Biological structures are extremely complex at the cellular level. The MCell project has been highly successful in simulating the microphysiology of systems of modest size, but many larger problems require too much storage and computation time to be simulated on a single workstation. MCell-K, a new parallel variant of MCell, has been implemented using the KeLP framework and is running on NPACI's Blue Horizon. MCell-K not only produces validated results consistent with the serial version of MCell but does so with unprecedented scalability. We have thus found a level of description and a way to simulate cellular systems that can approach the complexity of nature on its own terms. At the heart ofMCell is a 3D random walk that models diffusion using a Monte Carlo method. We discuss two challenging issues that arose in parallelizing the diffusion process — detecting time-step termination efficiently and performing parallel diffusion of particles in a biophysically accurate way. We explore the scalability limits of the present parallel algorithm and discuss ways to improve upon these limits.