Design of complex biologically based nanoscale systems using multi-agent simulations and structure-behavior-function representations

The process of designing integrated biological systems across scales is difficult, with challenges arising from the modeling, understanding, and search of complex system design spaces. This paper explores these challenges through consideration of how stochastic nanoscale phenomenon relate to higher level systems functioning across many scales. A domain-independent methodology is introduced which uses multi-agent simulations to predict emergent system behavior and structure-behavior-function (SBF) representations to facilitate design space navigation. The methodology is validated through a nanoscale design application of synthetic myosin motor systems. In the multi-agent simulation, myosins are independent computational agents with varied structural inputs that enable differently tuned mechanochemical behaviors. Four synthetic myosins were designed and replicated as agent populations, and their simulated behavior was consistent with empirical studies of individual myosins and the macroscopic performance of myosin-powered muscle contractions. However, in order to configure high performance technologies, designers must effectively reason about simulation inputs and outputs; we find that counter-intuitive relations arise when linking system performance to individual myosin structures. For instance, one myosin population had a lower system force even though more myosins contributed to system-level force. This relationship is elucidated with SBF by considering the distribution of structural states and behaviors in agent populations. For the lower system force population, it is found that although more myosins are producing force, a greater percentage of the population produces negative force. The success of employing SBF for understanding system interactions demonstrates how the methodology may aid designers in complex systems embodiment. The methodology's domain-independence promotes its extendibility to similar complex systems, and in the myosin test case the approach enabled the reduction of a complex physical phenomenon to a design space consisting of only a few critical parameters. The methodology is particularly suited for complex systems with many parts operating stochastically across scales, and should prove invaluable for engineers facing the challenges of biological nanoscale design, where designs with unique properties require novel approaches or useful configurations in nature await discovery.