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EN
Petri nets are a well-established modelling framework in life sciences and have been widely applied to systems and synthetic biology in recent years. With the various extensions they serve as graphical and simulation interface for both qualitative and quantitative modelling approaches. In terms of quantitative approaches, Stochastic and Continuous Petri nets are extensively used for modelling biological system’s dynamics if underlying kinetic data are known. However, these are often only vaguely defined or even missing. In this paper we present a fuzzy approach, which can be used to model biological processes with unknown kinetic data in order to still obtain quantitatively relevant simulation results. We define fuzzy firing rate functions, which can be used in Continuous Petri nets and are able to describe different processes that govern the dynamics of gene expression networks. They can be used in combination with the conventional firing rate functions and applied only in the parts of the system for which the kinetic data are missing. The case study of the proposed approach is performed on models of a hypothetical repressilator and Neurospora circadian rhythm.
EN
We present the information processing perspective on biological systems. Several metrics, similar to the ones used in digital electronic circuits, are introduced. These metrics allow us to compare biological information processing structures with their electronic counterparts, to define the ones with the best dynamical properties, analyse their compatibility and most importantly, automatize their design. Regarding the metric values obtained and used on a simple example, target applications of synthetic information processing biological structures are discussed.
PL
W artykule opisano zagadnienie przepływu informacji w systemach biologicznych. Zastosowano tu odwzorowanie na elementach i obwodach elektronicznych, co pozwoliło na analizę ich własności, w tym dynamicznych oraz zautomatyzowanie projektowania takich modeli. Zawarto także omówienie otrzymanych wyników badań.
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