In recent years, there has been an increasing interest in developing in vitro models that predict the behavior of cells in living organisms. Mathematical models based on differential equations, and related numerical algorithms, have been provided to this aim. In this work, we present first experiences in designing parallel strategies for accelerating an algorithm for behavior prediction based on the Cellular Potts Model (CPM). In particular, we exploit the computational power of Graphic Process Units in CUDA environment to address main low-level kernels involved. Tests and experiments complete the paper.