Molecular Biology produces a huge amount of data concerning the behavior of the
single constituents of living organisms. Nevertheless, this reductionism view is not
sucient to gain a deep comprehension of how such components interact together
at the system level, generating the set of complex behavior we observe in nature.
This is the main motivation of the rising of one of the most interesting and recent
applications of computer science: Computational Systems Biology, a new science
integrating experimental activity and mathematical modeling in order to study the
organization principles and the dynamic behavior of biological systems.
Among the formalisms that either have been applied to or have been inspired by
biological systems there are automata based models, rewrite systems, and process
calculi.
Here we consider a formalism based on term rewriting called Calculus of Looping
Sequences (CLS) aimed to model chemical and biological systems. In order to quantitatively
simulate biological systems a stochastic extension of CLS has been developed;
it allows to express rule schemata with the simplicity of notation of term
rewriting and has some semantic means which are common in process calculi.
In this thesis we carry out the study of the implementation of a stochastic simulator
for the CLS formalism. We propose an extension of Gillespie's stochastic
simulation algorithm that handles rule schemata with rate functions, and we present
an efficient bottom-up, pre-processing based, CLS pattern matching algorithm.
A simulator implementing the ideas introduced in this thesis, has been developed
in F#, a multi-paradigm programming language for .NET framework modeled on
OCaml. Although F# is a research project, still under continuous development,
it has a product quality performance. It merges seamlessly the object oriented,
the functional and the imperative programming paradigms, allowing to exploit the
performance, the portability and the tools of .NET framework
