Macha NIKOLSKI
Macha Nikolski Directeur de Recherches (CNRS)

Équipe LaBRI : Modèles et Algorithmes pour la Bioinformatique et la Visualisation
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













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