Algorithms for the Sequential Reprogramming of Boolean Networks

Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2019-09, Vol.16 (5), p.1610-1619
Main Authors: Mandon, Hugues, Su, Cui, Pang, Jun, Paul, Soumya, Haar, Stefan, Pauleve, Loic
Format: Article
Language:English
Subjects:
Algorithms
Animals
Binary decision diagrams
Bioinformatics
Biological information theory
Biological system modeling
Boolean algebra
Boolean functions
Boolean networks
Cell Transdifferentiation - genetics
Cellular communication
Cellular reprogramming
Cellular Reprogramming Techniques - methods
Computational Biology - methods
Computational modeling
Computational models
Computer applications
Computer Science
Control
Control methods
Data Structures and Algorithms
Force
Heuristic algorithms
Identification methods
Mice
Modeling and Simulation
Models, Genetic
Mutation
Mutation - genetics
Perturbation methods
Regenerative medicine
Systems and Control
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Summary:Cellular reprogramming, a technique that opens huge opportunities in modern and regenerative medicine, heavily relies on identifying key genes to perturb. Most of the existing computational methods for controlling which attractor (steady state) the cell will reach focus on finding mutations to apply to the initial state. However, it has been shown, and is proved in this article, that waiting between perturbations so that the update dynamics of the system prepares the ground, allows for new reprogramming strategies. To identify such sequential perturbations, we consider a qualitative model of regulatory networks, and rely on Binary Decision Diagrams to model their dynamics and the putative perturbations. Our method establishes a set identification of sequential perturbations, whether permanent (mutations) or only temporary, to achieve the existential or inevitable reachability of an arbitrary state of the system. We apply an implementation for temporary perturbations on models from the literature, illustrating that we are able to derive sequential perturbations to achieve trans-differentiation.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2019.2914383