Unfortunately, the interplay between surface topographies and cell behavior is complex and still incompletely understood. Rational approaches to search for bioactive surfaces will therefore omit previously unperceived interactions. Hence, in this projects, 2176 mathematically designed surface topologies were placed on chips of poly(lactic acid). Human mesenchymal stromal cells (hMSCs) were grown on the chips, and using high-content imaging and an analysis pipeline, surface effects on MSC proliferation and osteogenic differentiation were found. The analysis pipeline uses image processing and machine learning techniques to process raw chip images into results, such as best performing surfaces, surface parameters that play an important role in the tested biological process, and cell response predictors for new surfaces. Using robust statistics, quality measures and by exploiting surface similarity, we are able to significantly improve surface ranking consistency.Related publications
People involved
Marc Hulsman, Marcel ReindersPredicting protein secretion success
The cell-factory Aspergillus niger is widely used for industrial enzyme production. Selecting enzymes for large-scale production requires costly lab work to test for successful high-level secretion of the over-expressed enzyme. To reduce the amount of lab work, we developed a sequence-based classifier that predicts successful high-level secretion of homologous proteins. This enables the selection of a subset of potential enzymes out of a large set of enzymes.
A dataset of 638 proteins was used to train and validate a classifier, using a 10-fold cross-validation protocol. Using a linear discriminant classifier, an average accuracy of 0.85 was achieved, which in practice could lead to half the amount of lab work.
Feature selection results indicate what features are mostly defining for successful protein production,
which could be an interesting lead to couple sequence characteristics to biological processes involved in protein production and secretion.Related publications
People involved
Bastiaan v.d. Berg, Jurgen Nijkamp, Marcel Reinders, Dick de RidderMAIA: Integrating genome assemblies
De novo assembly of a eukaryotic genome with next-generation sequencing data is still a challenging task. Over the past few years several assemblers have been developed, often suitable for one specific type of sequencing data. The number of known genomes is expanding rapidly, therefore it becomes possible to use multiple reference genomes for assembly projects. We introduce an assembly integrator that makes use of all available data, i.e. multiple de novo assemblies and mappings against multiple related genomes, by optimizing a weighted combination of criteria.
The developed algorithm was applied on the de novo sequencing of the Saccharomyces cerevisiae CEN.PK 113-7D strain. Using Solexa and 454 read data, two de novo and three comparative assemblies were constructed and subsequently integrated, yielding 29 contigs, covering more than 12 Mbp; a drastic improvement compared with the single assemblies.