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Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesisuse asterix (*) to get italics
Loïc PaulevéPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2022
<p style="text-align: justify;">Boolean networks (BNs) are discrete dynamical systems with applications to the modeling of cellular behaviors. In this paper, we demonstrate how the software BoNesis can be employed to exhaustively identify combinations of perturbations which enforce properties on their fixed points and attractors. We consider marker properties, which specify that some components are fixed to a specific value. We study 4 variants of the marker reprogramming problem: the reprogramming of fixed points, of minimal trap spaces, and of fixed points and minimal trap spaces reachable from a given initial configuration with the most permissive update mode. The perturbations consist of fixing a set of components to a fixed value. They can destroy and create new attractors. In each case, we give an upper bound on their theoretical computational complexity, and give an implementation of the resolution using the BoNesis Python framework. Finally, we lift the reprogramming problems to ensembles of BNs, as supported by BoNesis, bringing insight on possible and universal reprogramming strategies. This paper can be executed and modified interactively.</p>
https://github.com/bnediction/reprogramming-with-bonesisYou should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
https://github.com/bnediction/reprogramming-with-bonesisYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
https://github.com/bnediction/bonesisYou should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
boolean networks, control, attractors, gene regulatory networks
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Combinatorics, Computational complexity, Dynamical systems, Molecular Biology, Systems biology
Jorge Gómez Tejeda Zañudo [jgtz@broadinstitute.org ], Sven Thiele [thieles@mpi-magdeburg.mpg.de], Pedro Tiago Monteiro [Pedro.Tiago.Monteiro@tecnico.ulisboa.pt], Kwang-Hyun Cho [chk@kaist.ac.kr], Tatsuya Akutsu [kuicr.kyoto-u.ac.jp], Franck Delaplace [franck.delaplace@univ-evry.fr], Heike Siebert [siebert@mi.fu-berlin.de ] No need for them to be recommenders of PCI Math Comp Biol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe [john@doe.com]
2022-08-31 15:00:21
Sergiu Ivanov
Ismail Belgacem, Anonymous