Simulating cell biology

Science is an iterative process of experiments and hypotheses. Experiments produce surprising results; hypotheses are created to explain the results; new experiments are designed to test the hypotheses, of which some agree, some fail without yielding useful information and some produce more surprises; and the cycle continues. As a field matures, knowledge grows and the hypotheses become more elaborate, eventually exceeding the limits of what a scientist can mentally grasp. This is where computational modeling becomes necessary, and where cell biology is today. Modeling serves the same purposes as scientific cartoons or calculations on the backs of envelopes, but is much more precise. A model can definitively show if an hypothesis can explain a set of data, make experimental predictions and help identify system aspects that are poorly understood. After many iterations of experiments and theory, models are often sufficiently supported by evidence that they represent the current understanding of a system, against which new results are compared. This primer focuses on simulations of biochemical reaction networks, which is a core component of most cell biological models. We leave aside the related arts of model building, model analysis such as sensitivity analysis, and model/ data comparison. We explore a range of simulation methods that vary in their level of physical approximation and abstraction. For concreteness, each is presented for the same generic elementary reversible chemica