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Laboratory of Computational Biology

Stein Aerts Bioinformatics Gene Regulation Drosophila Leuven University of Leuven Human Genetics Computational Biology Gene Regulatory Networks Transcriptomics


The S. Aerts Lab of Computational Biology (LCB) is a research group part of the Center for Human Genetics(CME), at the Faculty of Medicine

Research in our lab is focusing on gene and genome regulation, with applications in Drosophila and cancer.

Gene Regulatory Networks in Drosophila

We are interested in regulatory interactions between transcription factors and their target genes. We use retinal specification in Drosophila as model system and combine genetics, high-throughput technologies, and bioinformatics to characterize the gene regulatory networks implementing developmental programs.

Integrative Regulatory Genomics

We develop integrative genomics methods to identify "regulatory tracks" and regulatory motifs in a set of co-expressed genes. See the tools section.

Evolution of Gene Expression in Drosophila

cis-Regulatory variation is a major driver of developmental evolution. By comparing gene expression using RNA-Seq across homologous developmental programs, we aim to further our understanding of CRM divergence and plasticity, and the consequential divergence of gene expression and regulatory networks.

Cancer Genomics & Transcriptomics

Oncogenic programs are often perturbations of developmental programs with de-regulated transcription, which can be caused by various DNA aberrations including point mutations in transcription factors and signaling molecules, or deletions, amplifications, and translocations causing transcription factors to be mis-expressed. We are interested in mapping these DNA variations by next-generation sequencing, identifying driver mutations, and characterizing the downstream gene regulatory network changes.

Computational CRM prediction

The nodes in a gene regulatory network are cis-regulatory modules (CRM) where usually multiple transcription factors can bind to regulate the transcriptional initiation rate of target gene. We are interested in computational modeling of CRMs, their analysis, and genome-wide prediction. Both in Drosophila and human/mouse.