Graphical rule-based modeling formalisms and associated simulation algorithms have been developed to represent biochemical systems in terms of formal rules for biomolecular interactions.

However, representing a biochemical system as a chemical reaction network is often cumbersome and unnecessary. Conventional models are essentially specified with lists of biochemical species and their reactions. Limitations of conventional modeling approaches, which rely on explicit specifications of chemical reaction networks, lie in both model construction and simulation. Models that account for the functions of the component parts of proteins (e.g, linear motifs and protein interaction domains) are needed to better understand the dynamics of signal-transduction systems. Proteins in a signal-transduction system often have multiple component parts that enable the protein to interact with other molecules in a modular manner. For example, models sometimes inadequately account for the complexities of protein interactions, including interaction details at the level of protein sites and structural relationships among components of signaling proteins, particularly multisite protein modification in the context of multiprotein complexation. However, models often fail to capture the mechanistic details of signal transduction systems. In recent years, computational models have been increasingly used in cell signaling research and have been developed to study various pathways. In computational systems biology, studying a complex biochemical system involving a large number of interacting proteins often relies on in silico simulations to analyze and predict system behaviors.

Finite state automata key