Welcome BCS

Welcome to the Bioinspired Communication Systems Lab


Mission statement: We want to build programmable matter. Imagine objects composed of reprogrammable units instead of plain atoms; Inscribed rules instead of physical laws govern their behavior. Objects morph and change function according to local interactions or communications imposed on their constituents. Nature generated a hierarchy of units that have increasing levels of plasticity and corporate or assemble into large-scale systems. We aim to learn from nature how man-made large-scale distributed systems could self-organize and perform complex tasks. Applying statistical inference we reverse-engineer those systems by going from measurements of their global emergent behavior to their local interactions.

In order to realize that long-term vision we work on concrete self-organizing systems from technology and nature. Those include large-scale sensor networks, self-propelled particle systems, molecular self-assembly systems and molecular reaction networks.

The BCS group is a joint venture between the Department of Electrical Engineering and Information Technology and the Department of Biology at TU Darmstadt. We perform theoretical analysis and wet-lab work.

BCS is co-directing the LOEWE Research Program CompuGene. BCS is also part of DFG Collaborative Research Centre (SFB) 1053 (SFB) MAKI and an associated member of the DFG Graduate School (GRK) 1994 AIPHES.


  • 2020/04/30

    PhD Defense Francois-Xavier Lehr

    Date: Thursday, April 30, 2020

    Time: 10.00 a.m.

  • 2020/01/29

    Smart combinations

    TU Darmstadt launches Centre for Synthetic Biology with a strong focus on engineering

    TU Darmstadt is uniting its research competence in the field of synthetic biology in a new centre. By international standards, the “Centre for Synthetic Biology” positions itself uniquely due to its emphasis on engineering and technology.

  • 2019/12/04

    PhD Defense Leo Bronstein

    Date: Tuesday, December 3, 2019

    Time: 2.00 p.m.

    Location: S3/19, 0.5

    Title: Aoproximation and Model Reduction for the Stochastic Kinetics of Reaction Networks