  Multi-valued network models are an important qualitative modelling approach
used widely by the biological community. In this paper we consider developing
an abstraction theory for multi-valued network models that allows the state
space of a model to be reduced while preserving key properties of the model.
This is important as it aids the analysis and comparison of multi-valued
networks and in particular, helps address the well-known problem of state space
explosion associated with such analysis. We also consider developing techniques
for efficiently identifying abstractions and so provide a basis for the
automation of this task. We illustrate the theory and techniques developed by
investigating the identification of abstractions for two published MVN models
of the lysis-lysogeny switch in the bacteriophage lambda.
