Unraveling the Collective Dynamics of Complex Adaptive Biomedical Systems

Many systems in Biomedical Engineering and across the Biosciences involve collections of semi-autonomous objects which may be naturally occurring or artificial (e.g, nanostructures or nanomachines). Such collections of objects do not typically have any central controller, and yet respond to the same global cues in terms of feedback of information from the environment. They also typically will not be inert or identical. Instead they are heterogenous and may respond differently to a given piece of external information or system history according to the particular state that they are in, meaning that have effectively have a limited set of simple strategies. In this mini-review, we outline an approach to tackling the general challenge of understanding how collections of such heterogeneous, adaptive systems behave dynamically. These issues are important for understanding the inherent risks involved in the collective behavior of next-generation systems of objects (`agents’) across the spectrum of biomedical engineering applications.