DE-Sim: an Object-Oriented, Discrete-Event Simulation Tool for Data-Intensive Modeling of Complex Systems in Python

Recent advances in data collection, storage, and sharing have created unprecedented opportunities to gain insights into complex systems such as the biochemical networks that generate cellular behavior. Understanding the behavior of such systems will likely require larger and more comprehensive dynamical models that are based on a combination of first principles and empirical data. These models will likely represent each component and interaction using mechanistic approximations that are derived from first principles and calibrated with data. For example, dynamical models of biochemical networks often represent the interactions among molecules as chemical reactions whose rates are determined by combining approximations of chemical kinetics and empirically-observed reaction rates. Furthermore, complex models that represent multiple types of components and their interactions will require diverse approximations and large, heterogeneous datasets. New tools are needed to build and simulate such data-intensive models.