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Sequential Reprogramming of Boolean Networks Made Practical

Abstract : 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. attrac-tors, 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 reduces 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.
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Contributor : Loïc Paulevé <>
Submitted on : Wednesday, September 4, 2019 - 10:36:35 AM
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Hugues Mandon, Cui Su, Stefan Haar, Jun Pang, Loïc Paulevé. Sequential Reprogramming of Boolean Networks Made Practical. CMSB 2019 - 17th International Conference on Computational Methods in Systems Biology, Sep 2019, Trieste, France. pp.3--19, ⟨10.1007/978-3-030-31304-3_1⟩. ⟨hal-02178917v2⟩



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