Nanoparticles (NPs) have emerged as an advantageous drug delivery platform for the treatment of various ailments including cancer and cardiovascular and inflammatory diseases. However, their efficacy in shuttling materials to diseased tissue is hampered by a number of physiological barriers. One hurdle is transport out of the blood vessels, compounded by difficulties in subsequent penetration into the target tissue. Here, we report the use of two distinct micropropellers powered by rotating magnetic fields to increase diffusion-limited NP transport by enhancing local fluid convection. In the first approach, we used a single synthetic magnetic microrobot called an artificial bacterial flagellum (ABF), and in the second approach, we used swarms of magnetotactic bacteria (MTB) to create a directable “living ferrofluid” by exploiting ferrohydrodynamics. Both approaches enhance NP transport in a microfluidic model of blood extravasation and tissue penetration that consists of microchannels bordered by a collagen matrix.

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration. Morphogenesis in nature may use two different approaches: hierarchical, top-down control or spontaneously self-organizing dynamics such as reaction-diffusion Turing patterns. Here, we provide a demonstration of purely self-organizing behaviors to create emergent morphologies in large swarms of real robots. The robots achieve this collective organization without any self-localization and instead rely entirely on local interactions with neighbors. Results show swarms of 300 robots that self-construct organic and adaptable shapes that are robust to damage. This is a step toward the emergence of functional shape formation in robot swarms following principles of self-organized morphogenetic engineering.

In most swarm systems, agents are either aware of the position of their direct neighbors or they possess a substrate on which they can deposit information (stigmergy). However, such resources are not always obtainable in real-world applications because of hardware and environmental constraints. In this paper we study in 2D simulation the design of a swarm system which does not make use of positioning information or stigmergy.This endeavor is motivated by an application whereby a large number of Swarming Micro Air Vehicles (SMAVs), of fixed-wing configuration, must organize autonomously to establish a wireless communication network (SMAVNET) between users located on ground. Rather than relative or absolute positioning, agents must rely only on their own heading measurements and local communication with neighbors.Designing local interactions responsible for the emergence of the SMAVNET deployment and maintenance is a challenging task. For this reason, artificial evolution is used to automatically develop neuronal controllers for the swarm of homogenous agents. This approach has the advantage of yielding original and efficient swarming strategies. A detailed behavioral analysis is then performed on the fittest swarm to gain insight as to the behavior of the individual agents.

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.