Exploiting equation-free analysis for multi-level, agent-based models in cell biology

Abstract

Multi-level modeling approaches have been successfully applied in systems biology to model complex systems with different levels of organization. They allow for straightforwardly integrating upward and downward causation as well as compartmental dynamics. This makes multi-level models powerful, but also expensive to simulate. Consequently, the effort required for comprehensive simulation studies with complex multi-level models is often prohibitive. One way to decrease the demand for simulations is to apply analysis methods. However, most approaches focus on differential equations models and cannot handle models with stochasticity or dynamical nesting. Among the new approaches that allow for analysis of complex systems is equation-free analysis, which has been applied to perform coarse level bifurcation analysis in various areas. We present the integration of an equation-free method into the simulation language Simulation Experiment Specification on a Scala Layer (SESSL) to analyze bi-or multistability of biochemical models, defined in the multi-level modeling language ML-Rules, and its role in cell fate selection.