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The lymphatic system is a networked structure used by billions of immune cells, including T cells and Dendritic cells, to locate and identify invading pathogens. Dendritic cells carry pieces of pathogens to the nearest lymph node, and T cells travel through the lymphatic vessels and search within lymph nodes to find them. Here we investigate how the topology of the lymphatic network affects the time for this search to be completed. Building on prior work that maps out the human lymphatic network, we develop and extend a method to infer the lymphatic network topology of mice. We compare search times for the modeled and observed topologies and show that they are similar to each other and consistent with observed immune response times. This is relevant for translating immune response times in mice, where most experimental work occurs, into expected immune response times in humans. Our analysis predicts that for large systemic infections, the topology of the lymphatic network allows immune response times to remain fast even as animal mass increases by orders of magnitude. This work advances our understanding of how the structure of the lymphatic network supports the swarm intelligence of the immune system. It also elucidates general principles relating swarm size and organization to search speed. 

All living systems perpetuate themselves via growth in or on the body, followed by splitting, budding, or birth. We find that synthetic multicellular assemblies can also replicate kinematically by moving and compressing dissociated cells in their environment into functional self-copies. This form of perpetuation, previously unseen in any organism, arises spontaneously over days rather than evolving over millennia. We also show how artificial intelligence methods can design assemblies that postpone loss of replicative ability and perform useful work as a side effect of replication. This suggests other unique and useful phenotypes can be rapidly reached from wild-type organisms without selection or genetic engineering, thereby broadening our understanding of the conditions under which replication arises, phenotypic plasticity, and how useful replicative machines may be realized. 

Robot swarms have, to date, been constructed from artificial materials. Motile biological constructs have been created from muscle cells grown on precisely shaped scaffolds. However, the exploitation of emergent self-organization and functional plasticity into a self-directed living machine has remained a major challenge. We report here a method for generation of in vitro biological robots from frog ( Xenopus laevis ) cells. These xenobots exhibit coordinated locomotion via cilia present on their surface. These cilia arise through normal tissue patterning and do not require complicated construction methods or genomic editing, making production amenable to high-throughput projects. The biological robots arise by cellular self-organization and do not require scaffolds or microprinting; the amphibian cells are highly amenable to surgical, genetic, chemical, and optical stimulation during the self-assembly process. We show that the xenobots can navigate aqueous environments in diverse ways, heal after damage, and show emergent group behaviors. We constructed a computational model to predict useful collective behaviors that can be elicited from a xenobot swarm. In addition, we provide proof of principle for a writable molecular memory using a photoconvertible protein that can record exposure to a specific wavelength of light. Together, these results introduce a platform that can be used to study many aspects of self-assembly, swarm behavior, and synthetic bioengineering, as well as provide versatile, soft-body living machines for numerous practical applications in biomedicine and the environment.