title: Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.
abstract: Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.
authors.forename: Jure
authors.initials: J
authors.lastname: Bordon
authors.forename: Miha
authors.initials: M
authors.lastname: Moskon
authors.forename: Nikolaj
authors.initials: N
authors.lastname: Zimic
authors.forename: Miha
authors.initials: M
authors.lastname: Mraz
mesh_terms: D000818:Animals; D017143:Fuzzy Logic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007700:Kinetics; D008954:Models, Biological; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome