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Microsoft word - document.doc

Expression Variability from a Tet-inducible Positive Feedback Network in Mammalian Cells Diane M. Longo1, Alexander Hoffmann2, Lev S. Tsimring1,3, and Jeff Hasty1,3 experimentally observed gene expression variability and to Short Abstract — The stochastic nature of gene expression generate predictions that can be tested with further can result in significant cell-to-cell variability in protein levels. Here, we examine gene expression variability from a synthetic gene network in a mammalian system. We have constructed a synthetic mammalian positive feedback circuit in which a tetracycline-regulated transactivator (rtTA) induces its own We have constructed a synthetic gene network consisting synthesis in the presence of tetracycline. We analyze of an autoregulatory vector which contains the rtTA gene interclonal and intraclonal variability in gene expression from downstream of the O7-CMVm tetracycline-inducible several clonal cell lines transduced with the positive feedback promoter and a reporter vector [9] containing the sequence network. Finally, we develop a stochastic model of the for GFP downstream of the O7-CMVm promoter. engineered circuit to investigate the origin of variability in expression from the engineered mammalian gene network. autoregulatory vector and the GFP reporter vector into mouse embryonic fibroblasts (3T3 cells). Following Keywords — gene regulation, noise, synthetic biology selection, infected cells were cultured in doxycycline and cells with induced GFP expression levels were single-cell sorted and expanded into clonal lines. Flow cytometry was oise in gene expression can cause variability in used to measure GFP expression levels from inducible cell N genetically identical cell populations that have been lines cultured in a range of doxycycline concentrations. exposed to the same environment and have the same We have developed a stochastic model of the positive history [1]. Several studies have investigated the origins and feedback circuit which uses the Gillespie algorithm [10] to consequences of gene expression noise in model organisms simulate the behavior of the biochemical reactions involved such as the prokaryote E. coli and the eukaryote S. in the synthetic positive feedback network. cerevisiae [2-4]. Simple synthetic gene regulatory networks have been utilized to enable a quantitative analysis of gene expression noise in E. coli [5] and S. cerevisiae [6]. The recent development of inducible mammalian transgene We conclude with testable model predictions for the control systems has allowed for the construction of synthetic behavior of the engineered mammalian network. gene circuits in mammalian systems [7]. Thus, it is now possible to design synthetic mammalian gene networks that can facilitate a quantitative analysis of gene expression [1] Raser JM, O’Shea EK (2005) Noise in gene expression: origins, variability in mammalian systems. For example, a recent consequences, and control. Science 309, 2010-2013. study has utilized a tetracycline-regulatable control system Elowitz MB et al. (2002) Stochastic gene expression in a single cell. to investigate cell-to-cell variation in gene expression in [3] Raser JM, O’Shea EK (2004) Control of stochasticity in eukaryotic gene mammalian cells by quantifying mRNA levels in individual [4] Volfson D et al. (2005) Origins of extrinsic variability in eukaryotic gene Here, we construct a synthetic tetracycline-regulatable [5] Hooshangi S, Thiberge S, Weiss R (2005) Ultrasensitivity and noise positive feedback network in a mammalian system and propagation in a synthetic transcriptional cascade. PNAS 102, 3581-3586. perform quantitative single-cell gene expression assays to [6] Becskei A, Serrano L (2000) Engineering stability in gene networks by examine intraclonal and interclonal variability for several [7] Greber D, Fussenegger M (2007) Mammalian synthetic biology: mammalian cell lines containing the engineered gene engineering of sophisticated gene networks. J Biotechnol 130, 329-345. network. We develop a stochastic model of the positive [8] Raj A et al. (2006) Stochastic mRNA synthesis in mammalian cells. PLoS feedback circuit to investigate potential sources of the [9] Rossi FM, et al. (1998) Tetracycline-regulatable factors with distinct dimerization domains allow reversible growth inhibition by p16. Nat Genet 1Department of Bioengineering, University of California San Diego, La [10] Gillespie DT (1977) Exact stochastic simulation of coupled chemical 2Department of Biochemistry, University of California San Diego, La Jolla, California 92093, USA. E-mail: ahoffmann@ucsd.edu 3Institute for Nonlinear Science, University of California San Diego, La Jolla, California 92093, USA. E-mail: ltsimring@ucsd.edu

Source: http://q-bio.org/w/images/9/97/Poster08-longo.pdf

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2012 PROGRAM SCHEDULES October 23, 2011 (Sunday) October 24, 2011 (Monday), Academic Hall, Ren Min University Opening Ceremony Chairs: G.S. Bañuelos & X.B. Yin Guest Speech The Functional Agriculture in China: Present and Future Chinese Academy of Sciences, China Session 1 Selenium in Soils and the Need for Biofortification of CropsSources and Transformations of Selen

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