Kees Albers


Our genome encodes complex regulatory networks that direct the development of omnipotent
cells into many stable differentiated cell types. However, even the regulatory networks
underlying differentiated cell types remain surprisingly flexible: many differentiated cell
types can be reprogrammed to a pluripotent state, or directly into another differentiated
cell type.

The main aim of my group is to develop quantitative models to predict the functional
consequences of perturbations of gene regulatory networks, from gene expression to complex phenotypes. By integrating various genetic and epigenetic sequencing data sets we try to
understand how the expression of genes is regulated during differentiation and how genetic
variation disrupts this process. In particular mutations in gene regulatory elements
affecting transcription factor binding play a key role in common complex disease.

We are an interdisciplinary group consisting of computational and experimental researchers.
This allows us to generate the data we need to build these models, in addition to using
existing genomics data sets. We make use of two experimental model systems:
1) Trans-differentiation of human skin fibroblasts to neurons by overexpression and
repression of transcription factors;
2) Differentiation of human induced pluripotent stem cells to neurons by forced expression
of transcription factors.
We use methods from the field of machine learning and statistics, such as Bayesian networks,
to construct predictive statistical models. We use high-throughput sequencing techniques
such as single-cell RNA-sequencing, transcription factor profiling and epigenetic profiling
to measure the state of the regulatory network and the consequences of perturbations.

There are both wet-lab and computational projects for bachelor and master students. I am
especially interested to hear from prospective students and postdocs who are interested in
combining wet lab molecular biology experiments with quantitative modeling.


Kees Albers has a Masters degree in physics and obtained his PhD in 2008 from Radboud University on the development of approximate Bayesian inference algorithms and models for analysis of large pedigrees. He then became a research associate at the University of Cambridge and the Wellcome Trust Sanger Institute. As a member of the 1000 Genomes Project he developed a Bayesian approach to detect short insertions and deletions from one of the first large-scale population sequencing data sets. He also analyzed sequencing data to identify the causative genes for a number of rare haematological disorders. In 2012 he started his own group in the department of Human Genetics at the Radboud University Medical Center. From November 2013 he has a joint appointment as assistant professor in the Department of Molecular Developmental Biology at Radboud University.


Marie Curie Career Integration Grant (100 kEuro) 2013
Radboud University Medical Centre Fellowship (800 kEuro) 2012