Individual cells function as a collection of interacting biochemical molecules and molecular machines. Consider single-cell microbes, the simplest self sufficient living organisms. At a macroscopic level they are a small bag of biochemical cytoplasm with little internal structure. Yet microbes create and use the fundamental molecular machinery common to all life (protein synthesis via DNA transcription and ribosomal mRNA translation,regulation of gene expression, membrane control of small molecule transport, signal propagation)to sense the state of their environment and respond appropriately (grow, move, reproduce).Eukaryotic cells execute enhanced versions of these regulatory, metabolic, and signaling networks within structures of rich spatial complexity (organelles, membranes, filaments, etc).Such a picture poses a wide range of challenges for computational cell modeling in which various groups are addressing in different ways. Typically, one key input to all such models is a set of reaction rate equations which specify the biochemistry to be modeled. Each equation has one or more reactants which are converted at a certain rate into one or more products.Complex networks may contain 100s or 1000s of such coupled equations.