System identification of gene networks from time course expression data
Professor Masahiro Okamoto
Laboratory for Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Japan
Tuesday 16th September, 1015 – 1100“Urbygningen - Gamle Festsal”
Agricultural University of Norway, Ås
Professor Okamoto is one of the leading experts within the field of biochemical systems theory and its use on bioinformatic issues. If someone would like to meet Prof. Okamoto while he is here from Tuesday morning September 16 to Thursday morning September18, please contact centre administrator Barbara Eriksen (64 94 70 96 or 91 54 56 59, post@cigene.no). Though the lecture will be of primary interest for those engaged in making and understanding microarray data and gene networks, it should also be of relevance for a wider audience.
Outline:
Recent advances of powerful new technologies such as DNA microarrays provide a mass of gene expression data on a genomic scale. One of the most important research missions in post-genome-era is the system identification of gene networks by using these observed data. Estimation of the interaction mechanisms among system components by using experimentally observed dynamic responses (time-courses) of some of the system components is generally referred to as inverse problem. We previously introduced an efficient numerical optimization technique by using time-course data of system components, which is based on real-coded genetic algorithm to estimate the interaction coefficients among system components of a dynamic network model called S-system that is a type of power-law formalism and is suitable for description of organizationally complex systems such as gene expression networks. This technique with the combination of unimodal distribution crossover (UNDX), which is one of the efficient crossover operators, with the alternation of generation model called minimal generation map (MGG) showed remarkable superiority to the conventional simple genetic algorithm. In this study, for the purpose of inference of interaction mechanism of more large scale gene networks, we propose new efficient approaches to narrow down the network candidates that possibly explain the observed time series of expression profiles within the immense huge searching space of parameter values. We also describe on the GUI program for real-time visualization of the inferred network structures by using distributed parallel computer systems.
