Research

BIOMATH

  • Advanced modelling using kinetic models, computational fluid dynamics (CFD) and population balance models (PBM) as well as combinations
  • Model calibration, validation and optimal experimental design; sensitivity and uncertainty analysis
  • Model reduction using knowledge gained for more complex models (e.g. compartmental modelling)
  • Application domains: resource recovery, sustainable pharmaceutical engineering

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KERMIT

KERMIT operates along strongly interlaced research lines and serves as an attraction pole for applications in the biological sciences:

  • knowledge-based modelling (fuzzy systems, preference modelling and decision making, aggregation procedures, uncertainty modelling)
  • data-driven modelling (machine learning methods for complex prediction and automation tasks)
  • spatially explicit modelling (cellular automata, coupled-map lattices and individual-based models)

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BIOSTAT

The members of the BIOSTAT unit share a common interest in the theory and methods of statistical data analysis and their applications in life sciences. Research of the unit encompasses: 

  • The development of statistical methods to study complex data acquired from biological processes. Recent research topics include the study of extremes and anomalies, spatial data and sampling problems. 
  • The practice of statistics in the field of bioscience engineering. The research unit studies applications within the current trends of industry 4.0 (where multiple sensors are being used to monitor the quality of industrial products), artificial intelligence (where software is being developed to make machines smart) and citizen science (where volunteers can contribute to scientific research) among others. 

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BIOBIX

The BIOBIX lab of Bioinformatics and Computational Genomics focuses on the analysis of high-throughput “omics” data, particularly generated by massively parallel sequencing, mass spectrometry and microarrays, with the following main topics:

  • Development of innovative data-analytical methodologies to solve complex biological questions, related to imprinting, CpG mutability, epitranscriptomics, telomere biology and micropeptide biology.
  • Applied (epi)genomics, transcriptomics and proteomics research for cancer and aging studies, but also with applications in e.g. neuronal development, crop protection, virus discovery and ecology.

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Most recent publications

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