The role of stochastic gene switching in determining the pharmacodynamics of certain drugs: basic mechanisms

  • Krzysztof Puszynski
  • Alberto Gandolfi
  • Alberto d’OnofrioEmail author
Original Paper


In this paper we analyze the impact of the stochastic fluctuation of genes between their ON and OFF states on the pharmacodynamics of a potentially large class of drugs. We focus on basic mechanisms underlying the onset of in vitro experimental dose-response curves, by investigating two elementary molecular circuits. Both circuits consist in the transcription of a gene and in the successive translation into the corresponding protein. Whereas in the first the activation/deactivation rates of the single gene copy are constant, in the second the protein, now a transcription factor, amplifies the deactivation rate, so introducing a negative feedback. The drug is assumed to enhance the elimination of the protein, and in both cases the success of therapy is assured by keeping the level of the given protein under a threshold for a fixed time. Our numerical simulations suggests that the gene switching plays a primary role in determining the sigmoidal shape of dose-response curves. Moreover, the simulations show interesting phenomena related to the magnitude of the average gene switching time and to the drug concentration. In particular, for slow gene switching a significant fraction of cells can respond also in the absence of drug or with drug concentrations insufficient for the response in a deterministic setting. For higher drug concentrations, the non-responding fraction exhibits a maximum at intermediate values of the gene switching rates. For fast gene switching, instead, the stochastic prediction follows the prediction of the deterministic approximation, with all the cells responding or non-responding according to the drug dose.


Transcriptional Network Switching Rate Stochastic Fluctuation Gene Switching Deterministic Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank the anonymous referees for their useful suggestions.


KP, Polish National Center for Science, funds granted by Decision Number DEC-2012/05/D/ST7/02072. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Automatic ControlSilesian University of TechnologyGliwicePoland
  2. 2.Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” - CNRRomeItaly
  3. 3.International Prevention Research InstituteLyonFrance

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