Macha NIKOLSKI
Macha Nikolski Directeur de Recherches (CNRS)

Équipe LaBRI : Bench to Knowledge and Beyond
Fonction administrative : Directrice du CBIB
Page personnelle : http://www.labri.fr/~macha
Courrier électronique : macha.nikolskiarobaselabri.fr
Bureau : 269
Tél : +33 (0)5 40 00 38 24


Recherche

Macha Nikolski received her doctoral degree in Computer Science in 2000 from the University Bordeaux 1 under the direction of
Antoine Rauzy, on the subject of binary decision diagrams and their application to the reliability analysis of complex industrial
systems. She subsequently worked for two years in the United States as a research engineer for Synopsys, before returning to France
in 2002 to a position as Research Scientist (Chargée de Recherches) in the CNRS. As a founding member of the Mabiovis team in the
LaBRI and the Magnome team in the INRIA, she works with biologists in comparative genomics and systems biology. In comparative
genomics she has developed algorithms for data-mining analysis of genomic data through consensus clustering, and for large-scale
combinatorial analysis of genome formation through chromosomal rearrangements. The goal of this work is to construct
comprehensive views of the history and function of genomes, through the development of multi-scale representations of the
mathematical relations between genes, and of algorithmic descriptions of the mechanisms of genome evolution.

In systems biology her recent research is on multi-scale hybrid and stochastic modeling of cell behaviour, through the development
of a mixed-formalism modeling platform called BioRica. The focus of this work is to develop network descriptions of cell behaviour
(interaction, metabolic, and regulation networks), and link them together into hierarchical models of complete systems that admit
high-performance simulation. This approach has been successfully used in e.g. generalizing a single-cell model of transmission of
oxidatively damaged proteins from mother to daughter cells, to a structured population model in which one can identify specific
lineages with effective strategies that increase survival of the population.

Extrait de publications


Claudine Boiziau, Macha Nikolski, Elodie Mordelet, Justine Aussudre, Karina Vargas-Sanchez, et al.. A Peptide Targeting Inflammatory CNS Lesions in the EAE Rat Model of Multiple Sclerosis. Inflammation, Springer Verlag, 2018

Thomas Bazin, Katarzyna Hooks, Thomas Barnetche, Marie-Elise Truchetet, Raphaël Enaud, et al.. Microbiota Composition May Predict Anti-Tnf Alpha Response in Spondyloarthritis Patients: an Exploratory Study. Scientific Reports, Nature Publishing Group, 2018, 8 (1), pp.5446

Camille Tumiotto, Pantxika Bellecave, Patricia Recordon-Pinson, Alexi Groppi, Macha Nikolski, et al.. DIVERSITY OF HIV-1 IN AQUITAINE, SOUTH-WESTERN FRANCE, 2012-2016. AIDS Research and Human Retroviruses, Mary Ann Liebert, 2018

Justine Rudewicz, Hayssam Soueidan, Raluca Uricaru, Hervé Bonnefoi, Richard Iggo, et al.. MICADo - Looking for mutations in targeted PacBio cancer data: an alignment-free method. Frontiers in Genetics, Frontiers, 2016

Eric Dausse, Aurélien Barré, Aimé Ahissan, Alexis Groppi, Alain Rico, et al.. Aptamer selection by direct microfluidic recovery and surface plasmon resonance evaluation. Biosensors and Bioelectronics, Elsevier, 2016


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