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MotivationTranscription of a gene can be induced by the binding events of specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentration of the TFs, the quality of the transcription factor binding sites (TFBS) in the CRM and the properties of the TF itself (e.g. effectiveness, competitive interaction with other TFs). With the availability of an increasing number of detailed measurements of gene concentrations in different situations (e.g. tissues, developmental time points) as well as transcription factor DNA-binding preferences, it has become possible to build mathematical models for transcriptional regulation. Building mathematical models to associate a specific occupation of a specific CRM with an observed transcriptional response promotes a better understanding of the transcriptional regulation and enables _in-silico_ hypothesis-testing about postulated TFs or mechanisms. An increasingly successful approach to mathematically simulate transcriptional regulation are thermodynamic models, which model the interaction of TF and DNA using kinetic equations. Several thermodynamic models have been proposed in the last years [2,3,4]. These models take the CRM sequence, a set of TFs along with their concentration and predict the transcriptional response of the target gene as mediated by the CRM and the TFs. A training algorithm then optimizes the model's internal parameters to minimize the difference between the observed and predicted transcriptional response. STREAM currently uses the thermodynamic model introduced by Reinitz et al. [1], but the framework is flexible and can be used in conjunction with other models implemented in Java. STREAM offers several optimization methods including gradient descent and simulated annealing for adjusting the internal parameters of the model to best fit the user's input data. References(1) John Reinitz, Shuling Hou, and David H. Sharp. Transcriptional Control in Drosophila. Complexus, 1:54–64, 2003.(2) Hilde Janssens, Shuling Hou, Johannes Jaeger, Ah-Ram Kim, Ekaterina Myasnikova, David Sharp, and John Reinitz. Quantitative and predictive model of transcriptional control of the Drosophila melanogaster even skipped gene. Nat Genet, 38(10):1159-1165, Oct 2006. doi: 10.1038/ng1886. URL [http://dx.doi.org/10.1038/ng1886]. (3) Robert P Zinzen, Kate Senger, Mike Levine, and Dmitri Papatsenko. Computational models for neurogenic gene expression in the Drosophila embryo. Curr Biol, 16 (13):1358–1365, Jul 2006. doi: 10.1016/j.cub.2006.05.044. URL [http://dx.doi.org/10.1016/j.cub.2006.05.044]. (4) Eran Segal, Tali Raveh-Sadka, Mark Schroeder, Ulrich Unnerstall, and Ulrike Gaul. Predicting expression patterns from regulatory sequence in drosophila segmentation. Nature, Jan 2008. doi: 10.1038/nature06496. URL [http://dx.doi.org/10.1038/nature06496]. |