Combined sequenced-based model of the Drosophila gap gene network Dymova A.V., Kozlov K.N. Peter the Great St. Petersburg Polytechnic University, Politekhnicheskaya St., 29, St. Petersburg, Russia e-mail: dymova.arina@mail.ru, kozlov_kn@spbstu.ru Gursky V.V. Ioffe Institute, Politekhnicheskaya St., 26, St. Petersburg, Russia e-mail: gursky@math.ioffe.ru Samsonova M.G. Peter the Great St. Petersburg Polytechnic University, Politekhnicheskaya St., 29, St. Petersburg, Russia e-mail: m.samsonova@spbstu.ru Transcription is vital for normal functioning of all organisms, as it is the most significant regulation level of gene expression. An interest in detailed analysis of transcriptional regulation grows due to new large-scale data acquisition techniques. There is a large amount of experimental data available about the Drosophila segment determination system. The gap gene system implements the most upstream regulatory layer of the segmentation gene network. It receives inputs from long-range protein gradients encoded by maternal coordinate genes and establishes discrete territories of gene expression. In this process the gap gene cross-regulation plays important role. The formation of gap gene expression domains is a dynamic process: the domains do not form in one place, but instead in the posterior half of the embryo they shift anteriorly during cleavage cycle 14. Despite our expanding knowledge of the biochemistry of gene regulation, we lack a quantitative understanding of this process at a molecular level. We applied the systems-level approach to study gap gene network in Drosophila. A combined sequence-based model of gap gene regulatory network controlling segment determination in the early Drosophila embryo was developed. We modified the recent model from [1] in three ways. First, we narrowed down the spatial domain of the model by considering only the trunk region of blastoderm from 35% to 92% of embryo length along the A-P axis. This allowed us to combine the data on protein concentration from FlyEx database [2] and on mRNA concentration from SuperFly database [3] in the fitting procedure. Second, we used