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Learning Boolean controls in regulated metabolic networks: a case-study

Abstract : Many techniques have been developed to infer Boolean regulations from a prior knowledge network and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks. This paper provides a formalisation of the inference of regulations for metabolic networks as a satisfiability problem with two levels of quantifiers, and introduces a method based on Answer Set Programming to solve this problem on a small-scale example.
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Contributor : Loïc Paulevé <>
Submitted on : Thursday, May 6, 2021 - 3:38:21 PM
Last modification on : Thursday, May 27, 2021 - 2:48:02 PM


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  • HAL Id : hal-03207589, version 2


Kerian Thuillier, Caroline Baroukh, Alexander Bockmayr, Ludovic Cottret, Loïc Paulevé, et al.. Learning Boolean controls in regulated metabolic networks: a case-study. 2021. ⟨hal-03207589v2⟩



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