As with the experimental sciences, the cell simulation field is largely driven by the development of new techniques, and of new tools that implement those techniques. Many techniques under development are working to increase simulation accuracy while reducing the computational burden. A central challenge concerns simulation of interacting processes that occur on different time scales; the fast scale imposes a short simulation time step, but that makes the simulation too slow to observe the slow scale. This is being met with new algorithms and hybrid simulations that treat space and stochasticity only as required. Also, given the size and the possible nonlinearity and non-determinism represented by biological models, tools for analysis of models, such as those that provide parametric sensitivity analysis, and for comparing models to data for parameterization and (in)validation are both profoundly needed and in a relatively primitive state.It is easy to describe the ideal simulation tool: it should be able to simulate reactions and diffusion as accurately as needed, account for all relevant mechanical processes, help with model parameterization, validate and discriminate between models using data, and be easy to use. Many modeling tools are aiming towards this goal but it remains elusive, in part because of the extraordinary speed with which improved analysis methods and cellular measurements are being developed.