Abstract
Petri nets are very well suited for the representation of biological systems. Biological entities like proteins, metabolites, genes etc. can be defined as places; biochemical reactions, regulatory effects, modifications etc. can be defined as transitions. This 1 to 1 correspondence of molecules/reactions and places/transitions allows a very intuitive setup of a computational model framework. In this chapter, we will show how, additionally, the current states of biological entities and the reaction effects can be defined in a very intuitive and natural way using elements taken from fuzzy logic theory. Often exact data or detailed knowledge about concentrations, reaction kinetics or regulatory effects is missing. Thus, computational modeling of a biological system requires dealing with uncertainty and rough information provided by qualitative knowledge and linguistic descriptions. The Petri net and fuzzy logic (PNFL) approach allows natural language based descriptions of entities as well as (if-then) rule based definitions of reaction effects, both of which can easily and directly be derived from qualitative (linguistic) knowledge. PNFL bridges the gap between qualitative knowledge and quantitative modeling.
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Windhager, L., Erhard, F., Zimmer, R. (2011). Fuzzy Modeling. In: Koch, I., Reisig, W., Schreiber, F. (eds) Modeling in Systems Biology. Computational Biology, vol 16. Springer, London. https://doi.org/10.1007/978-1-84996-474-6_9
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DOI: https://doi.org/10.1007/978-1-84996-474-6_9
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