Bayesian Inference for Identifying Interaction Rules in Moving Animal Groups

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of “universal” classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed

Cellular expression through morphogen delivery by light activated magnetic microrobots

Microrobots have many potential uses in microbiology since they can be remotely actuated and precisely manipulated in biochemical fluids. Cellular function and response depends on biochemicals. Therefore, various delivery methods have been developed for delivering biologically relevant cargo using microrobots. However, localized targeting without payload leakage during transport is challenging. Here, we design a microrobotic platform capable of on-demand delivery of signaling molecules in biological systems. The on-demand delivery method is based on a light-responsive photolabile linker which releases a cell-to-cell signaling molecule when exposed to light, integrated on the surface of microrobots. Successful delivery of the signaling molecules and subsequent gene regulation is also demonstrated. This proposed method can be used for multiple applications, especially in biology, engineering, and medicine where on-demand delivery of chemical cargo at targeted locations is important.ONR (Grant N00014-11-1-0725)NSF (Grants CNS-1446474 and CNS-1446592