Institut de Mathématiques de Marseille, UMR 7373

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REMY Elisabeth

Organisme : CNRS
Chargé de Recherche

Equipe: Mathématiques et Algorithmique pour la Biologie des Systèmes (BMA-MABioS) (responsable de)

elisabeth.remy _AT_

univ-amu.fr

04 91 26 95 65

Bureau: 133, Etg: 1, Bât: TPR2 - Site : Luminy

Page Web perso

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Membre du conseil du laboratoire

Domaines de Recherche:

Productions scientifiques :

Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling.

Auteur(s): Flobak Åsmund, Baudot A., Remy E., Thommesen Liv, Thieffry Denis, Kuiper Martin, Lægreid Astrid

(Article) Publié: Plos Computational Biology, vol. 11 p.e1004426 (2015)

Ref HAL: 01221572_v1
PMID 26317215
PubMed Central: PMC4567168
Exporter : BibTex | endNote
Résumé:

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.

Logical modelling of regulatory networks, methods and applications.

Auteur(s): Chaouiya Claudine, Remy Elisabeth

(Article) Publié: Bulletin Of Mathematical Biology, vol. 75 p.891-5 (2013)

Ref HAL: hal-01282925_v1
PMID 23722297
Exporter : BibTex | endNote

Relations between gene regulatory networks and cell dynamics in Boolean models

Auteur(s): Didier Gilles, Remy Elisabeth

(Article) Publié: Discrete Applied Mathematics, vol. 160 p.2147 - 2157 (2012)

Ref HAL: hal-01258494_v1
DOI: 10.1016/j.dam.2012.05.010
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Résumé:

no abstract

Dynamically consistent reduction of logical regulatory graphs

Auteur(s): Naldi Aurélien, Remy Elisabeth, Thieffry Denis, Chaouiya Claudine

(Article) Publié: -Journal Of Theoretical Computer Science (Tcs), vol. 412 p. (2011)

Ref HAL: hal-01284743_v1
DOI: 10.1016/j.tcs.2010.10.021
Exporter : BibTex | endNote

Mapping multivalued onto Boolean dynamics

Auteur(s): Didier Gilles, Remy Elisabeth, Chaouiya Claudine

(Article) Publié: Journal Of Theoretical Biology, vol. 270 p.177 (2011)

Ref HAL: hal-00656334_v1
PMID 20868697
DOI: 10.1016/j.jtbi.2010.09.017
Exporter : BibTex | endNote
Résumé:

This paper deals with the generalized logical framework defined by René Thomas in the 70's to qualitatively represent the dynamics of regulatory networks. In this formalism, a regulatory network is represented as a graph, where nodes denote regulatory components (basically genes) and edges denote regulations between these components. Discrete variables are associated to regulatory components accounting for their levels of expression. In most cases, Boolean variables are enough, but some situations may require further values. Despite this fact, the majority of tools dedicated to the analysis of logical models are restricted to the Boolean case. A formal Boolean mapping of multivalued logical models is a natural way of extending the applicability of these tools. Three decades ago, a multivalued to Boolean variable mapping was proposed by P. Van Ham. Since then, all works related to multivalued logical models and using a Boolean representation rely on this particular mapping. We formally show in this paper that this mapping is actually the sole, up to cosmetic changes, that could preserve the regulatory structures of the underlying graphs as well as their dynamical behaviours.

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