UR1404 Applied Mathematics and Informatics from Genome to the Environment (MaIAGE)



The MaIAGE laboratory gathers mathematicians, computer scientists, bioinformaticians and biologists to tackle problems from biology, agronomy and ecology. Our research concerns processes at various levels, ranging from molecular, cellular or multicellular levels to organisms, populations, and entire ecosystems.

MaIAGE develops new methods in mathematics, statistics, and computer science, which may be generic or driven by specific biological problems. A particular focus is on developing software, databases, ontologies and web services to be used by biologists in analyzing data or mining the scientific literature.

A main expertise of the laboratory is in statistical inference and dynamic modelling, along with bioinformatics, automatic control and algorithmics. Research and engineering activities are based on strong involvement in the target disciplines: ecology, environment, molecular biology and systems biology.

Latest publications

Bray J.P., O'Reilly-Nugent, A., Kon Kam King, G., Sarit Kaserzon, Nichols, S. J., Mac Nally, R., Thompson, Ross M., Kefford, B. J. (2020) Can SPEcies At Risk of pesticides (SPEAR) indices detect effects of target stressors among multiple interacting stressors?. Science of The Total Environment, Volume : in press, https://doi.org/10.1016/j.scitotenv.2020.142997

L Schwendimann, D Merda, T Berger, S Denayer, C Feraudet, Tarisse, A J Kläui, S Messio, M Y Mistou, Y Nia, J A Hennekinne, H U Graber
(2020) Staphylococcal enterotoxin gene cluster: prediction of enterotoxin (SEG and SEI) production and of the source of food poisoning based on v Saβ typing . APPLIED AND ENVIRONMENTAL MICROBIOLOGY, Volume : , doi: 10.1128/AEM.02662-20.

Sultan, Ibrahim; Fromion, Vincent; Schbath, Sophie; Nicolas, Pierre. (2020) Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application to Listeria monocytogenes. Journal of The Royal Society Interface, , DOI

Mazo G., Portier F. (2020) Parametric versus nonparametric: The fitness coefficient. . Scandinavian Journal Of Statistics. , , DOI

Ezanno P., Andraud M., Beaunée G., Hoch T., Krebs S., Rault A., Touzeau S., Vergu E., Widgren S. (2020) How mechanistic modelling supports decision making for the control of enzootic infectious diseases.. Epidemics, , DOI

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