SysBio.se - Systems and Synthetic Biology

Systems and Synthetic Biology is headed by Professor Ivan Mijakovic and is composed of several labs directed by faculty members at the department. The lab and computational facilities are shared among all members of Systems and Synthetic Biology. The division is very international with researchers from more than 30 countries and we have as a key objective to support young researchers in their careers. We are participants in several large research centers and in many international projects funded by the EU and other funding agencies.
The division of Systems and Synthetic Biology is part of the Department of Biology and Biological Engineering at Chalmers University of Technology.

Labs

Chen Lab

Our research focus is on reprogramming cellular metabolism to produce biochemicals, fuels and pharmaeuceticals.

David Lab

Our research focus is on developing microbial drug discovery and production platforms.

Kerkhoven Lab

Through model-driven analysis of experimental data we aim to understand, predict and engineer biology, among others in baker’s and oleaginous yeasts and antibiotic producing bacteria.

King Lab

Our main research focus is on the automation of science.

Larsson Lab

Our lab is focused on understanding the physiology and metabolic regulation in the yeast Saccharomyces cerevisiae and to apply this knowledge in various metabolic engineering projects.

Mijakovic Lab

We work on signaling and regulation in bacterial cells, focusing in particular on regulatory phenomena based on protein phosphorylation.

Nielsen Lab

Our research focus is on systems biology of metabolism. The yeast Saccharomyces cerevisiae is our key organism for experimental research.

Petranovic Lab

We are combining methods of molecular and cell biology, biochemistry, bioinformatics and systems biology with the scope to study how cells regulate aging and death pathways.

Siewers Lab

We develop and apply synthetic biology tools to generate yeast cell factories for the sustainable production of proteins and chemicals as well as to create whole-cell biosensors.

Zelezniak Lab

We apply best practices of computational biology together with machine learning to understand the behavior of metabolic networks at single and multispecies levels.