Research Groups

It is the central mission of our institute to understand how cells form tissues. How does a cell in the middle of an organ know when to stop growing because the organ is large enough over-all? How does a cell know along which direction to divide to form a well-structured tissue and not just a clump of cells? Cells individually take these decisions based on communication with their neighbours, and on global signals such as mechanical forces and morphogen gradients. Cells hence “compute” when integrating the signals from their environment in order to determine a decision. It is, however, unknown according to which mechanisms and procedures these “computations” unfold.

Our group’s goal is to unravel the “algorithms of tissue formation”, that is the cell-cell communication patterns and intracellular decision making that enable cells to organize into tissues. Cells in a tissue constitute an elaborate stochastic, concurrent computing device. While the components (=molecules) and source code (=genome) are increasingly known, the algorithms enacted remain a frontier for systems biology. It is our long-term aim to reverse-engineer these algorithms for early embryogenesis of C.elegans, D.melanogaster, and D.rerio.

This defines our vision of a “Virtual Tissue”, that is, a computer simulation of a multi-cellular system that combines our current knowledge of biomechanics, molecular pathways, and spatial patterning, enabling us to test if this knowledge is sufficient to explain the growth and form we observe.

Working toward this goal requires a number of computational and theoretical advances, including a modular multi-scale simulation paradigm for biological processes, and interactive real-time microscopy. At the technical level, it also requires advances in high-performance computing and the corresponding programming languages. These are our mid-term goals.

Our past and current research focuses on providing the prerequisites, contributing novel theories and algorithms driven by concrete biological questions. Ultimately, we hope to converge to a mechanistic understanding of how cells communicate, process information and take decisions, within the boundary conditions set by biophysics, and in the systems considered. The biological phenomena considered are flows in the C.elegans egg and the subsequent rounds of cell divisions, as well as zebrafish and drosophila embryogenesis.

We aim to enable the next step for computational biology: simulation of developmental processes in 3D.