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AlgoReCell 

Models and Algorithms for cellular reprogramming strategies prediction


Axis : SciLex
Subject : Modèles et algorithmes pour la prédiction de stratégies de reprogrammation cellulaire
Thesis Supervisor : Stefan Haar,LSV & Loic Paulevé, LRI
Beginning: 2016
Institution :LSV - ENS ParisSaclay
Host Laboratory : Inria Saclay
PhD : Hugues Mandon
Link from thèses.fr : Hugues Mandon

Scientific productions :
  • Hugues Mandon, Stefan Haar, and Loïc Paulevé. Temporal Reprogramming of Boolean Networks. In CMSB 2017 - 15th conference on Computational Methods for Systems Biology, volume 10545 of Lecture Notes in Computer Science, 179–195. Springer International Publishing, 2017. DOI:10.1007/978-3-319-67471-1_11
  • Hugues Mandon, Stefan Haar, and Loïc Paulevé. Relationship between the Reprogramming Determinants of Boolean Networks and their Interaction Graph. In Eugenio Cinquemani and Alexandre Donzé, editors, Hybrid Systems Biology: 5th International Workshop, HSB 2016, Grenoble, France, October 20-21, 2016, Proceedings, volume 9957 of Lecture Notes in Computer Science, 113–127. Springer International Publishing, 2016. DOI:10.1007/978-3-319-47151-8_8

Context :
Cellular reprogramming consists in acting on certain specific genes, called Reprogramming Determinants (RD), in order to trigger cellular de-differentiation or re-differentiation. The in-silico prediction of such RDs is a major goal, especially in regenerative medicine. The aim of this thesis project is to provide a formal basis for the identification of disturbances that allow for a change of attractor in discrete models of biological networks. First, the impact of the different reprogramming strategies (permanent or temporary mutations, order of mutations, etc.) will be studied in depth. Then, algorithms will be designed to identify and verify the RDs found by the different reprogramming strategies. Such algorithms will use extensive analysis of network dynamics, and require the development of new techniques to exploit competition in discrete networks in order to be usable on large networks. Based on a rigorous analysis of the dynamics of large networks, the project will lead to a decisive advance in the calculation of accurate predictions for experimental cellular reprogramming.

Scientific goals :
The goal of this PhD project is to bring a formal perspective to the identification of cell reprogramming strategies from logical model sofbiological networks,and apply it to the differentiation of mesenchymal stem cells into adipocytes and osteoblasts.It will consists in(1)characterizing the different classes of reprogramming strategies,and their impact on the fidelity of the reprogramming (2)designing scalable algorithms for the complete identification and verification of reprogramming strategies from Bolean and logical models by exploiting both network topology and concurrency to analyse the dynamics of the models;(3)providing prototype implementations that will be applied on benchmark models and on models of generegulatory networks under going in adipocytes and osteoblasts.The expected outcome of the project is a rigorous framework for the prediction of reprogramming strategies from logical models of biological networks with controlled guarantees on thefidelity.The approach will be applied for there differentiation of adipocytes and osteoblasts,studied by collaborators in Luxembourgand who can performe xperimental validation for newly predicted strategies.