1 research outputs found
Tools and techniques for multi-valued networks using rewriting logic
PhD ThesisMulti-valued networks (MVNs) are an important, widely used qualitative modelling technique
where time and states are discrete. MVNs extend the well-known Boolean networks by
providing a more powerful qualitative modelling approach for biological systems by allowing
an entity’s state to be within a range of discrete set of values instead of just 0 and 1. They
provide a logical framework for qualitatively modelling and analysing control systems and
have been successfully applied to biological systems and circuit design. While a range of
support tools for developing and analysing MVNs exist, more work is needed to develop
tools to support the practical applications of those techniques.
One of the frameworks that have been successfully applied to biological systems is
Rewriting Logic (RL), an algebraic specification framework that is capable of modelling and
analysing the behaviour of dynamic, concurrent systems. The flexibility of RL techniques
such as implementation of strategies has allowed it to be successfully used to model a wide
range of different formalisms and systems, such as process algebras, Petri nets, and biological
systems. RL specification, programming and computation is supported by a range of powerful
analysis tools which was one of the motivations for choosing to use RL. We choose Maude
as a tool in our work here which is a high-performance reflective language supporting both
equational and RL specification. Maude is going to be used through this thesis to model and
analyse a range of MVNs using RL.
In this thesis we aim to investigate the application of RL to modelling and analysing
both synchronous and asynchronous MVNs, thus enabling the application of support tools
available for RL. We start by constructing an RL model for MVNs using a translation
approach that translates an MVNs set of equations into rewrite rules. We formally show that
our translation approach is correct by proving its soundness and completeness. We illustrate
the techniques and the developed RL framework for MVNs by presenting a range of case
studies which provides a good illustration of the practical application of the developed RL
framework. We then introduce an artificial, scalable MVN model in order to allow a range of
model sizes to be considered and we investigate the performance of our RL framework. We
analyse a larger regulatory network from the literature using our RL framework to give some
insights into how it coped with a larger case studyMinistry of Higher Education in Saudi Arabi