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 ]

  • 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 ]

  • 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 ]

  • 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.

  • 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.

  • 8M. 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 ]

  • 9M. 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.

  • 10C. 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 ]

  • 11D. 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.

  • 12V. 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.

Publications of the year

Articles in International Peer-Reviewed Journals

  • 13I. Belgacem, S. Casagranda, E. Grac, D. Ropers, J.-L. Gouzé.

    Reduction and stability analysis of a transcription-translation model of RNA polymerase, in: Bulletin of Mathematical Biology, 2018, vol. 80, no 2, pp. 294-318. [ DOI : 10.1007/s11538-017-0372-4 ]

  • 14S. Casagranda, S. Touzeau, D. Ropers, J.-L. Gouzé.

    Principal process analysis of biological models, in: BMC Systems Biology, 2018, vol. 12, 68 p. [ DOI : 10.1186/s12918-018-0586-6 ]

  • 15E. Cinquemani.

    Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data, in: Processes, August 2018, vol. 6, no 9, pp. 1-24. [ DOI : 10.3390/pr6090136 ]

  • 16E. Cinquemani.

    Stochastic reaction networks with input processes: Analysis and application to gene expression inference, in: Automatica, 2018, pp. 1-9.

  • 17A. Kremling, J. Geiselmann, D. Ropers, H. De Jong.

    An ensemble of mathematical models showing diauxic growth behaviour, in: BMC Systems Biology, December 2018, vol. 12, no 1, pp. 1-16. [ DOI : 10.1186/s12918-018-0604-8 ]

  • 18D. Lucena, M. Mauri, F. Schmidt, B. Eckhardt, P. L. Graumann.

    Microdomain formation is a general property of bacterial membrane proteins and induces heterogeneity of diffusion patterns, in: BMC Biology, December 2018, vol. 16, no 1. [ DOI : 10.1186/s12915-018-0561-0 ]

  • 19A. Marguet.

    Uniform sampling in a structured branching population, in: Bernoulli, 2019, https://arxiv.org/abs/1609.05678.

  • 20I. 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, October 2018, pp. 1-48. [ DOI : 10.1007/s00285-018-1299-6 ]


Other Publications