Sequential Reprogramming of Boolean Networks Made Practical

  • Hugues Mandon
  • Cui Su
  • Stefan Haar
  • Jun Pang
  • Loïc PaulevéEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11773)


We address the sequential reprogramming of gene regulatory networks modelled as Boolean networks. We develop an attractor-based sequential reprogramming method to compute all sequential reprogramming paths from a source attractor to a target attractor, where only attractors of the network are used as intermediates. Our method is more practical than existing reprogramming methods as it incorporates several practical constraints: (1) only biologically observable states, viz. attractors, can act as intermediates; (2) certain attractors, such as apoptosis, can be avoided as intermediates; (3) certain nodes can be avoided to perturb as they may be essential for cell survival or difficult to perturb with biomolecular techniques; and (4) given a threshold k, all sequential reprogramming paths with no more than k perturbations are computed. We compare our method with the minimal one-step reprogramming and the minimal sequential reprogramming on a variety of biological networks. The results show that our method can greatly reduce the number of perturbations compared to the one-step reprogramming, while having comparable results with the minimal sequential reprogramming. Moreover, our implementation is scalable for networks of more than 60 nodes.


Cell reprogramming Boolean networks Attractors 



This research was supported by the ANR-FNR project AlgoReCell (ANR-16-CE12-0034; FNR INTER/ANR/15/11191283); Labex DigiCosme (project ANR-11-LABEX-0045-DIGICOSME) operated by ANR as part of the program “Investissement d’Avenir” Idex Paris-Saclay (ANR-11-IDEX-0003-02); and by the project SEC-PBN funded by University of Luxembourg. Cui Su was also partially supported by the COST Action IC1405.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hugues Mandon
    • 1
  • Cui Su
    • 2
  • Stefan Haar
    • 1
  • Jun Pang
    • 2
    • 3
  • Loïc Paulevé
    • 4
    Email author
  1. 1.LSV, ENS Paris-Saclay, Inria, CNRS, Université Paris-SaclayCachanFrance
  2. 2.SnTUniversity of LuxembourgLuxembourgLuxembourg
  3. 3.FSTCUniversity of LuxembourgEsch-sur-AlzetteLuxembourg
  4. 4.Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800TalenceFrance

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