Bio-array images processing and genetic networks modelling.
The new tools available for gene expression studies are essentially the bio-array methods using a large variety of physical detectors (isotopes, fluorescent markers, ultrasounds...). Here we present first rapidly an image-processing method independent of the detector type, dealing with the noise and with the peaks overlapping, the peaks revealing the detector activity (isotopic in the presented example), correlated with the gene expression. After this primary step of bio-array image processing, we can extract information about causal influence (activation or inhibition) a gene can exert on other genes, leading to clusters of genes co-expression in which we extract an interaction matrix M and an associated interaction graph G explaining the genetic regulatory dynamics correlated to the studied tissue function. We give two examples of such interaction matrices and graphs (the flowering genetic regulatory network of Arabidopsis thaliana and the lytic/lysogenic operon of the phage Mu) and after some theoretical rigorous results recently obtained concerning the asymptotic states generated by the genetic networks having a given interaction matrix and reciprocally concerning the minimal (in the sense of having a minimal number of non-zero coefficients) matrices having given stationary stable states.
Demongeot
Jacques
J
TIMC-IMAG, CNRS 5525, Faculty of Medicine, 38700 La Tronche, France. Jacques.Demongeot@imag.fr
Thuderoz
Florence
F
Baum
Thierry Pascal
TP
Berger
François
F
Cohen
Olivier
O
D017360:Arabidopsis / Q000235:genetics; D010583:Bacteriophage mu / Q000235:genetics; D015870:Gene Expression; D005786:Gene Expression Regulation; D007091:Image Processing, Computer-Assisted; D008433:Mathematics; D008957:Models, Genetic; D009876:Operon