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Bibliography

Publications of the year

Doctoral Dissertations and Habilitation Theses

Articles in International Peer-Reviewed Journals

  • 2S. Danthine, C. Vors, D. Agopian, A. Durand, R. Guyon, F. Carrière, C. Knibbe, M. Létisse, M.-C. Michalski.

    Homogeneous triacylglycerol tracers have an impact on the thermal and structural properties of dietary fat and its lipolysis rate under simulated physiological conditions, in: Chemistry and Physics of Lipids, December 2019, vol. 225, 104815 p. [ DOI : 10.1016/j.chemphyslip.2019.104815 ]

    https://hal.archives-ouvertes.fr/hal-02326893
  • 3A. A. Davin, T. Tricou, E. Tannier, D. M. de Vienne, G. J. Szöllosi.

    Zombi: A phylogenetic simulator of trees, genomes and sequences that accounts for dead lineages, in: Bioinformatics, 2019. [ DOI : 10.1093/bioinformatics/btz710 ]

    https://hal.archives-ouvertes.fr/hal-02302010
  • 4A. Denizot, M. Arizono, V. U. Nägerl, H. Soula, H. Berry.

    Simulation of calcium signaling in fine astrocytic processes: effect of spatial properties on spontaneous activity, in: PLoS Computational Biology, August 2019, vol. 15, no 8, e1006795, pp. 1-33. [ DOI : 10.1371/journal.pcbi.1006795 ]

    https://hal.inria.fr/hal-02184344
  • 5G. Gangarossa, S. Perez, Y. Dembitskaya, I. Prokin, H. Berry, L. Venance.

    BDNF controls bidirectional endocannabinoid-plasticity at corticostriatal synapses, in: Cerebral Cortex, April 2019, vol. bhz081, pp. 1-18. [ DOI : 10.1093/cercor/bhz081 ]

    https://hal.inria.fr/hal-02076121
  • 6D. Hasic, E. Tannier.

    Gene tree reconciliation including transfers with replacement is NP-hard and FPT, in: Journal of Combinatorial Optimization, 2019, vol. 38, no 2, pp. 502–544. [ DOI : 10.1007/s10878-019-00396-z ]

    https://hal.archives-ouvertes.fr/hal-02301454
  • 7D. Hasic, E. Tannier.

    Gene tree species tree reconciliation with gene conversion, in: Journal of Mathematical Biology, 2019, vol. 78, no 6, pp. 1981–2014, https://arxiv.org/abs/1703.08950. [ DOI : 10.1007/s00285-019-01331-w ]

    https://hal.archives-ouvertes.fr/hal-01495707
  • 8C. Knibbe, G. Beslon, D. Misevic.

    Introduction to the ECAL 2017 Special Issue, in: Artificial Life, November 2019, vol. 25, no 4, pp. 313-314. [ DOI : 10.1162/artl_a_00298 ]

    https://hal.inria.fr/hal-02428658
  • 9N. Méger, C. Rigotti, C. Pothier, T. Nguyen, F. Lodge, L. Gueguen, R. Andréoli, M.-P. Doin, M. Datcu.

    Ranking evolution maps for Satellite Image Time Series exploration: application to crustal deformation and environmental monitoring, in: Data Mining and Knowledge Discovery, January 2019, vol. 33, no 1, pp. 131-167. [ DOI : 10.1007/s10618-018-0591-9 ]

    https://hal.archives-ouvertes.fr/hal-01898015
  • 10E. Parey, A. Crombach.

    Evolution of the Drosophila melanogaster Chromatin Landscape and Its Associated Proteins, in: Genome Biology and Evolution, January 2019, vol. 11, no 3, pp. 660-677. [ DOI : 10.1093/gbe/evz019 ]

    https://hal.inria.fr/hal-02064719
  • 11S. Peignier, C. Rigotti, G. Beslon.

    Evolutionary Subspace Clustering Using Variable Genome Length, in: Computational Intelligence, 2020, pp. 1-39, forthcoming.

    https://hal.archives-ouvertes.fr/hal-02405598
  • 12J. P. Rutten, P. Hogeweg, G. Beslon.

    Adapting the engine to the fuel: mutator populations can reduce the mutational load by reorganizing their genome structure, in: BMC Evolutionary Biology, 2019, vol. 19, no 1, pp. 1-17. [ DOI : 10.1186/s12862-019-1507-z ]

    https://hal.archives-ouvertes.fr/hal-02350040

International Conferences with Proceedings

  • 13Q. Carde, M. Foley, C. Knibbe, D. P. Parsons, J. Rouzaud-Cornabas, G. Beslon.

    How to reduce a genome? ALife as a tool to teach the scientific method to school pupils, in: ALIFE 2019 - Conference on Artificial Life, Newcastle, United Kingdom, MIT Press, July 2019, pp. 497-504. [ DOI : 10.1162/isal_a_00211 ]

    https://hal.inria.fr/hal-02285718
  • 14L. Foulon, S. Fenet, C. Rigotti, D. Jouvin.

    Scoring Message Stream Anomalies in Railway Communication Systems, in: LMID 2019 - IEEE Workshop on Learning and Mining with Industrial Data, Beijing, China, November 2019, pp. 1-8.

    https://hal.archives-ouvertes.fr/hal-02357924
  • 15T. Nguyen, N. Méger, C. Rigotti, C. Pothier, N. Gourmelen, E. Trouvé.

    A pattern-based mining system for exploring Displacement Field Time Series, in: 19th IEEE International Conference on Data Mining (ICDM) Demo, Beijing, China, Proceedings of the IEEE International Conference on Data Mining (ICDM) Demo, IEEE, November 2019, pp. 1110-1113.

    https://hal.archives-ouvertes.fr/hal-02361793

National Conferences with Proceedings

  • 16L. Foulon, C. Rigotti, S. Fenet, D. Jouvin.

    Approximation du score CFOF de détection d’anomalie dans un arbre d’indexation iSAX : Application au contexte SI de la SNCF, in: EGC 2019 - 19ème Conférence francophone sur l'Extraction et la Gestion des Connaissances, Metz, France, 2019, pp. 1-12.

    https://hal.inria.fr/hal-02019035

Scientific Books (or Scientific Book chapters)

  • 17M. De Pittà, H. Berry.

    Computational Glioscience, Springer Series in Computational Neuroscience, Springer, 2019, pp. 1-505. [ DOI : 10.1007/978-3-030-00817-8 ]

    https://hal.inria.fr/hal-01995842
  • 18M. De Pittà, E. Ben-Jacob, H. Berry.

    G protein-coupled receptor-mediated calcium signaling in astrocytes, in: Computational Glioscience, M. De Pittà, H. Berry (editors), Springer Series in Computational Neuroscience, Springer, January 2019, pp. 115-150. [ DOI : 10.1007/978-3-030-00817-8_5 ]

    https://hal.inria.fr/hal-01995850
  • 19M. De Pittà, H. Berry.

    A Neuron-Glial Perspective for Computational Neuroscience, in: Computational Glioscience, M. De Pittà, H. Berry (editors), Springer Series in Computational Neuroscience, Spinger, January 2019, pp. 3-35. [ DOI : 10.1007/978-3-030-00817-8_1 ]

    https://hal.inria.fr/hal-01995849
  • 20A. Denizot, H. Berry, S. Venugopal.

    Intracellular Ca 2+ Dynamics in Astrocytes: Modeling the Underlying Spatiotemporal Diversity, in: Encyclopedia of Computational Neuroscience, 2019, pp. 1-19, forthcoming.

    https://hal.inria.fr/hal-02419317
  • 21J. Lallouette, M. De Pittà, H. Berry.

    Astrocyte networks and intercellular calcium propagation, in: Computational Glioscience, M. De Pittà, H. Berry (editors), Springer Series in Computational Neuroscience, January 2019, pp. 177-210. [ DOI : 10.1007/978-3-030-00817-8_7 ]

    https://hal.inria.fr/hal-01995852
  • 22M. Stimberg, D. F. M. Goodman, R. Brette, M. De Pittà.

    Modeling Neuron–Glia Interactions with the Brian 2 Simulator, in: Computational Glioscience, M. D. Pittà, H. Berry (editors), January 2019, pp. 471-505. [ DOI : 10.1007/978-3-030-00817-8_18 ]

    https://hal.sorbonne-universite.fr/hal-02325277

Internal Reports

  • 23F. Berthoud, P. Guitton, L. Lefèvre, S. Quinton, A. Rousseau, J. Sainte-Marie, C. Serrano, J.-B. Stefani, P. Sturm, E. Tannier.

    Sciences, Environnements et Sociétés : Rapport long du groupe de travail MakeSEnS d’Inria, Inria, October 2019.

    https://hal.inria.fr/hal-02340948

Scientific Popularization

Other Publications

  • 25L. Foulon, S. Fenet, C. Rigotti, D. Jouvin.

    Detecting Anomalies over Message Streams in Railway Communication Systems, September 2019, 1 p, AALTD@ECML/PKDD 2019 - 4th Workshop on Advanced Analytics and Learning on Temporal Data. Poster, Poster.

    https://hal.archives-ouvertes.fr/hal-02357927
  • 26C. Rocabert, G. Beslon, C. Knibbe, S. Bernard.

    Phenotypic Noise and the Cost of Complexity, November 2019, EvoLyon 2019, Poster.

    https://hal.archives-ouvertes.fr/hal-02402443
References in notes
  • 27P. Galison.

    Image and Logic: A Material Culture of Microphysics, University Of Chicago Press, 1997.
  • 28C. Vors, G. Pineau, L. Gabert, al..

    Modulating absorption and postprandial handling of dietary fatty acids by structuring fat in the meal: a randomized crossover clinical trial, in: Am J Clin Nutr, 2013, vol. 97, pp. 23–36.


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