Sabine Hauert

Identification and Characterization of Receptor-Specific Peptides for siRNA Delivery

Tumor-targeted delivery of siRNA remains a major barrier in fully realizing the therapeutic potential of RNA interference. While cell-penetrating peptides (CPP) are promising siRNA carrier candidates, they are universal internalizers that lack cell-type specificity. Herein, we design and screen a library of tandem tumor-targeting and cell-penetrating peptides that condense siRNA into stable nanocomplexes for cell type-specific siRNA delivery. Through physiochemical and biological characterization, we identify a subset of the nanocomplex library of that are taken up by cells via endocytosis, trigger endosomal escape and unpacking of the carrier, and ultimately deliver siRNA to the cytosol in a receptor-specific fashion. To better understand the structure–activity relationships that govern receptor-specific siRNA delivery, we employ computational regression analysis and identify a set of key convergent structural properties, namely the valence of the targeting ligand and the charge of the peptide, that help transform ubiquitously internalizing cell-penetrating peptides into cell type-specific siRNA delivery systems.

Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology

Nanoparticles are designed to deliver therapeutics and diagnostics selectively to tumors. Their size, shape, charge, material, coating, and cargo determine their individual functionalities. A systems approach could help predict the behavior of trillions of nanoparticles interacting in complex tumor environments. Engineering these nanosystems may lead to biomimetic strategies where interactions between nanoparticles and their environment give rise to cooperative behaviors typically seen in natural self-organized systems. Examples include nanoparticles that communicate the location of a tumor to amplify tumor homing or self-assemble and disassemble to optimize nanoparticle transport. The challenge is to discover which nanoparticle designs lead to a desired system behavior. To this end, novel nanomaterials, deep understanding of biology, and computational tools are emerging as the next frontier.

Evolving behaviour trees for swarm robotics

Controllers for swarms of robots are hard to design as swarm behaviour emerges from their interaction, and so controllers are often evolved. However, these evolved controllers are often difficult to understand, limiting our ability to predict swarm behaviour. We suggest behaviour trees are a good control architecture for swarm robotics, as they are comprehensible and promote modular reuse. We design a foraging task for kilobots and evolve a behaviour tree capable of performing that task, both in simulation and reality, and show the controller is compact and understandable.

A Two Teraflop Swarm

We introduce the Xpuck swarm, a research platform with an aggregate raw processing power in excess of two teraflops. The swarm uses 16 e-puck robots augmented with custom hardware that uses the substantial CPU and GPU processing power available from modern mobile system-on-chip devices. The augmented robots, called Xpucks, have at least an order of magnitude greater performance than previous swarm robotics platforms. The platform enables new experiments that require high individual robot computation and multiple robots. Uses include online evolution or learning of swarm controllers, simulation for answering what-if questions about possible actions, distributed super-computing for mobile platforms, and real-world applications of swarm robotics that requires image processing, or SLAM. The teraflop swarm could also be used to explore swarming in nature by providing platforms with similar computational power as simple insects. We demonstrate the computational capability of the swarm by implementing a fast physics-based robot simulator and using this within a distributed island model evolutionary system, all hosted on the Xpucks.