Our computational biology group believes in a new wave of scientific enlightenment fuelled by intelligent software.
For this purpose, we develop easily accessible open-source software to provide life scientists with toolkits to tackle fundamental problems in biology and health care. Inspired by recent advancements in (meta-) deep (reinforcement) learning and cloud-computing, our ambition is to use this software to bring the predictive capacity of artificial intelligence to molecular biology. Together, this allows us to associate molecular mechanisms of gene regulation with phenotypic changes in complex traits.
We study the regulatory evolution of gene expression.
The evolution of gene regulation
Natural variation is a crucial property of life because it is the outcome of a dynamic genome which creates vast combinatorial population diversity as a hedge against natural selection. Natural variation denotes individual variants of the population with the molecular composition to either adapt to changing environmental conditions or perish due to a lack of adaptive potential. It has been debated for decades whether point mutations, transposons, gene/genome duplications, or changes in gene regulation are the major molecular mechanisms generating natural variation in genomic landscapes.
To contribute to this debate, we use a comparative approach to study the evolution of gene regulation and aim to associate gene regulatory changes with the adaptive evolution of complex traits.
Ultimately, we seek to answer the question how gene regulatory instructions are retained across diverse species despite the presence of natural variation and heterochrony (shifts in developmental timing) driving changes in gene expression.
Detection of conserved and variable modules in gene regulatory networks using a comparative approach
To model this adaptive change on the organism-level, we use the reductionist concept of system-scale gene regulatory networks (sysGRNs) to quantify gene regulatory changes via changes in the topology of the inferred network graph. Comparing such sysGRNs across cell-types, individual organisms, ecotypes, and species enables the identification of conserved and variable sub-networks (= modules) that can be associated with molecular or phenotypic change and allows to identify entire biological pathways driving the evolution of a complex trait. In addition, we seek to trace the birth, sequence divergence, and copy-number-variation of each gene in the sysGRN to understand the evolutionary dynamics of [ regulator – target ] associations which may explain the emergence of adaptability via macro-evolutionary leaps caused by massive regulatory rewiring events.
For this challenging task, we build the mathematical, computational, and comparative frameworks to quantify module retention and adaptive change in sysGRNs and rely on in-house generated as well as publicly available transcriptome datasets to infer such networks covering various biological processes at various resolutions of organismal complexity. Together, this network approach will unveil modules of developmental instructions that are associated with common morphological features shared between related species and provides a new analytical tool to screen for candidate pathways driving adaptive change in various environmental conditions.
We successfully applied aspects of this research strategy to a diverse portfolio of biological questions and keep refining this process to accommodate an increasing amount of incoming data and technologies and to serve a wider range of biological applications. You can find a more detailed summary of our previous research question on the Research Portfolio page.
Finally, our group seeks to train and mentor the next generation of interdisciplinary system-scale thinkers able to integrate insights from computer science, statistical modeling, and diverse life science disciplines to derive a macro-level understanding of natural variation and heredity. If you wish to join or otherwise support our efforts, please read further details here about how to get on-board.