MiGHT, a multi-level Gillespie hybrid tracked modeling framework which allows for cellular and environmental adaptivity

Cells dynamically alter their behavior and adapt the profile of receptors and ligands they produce in response to their personal history. This is clear in development and, even more so, in our understanding of the differentiation and action of the cells of the immune system. To model the adaptive changes of leukocytes, we must incorporate the cell's internal state as it is shaped by the cell's history within an environment. To do so, we have developed a multi-level Gillespie hybrid tracked (MiGHT) modeling schema. MiGHT derives from our previous modeling framework TIPS, a tracked interacting particle system (IPS) modeling framework. TIPS expanded on the IPS modeling structure by tagging entities to allow for agent-based manipulations and the tracking of individual agents over the course of a simulation through complex binding via a hierarchical structure. In MiGHT we have incorporated the ability to model internal mechanisms as sub-level self-contained models, which can both respond to historical events and the microenvironment, as well as influence their agent's future behavior. MiGHT also includes the ability to utilize hybrid simulation methods, including tau-leaping estimations and differential equation time steps. This gives us the capability of efficiently simulating differing amounts of molecules, such as cytokines as well as the ability to efficiently model internal interactions. Lastly, MiGHT includes an environment restrictions method, which allows for the environment to directly affect the behavior of the simulation as well as the ability to alter the simulation techniques based on individual bin entity concentrations. This creates a multi-level simulation in which entities can actively adapt their behavior to both history and the environment. Using MiGHT we have simulated the macrophage response to lipopolysaccharide (LPS). We show that a single population of macrophages can transition through the stages of macrophage activation and inactivation via a simple set of dynamics. Furthermore, we show that the systemic response of macrophages is dependent on how individual macrophages are distributed within the environment.