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I develop multi-scale computational models to unravel pattern formation during development. I focus on the growth and patterning of the major body axis in animals and plants. A major aim is to find general evolutionary and developmental design principles.

My main scientific interest is in utilizing "omics" data and especially genomes to understand function and evolution of complex biological systems.

Our lab develops new data science and bioinformatics methods to analyse data from quantitative (epi)genomics technologies and applies them to understand cell fate decisions.

Through multidisciplinary work I attempt to improve our understanding of the functioning of the immune system in a quantitative manner. We use mathematical modeling and bioinformatics to find mechanistic and quantitative interpretations of the typically complex data obtained in the laboratories of close collaborators.

We study the immune systems of primates and their co-evolution with pathogens by a comparative genetics approach, focussing on the genes of the Major Histocompatibility Complex (MHC) and Killer Cell Immunoglobulin-like receptors (KIR). The MHC is associated with susceptibility and resistance to a myriad of diseases.

It captivates us how biological complexity is often governed by very simple rules. We focus our scientific energy on simulating complex microbial ecosystems and identifying key principles of their evolution. These models contain many interacting players, such as genes, mobile genetic elements, microbes, and their hosts like plants and animals. We believe that these computational models are integral to understanding the complexity of nature.

Metagenomics is one of the most important tools to explore our microbial world. We develop innovative bioinformatic tools to study metagenomes with applications from ecology to medicine.

In my group we use multiscale models to study the mechanisms regulating the pool of stem cells in the root apex, with the aim of understanding how the size, patterning and regenerative capabilities of the quiescent center (QC) is regulated in different species.

I study biological evolution in heterogeneous environments using theoretical (mathematical) models, and aim to understand the growth of bacteria on mixed-substrate media based on coarse-grained, systems-level constraints and principles.

The aim of my research is to understand biotic systems as dynamic information processing systems at many interconnected levels. Within this framework my current research focuses on evolutionary dynamics and morphogenesis.

Vertebrate cells present short peptides from internal proteins to signal to the immune system. I develop and apply bioinformatic methods to identify antigenic peptides in the proteome of pathogens, tumors, and hosts, and study how these peptides signal to the immune system.

The aim of our research is to study the evolution and function of eukaryotic microbes through (comparative) genomics. We specifically focus on co-evolutionary arms races between pathogens with their hosts.

The research in my group is defined by the following key words: systems genetics, multi-omics, data integration, network biology and bioinformatics. There are two main questions we focus on: How does genetic variation lead to phenotypic variation? What are the interactions between the (soil) microbiome and the environment?

We strive to understand the principles underlying archaeal and bacterial genome architecture, the genomic basis of evolutionary transitions, and the shape of the tree of life. To do this, we develop and apply methods in evolutionary genomics.

In our team, we address these questions using computer models. Models are great tools to check if our current understanding is consistent with expected outcomes, and to test mechanistic hypotheses for their feasibility. A well-crafted model is also predictive: In our everyday lives we all rely on weather models to predict rainfall. Our ultimate goal is to generate predictive models of animal development with all of its complexities.