Publications

  • Carta, M. Chaves, J.-L. Gouzé (2015). Continuous-switch piecewise quadratic models of biological networks: Application to bacterial growth. Automatica, 61:164-172
  • Chaves, A. Carta (2015). Attractor computation using interconnected boolean networks: testing growth rate models in E. coli. Theor Comput Sci, 599:47-63
  • Giordano, F. Mairet, J.-L. Gouzé, J. Geiselmann, H. de Jong (2016). Dynamical allocation of cellular resources as an optimal control problem: Novel insights into microbial growth strategies. PLoS Comput. Biol., 12(3): e1004802
  • Izard, C. Gomez Balderas, D. Ropers, S. Lacour, X. Song, Y. Yang, A.B. Lindner, J. Geiselmann, H. de Jong (2015). A synthetic growth switch based on controlled expression of RNA polymerase. Mol. Syst. Biol., 11(11):840
  • Kremling, J. Geiselmann, D. Ropers, H. de Jong (2015). Understanding carbon catabolite repression in Escherichia coli using quantitative models. Trends Microbiol, 23(2):99-109
  • Mihalcescu, M. Van Melle – Gateau, B. Chelli, C. Pinel, J.L. Ravanat (2015). Green autofluorescence, a double edged monitoring tool for bacterial growth and activity in micro-plates. Phys. Biol., 12(6):066016
  • Morin, D. Ropers, F. Letisse, S. Laguerre, J.C. Portais, M. Cocaign-Bousquet, B. Enjalbert (2016). The post-transcriptional regulatory system CSR controls the balance of metabolic pools in upper glycolysis of Escherichia coli. Mol. Microbiol. In press
  • Stefan, C. Pinel, S. Pinhal, E. Cinquemani, J. Geiselmann, H. de Jong (2015). Inference of quantitative models of bacterial promoters from time-series reporter gene data. PLoS Comput Biol, 11(1):e1004028
  • Trauchessec, M. Jaquinod, A. Bonvalot, V. Brun, C. Bruley, D. Ropers, H. de Jong, J. Garin, G. Bestel-Corre, M. Ferro (2014). Mass spectrometry-based workflow for accurate quantification of E. coli enzymes: how proteomics can play a key role in metabolic engineering. Mol Cell Proteom, 13(4):954-968.
  • Zulkower, M. Page, D. Ropers, J. Geiselmann, H. de Jong (2015). Robust reconstruction of gene expression profiles using linear inversion. Bioinformatics, 31(12):i71-i79