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J. Fabriková

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

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2

I&C Journal 2014 Journal Article

ϵ-Semantics computations on biological systems

  • A. Casagrande
  • T. Dreossi
  • J. Fabriková
  • C. Piazza

The assumption of being able to perform infinite precision measurements does not only lead to undecidability, but it also introduces artifacts in the mathematical models that do not correspond to observable behaviours of systems under study. When bounded spatial regions are involved, such issues can be avoided if arbitrarily small sets of points are not definable in the mathematical setting. ϵ-semantics were introduced in this spirit. In this paper we investigate the use of ϵ-semantics deeper, in the context of reachability analysis of hybrid automata. In particular, we focus on two ϵ-semantics and reason about their computability. We then try our approach on biological model analysis to give evidence about the effectiveness of the methodology.

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TCS Journal 2009 Journal Article

On algorithmic analysis of transcriptional regulation by LTL model checking

  • J. Barnat
  • L. Brim
  • I. Černá
  • S. Dražan
  • J. Fabriková
  • D. Šafránek

Studies of cells in silico can greatly reduce the need for expensive and prolonged laboratory experimentation. The use of model checking for the analysis of biological networks has attracted much attention recently. The practical limitations are still the size of the model, and the time needed to generate the state space. This paper is focused on the model checking approach for analysis of piecewise-linear deterministic models of genetic regulatory networks. Firstly, the qualitative simulation algorithm of de Jong et al. that builds the heart of Genetic Network Analyzer (GNA) is revisited and its time complexity is studied in detail. Secondly, a novel algorithm that reduces the state space generation time is introduced. The new algorithm is developed as an abstraction of the original GNA algorithm. Finally, a fragment of linear time temporal logic for which the provided abstraction is conservative is identified. Efficiency of the new algorithm when implemented in the parallel model checking environment is demonstrated on a set of experiments performed on randomly modified biological models. In general, the achieved results bring a new insight into the field of qualitative simulation emerging in the context of systems biology.

Details DOI
v2026.09.13