Model reduction and consistency in discrete event representation of biological systems

The recent developments in biological experiments have awarded the research community with valuable information, which describe finely regulated systems that govern the cell dynamics. One of the greatest challenges, however, remains to represent this extensive amount of knowledge in a proper way that can be used in simulations, and validated automatically, in order to ultimately achieve a desired behavior for the system (cell) under control. Many tools and techniques have been proposed in the literature to address this important problem. In this paper, the use of Petri nets for knowledge representation is investigated. Two algorithms are provided to construct Petri net models for cell dynamics using data available in public domain biological database. The first algorithm generates a low-level model capturing protein-protein interactions and the second, produces a high-level model which describes pathway sequences and is considerably easier to analyze. A concept of consistency between the two models is introduced and a test for consistency is provided.