  We propose a theoretical formalism, molecular finite automata (MFA), to
describe individual proteins as rule-based computing machines. The MFA
formalism provides a framework for modeling individual protein behaviors and
systems-level dynamics via construction of programmable and executable
machines. Models specified within this formalism explicitly represent the
context-sensitive dynamics of individual proteins driven by external inputs and
represent protein-protein interactions as synchronized machine
reconfigurations. Both deterministic and stochastic simulations can be applied
to quantitatively compute the dynamics of MFA models. We apply the MFA
formalism to model and simulate a simple example of a signal transduction
system that involves a MAP kinase cascade and a scaffold protein.
