A symbolic-based approach to modelling biochemical processes and cellular
dynamics is likely to turn useful in computational biology, where attempts to represent
the cell as a huge, complex dynamic system must trade with the linguistic nature of the
DNA and the individual behavior of the organelles living within. The early version of
the metabolic algorithm gave a first answer to the problem of representing oscillatory
biological phenomena, so far being treated with traditional (differential) mathematical
tools, in terms of rewriting systems. We are now working on a further version of this
algorithm, in which the rule application is tuned by reaction maps depending on the
specific phenomenon under consideration. Successful simulations of the Brusselator, the
Lotka-Volterra population dynamics and the PKC activation foster potential applications
of the algorithm in systems biology
