Inferring qualitative relations in genetic networks and metabolic pathways

Motivation: inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boole an network was not sufficient as a model of a genetic network. Results: First, a Boolean network model with noise is proposed, together wi th art inference algorithm for it. Next, a qualitative network model is pro posed, in which regulation rules are represented as qualitative rules and e mbedded in the network structure. Algorithms are also presented for inferri ng qualitative relations from time series data. Then, an algorithm for infe rring S-systems (synergistic and saturable systems) from time series data i s presented, where S-systems are based on a particular kind of nonlinear di fferential equation and have been applied to the analysis of various biolog ical systems. Theoretical results are shown for Boolean networks with noise s and simple qualitative networks. Computational results are shown for Bool ean networks with noises and S-systems, where real data are not used becaus e the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.