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Bibliography

Major publications by the team in recent years
  • 1V. Baldazzi, D. Ropers, Y. Markowicz, D. Kahn, J. Geiselmann, H. de Jong.

    The carbon assimilation network in Escherichia coli is densely connected and largely sign-determined by directions of metabolic fluxes, in: PloS Computational Biology, 2010, vol. 6, no 6, e1000812 p.
  • 2S. Berthoumieux, M. Brilli, H. de Jong, D. Kahn, E. Cinquemani.

    Identification of metabolic network models from incomplete high-throughput datasets, in: Bioinformatics, 2011, vol. 27, no 13, pp. i186–i195.
  • 3S. Berthoumieux, H. De Jong, G. Baptist, C. Pinel, C. Ranquet, D. Ropers, J. Geiselmann.

    Shared control of gene expression in bacteria by transcription factors and global physiology of the cell, in: Molecular Systems Biology, January 2013, vol. 9, no 1, 11 p. [ DOI : 10.1038/msb.2012.70 ]

    http://hal.inria.fr/hal-00793352
  • 4E. Cinquemani, V. Laroute, M. Bousquet, H. De Jong, D. Ropers.

    Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data, in: Bioinformatics, 2017, vol. 33, no 14, pp. i301–i310. [ DOI : 10.1093/bioinformatics/btx250 ]

    https://hal.archives-ouvertes.fr/hal-01607919
  • 5N. Giordano, F. Mairet, J.-L. Gouzé, J. Geiselmann, H. De Jong.

    Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies, in: PLoS Computational Biology, March 2016, vol. 12, no 3, e1004802 p. [ DOI : 10.1371/journal.pcbi.1004802 ]

    https://hal.inria.fr/hal-01332394
  • 6J. Izard, C. Gomez-Balderas, D. Ropers, S. Lacour, X. Song, Y. Yang, A. B. Lindner, J. Geiselmann, H. De Jong.

    A synthetic growth switch based on controlled expression of RNA polymerase, in: Molecular Systems Biology, November 2015, vol. 11, no 11, 16 p.

    https://hal.inria.fr/hal-01247993
  • 7A. Llamosi, A. Gonzalez, C. Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen, G. Batt.

    What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast, in: PLoS Computational Biology, February 2016, vol. 12, no 2, e1004706 p.

    https://hal.inria.fr/hal-01248298
  • 8A. Marguet, M. Lavielle, E. Cinquemani.

    Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data, in: Bioinformatics, 2019, vol. 35, no 14, pp. i586–i595. [ DOI : 10.1093/bioinformatics/btz378 ]

    https://hal.archives-ouvertes.fr/hal-02317115
  • 9M. Morin, D. Ropers, F. Letisse, S. Laguerre, J.-C. J.-C. Portais, M. Cocaign-Bousquet, B. Enjalbert.

    The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli, in: Molecular Microbiology, January 2016, vol. 4, 15 p. [ DOI : 10.1111/mmi.13343 ]

    https://hal.inria.fr/hal-01418224
  • 10M. Morin, D. Ropers, F. Letisse, S. Laguerre, J.-C. J.-C. Portais, M. Cocaign-Bousquet, B. Enjalbert.

    The Csr system regulates Escherichia coli fitness by controlling glycogen accumulation and energy levels, in: mBio, 2017, vol. 8, no 5, pp. 1-14.

    https://hal.inria.fr/hal-01672038
  • 11C. Peano, J. Wolf, J. Demol, E. Rossi, L. Petiti, G. de Bellis, J. Geiselmann, T. Egli, S. Lacour, P. Landini.

    Characterization of the Escherichia coli σ(S) core regulon by Chromatin Immunoprecipitation-sequencing (ChIP-seq) analysis, in: Scientific Reports, 2015, vol. 5, 15 p. [ DOI : 10.1038/srep10469 ]

    https://hal.inria.fr/hal-01217828
  • 12D. Stefan, C. Pinel, S. Pinhal, E. Cinquemani, J. Geiselmann, H. De Jong.

    Inference of quantitative models of bacterial promoters from time-series reporter gene data, in: PLoS Computational Biology, 2015, vol. 11, no 1, e1004028.

    https://hal.inria.fr/hal-01097632
  • 13V. Zulkower, M. Page, D. Ropers, J. Geiselmann, H. De Jong.

    Robust reconstruction of gene expression profiles from reporter gene data using linear inversion, in: Bioinformatics, 2015, vol. 31, no 12, pp. i71–i79.

    https://hal.inria.fr/hal-01217800
Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 14E. Cinquemani.

    Identification, estimation and control of gene expression and metabolic network dynamics, Université Grenoble-Alpes, ED MSTII, November 2019, Habilitation à diriger des recherches.

    https://hal.inria.fr/tel-02424024

Articles in International Peer-Reviewed Journals

  • 15E. Cinquemani.

    Stochastic reaction networks with input processes: Analysis and application to gene expression inference, in: Automatica, 2019, vol. 101, pp. 150-156. [ DOI : 10.1016/j.automatica.2018.11.047 ]

    https://hal.inria.fr/hal-01925923
  • 16M. Di Salvo, S. Puccio, C. Peano, S. Lacour, P. Alifano.

    RhoTermPredict: an algorithm for predicting Rho-dependent transcription terminators based on Escherichia coli, Bacillus subtilis and Salmonella enterica databases, in: BMC Bioinformatics, December 2019, vol. 20, no 1, pp. 1-11. [ DOI : 10.1186/s12859-019-2704-x ]

    https://hal.inria.fr/hal-02423208
  • 17M. Hoffmann, A. Marguet.

    Statistical estimation in a randomly structured branching population, in: Stochastic Processes and their Applications, 2019, vol. 129, no 12, pp. 5236-5277. [ DOI : 10.1016/j.spa.2019.02.015 ]

    https://hal.archives-ouvertes.fr/hal-01662203
  • 18S. Leonard, S. Meyer, S. Lacour, W. Nasser, F. Hommais, S. Reverchon.

    APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data, in: Nucleic Acids Research, May 2019, vol. 47, no 15, pp. 1-12. [ DOI : 10.1093/nar/gkz485 ]

    https://hal.archives-ouvertes.fr/hal-02152104
  • 19A. Marguet, M. Lavielle, E. Cinquemani.

    Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data, in: Bioinformatics, 2019, vol. 35, no 14, pp. i586-i595. [ DOI : 10.1093/bioinformatics/btz378 ]

    https://hal.archives-ouvertes.fr/hal-02317115
  • 20A. Marguet.

    A law of large numbers for branching Markov processes by the ergodicity of ancestral lineages, in: ESAIM: Probability and Statistics, 2019, pp. 1-23, https://arxiv.org/abs/1707.07993, forthcoming. [ DOI : 10.1051/ps/2018029 ]

    https://hal.archives-ouvertes.fr/hal-01567317
  • 21A. Marguet.

    Uniform sampling in a structured branching population, in: Bernoulli, 2019, vol. 25, no 4A, pp. 2649-2695, https://arxiv.org/abs/1609.05678. [ DOI : 10.3150/18-BEJ1066 ]

    https://hal.archives-ouvertes.fr/hal-01362366
  • 22Y. Martin, M. Page, C. Blanchet, H. De Jong.

    WellInverter: a web application for the analysis of fluorescent reporter gene data, in: BMC Bioinformatics, December 2019, vol. 20, no 1, 309 p. [ DOI : 10.1186/s12859-019-2920-4 ]

    https://hal.inria.fr/hal-02195461
  • 23S. Pinhal, D. Ropers, J. Geiselmann, H. De Jong.

    Acetate metabolism and the inhibition of bacterial growth by acetate, in: Journal of Bacteriology, July 2019, vol. 201, no 13, pp. 147 - 166. [ DOI : 10.1128/JB.00147-19 ]

    https://hal.inria.fr/hal-02195459
  • 24I. Yegorov, F. Mairet, H. De Jong, J.-L. Gouzé.

    Optimal control of bacterial growth for the maximization of metabolite production, in: Journal of Mathematical Biology, 2019, vol. 78, no 4, pp. 985–1032. [ DOI : 10.1007/s00285-018-1299-6 ]

    https://hal.inria.fr/hal-01929475

International Conferences with Proceedings

  • 25E. Cinquemani, F. Mairet, I. Yegorov, H. De Jong, J.-L. Gouzé.

    Optimal control of bacterial growth for metabolite production: The role of timing and costs of control, in: ECC 2019 - 18th European Control Conference, Naples, Italy, IEEE, June 2019, pp. 2657-2662. [ DOI : 10.23919/ECC.2019.8796079 ]

    https://hal.inria.fr/hal-02384034

Other Publications



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