zbMATH — the first resource for mathematics

Transcription factor network reconstruction using the living cell array. (English) Zbl 1400.92218
Summary: The objective of identifying transcriptional regulatory networks is to provide insights as to what governs an organism’s long term response to external stimuli. We explore the coupling of the living cell array (LCA), a novel microfluidics device which utilizes fluorescence levels as a surrogate for transcription factor activity with reverse Euler deconvolution (RED) a computational technique proposed in this work to decipher the dynamics of the interactions. It is hypothesized that these two methods will allow us to first assess the underlying network architecture associated with the transcription factor network as well as specific mechanistic consequences of transcription factor activation such as receptor dimerization or tolerance.
The overall approach identifies evidence of time-lagged response which may be indicative of mechanisms such as receptor dimerization, tolerance mechanisms which are evidence of various receptor mediated dynamics, and feedback loops which regulate the response of an organism to changing environmental conditions. Furthermore, through the exploration of multiple network architectures, we were able to obtain insights as to the role each transcription factor plays in the overall response and their overall redundancy in the organism’s response to external perturbations. Thus, the LCA along with the proposed analysis technique is a valuable tool for identifying the possible architectures and mechanisms underlying the transcriptional response.
92C42 Systems biology, networks
92C55 Biomedical imaging and signal processing
gNCA; MatInspector
Full Text: DOI
[1] Boscolo, R., Sabatti, C., Liao, J.C., Roychowdhury, V., 2004. Reconstructing hidden regulatory layers by network component analysis: theory and application, ⟨http://www.ee.ucla.edu/%7Ericcardo/NCA/Boscolo-TCBB-0516.pdf⟩.
[2] Boulesteix, A.L.; Strimmer, K., Predicting transcription factor activities from combined analysis of microarray and chip data: a partial least squares approach, Theor. biol. med. model, 2, 23, (2005)
[3] Campbell, K.J.; Perkins, N.D., Post-translational modification of rela(p65) NF-kappab, Biochem. soc. trans., 32, 1087-1089, (2004)
[4] Cartharius, K.; Frech, K.; Grote, K.; Klocke, B.; Haltmeier, M.; Klingenhoff, A.; Frisch, M.; Bayerlein, M.; Werner, T., Matinspector and beyond: promoter analysis based on transcription factor binding sites, Bioinformatics, 21, 2933-2942, (2005)
[5] Cheng, Y., Church, G.M., 2000. Biclustering of expression data. In: Proceedings of the International Conference on Intelligent Systems for Molecular Biology, vol. 8, pp. 93-103.
[6] Dasika, M.S.; Gupta, A.; Maranas, C.D., A mixed integer linear programming (MILP) framework for inferring time delay in gene regulatory networks, Pac. symp. biocomput., 474-485, (2004)
[7] D’Haeseleer, P.; Wen, X.; Fuhrman, S.; Somogyi, R., Linear modeling of mrna expression levels during CNS development and injury, Pac. symp. biocomput., 41-52, (1999)
[8] Dudbridge, F.; Koeleman, B.P., Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies, Am. J. hum. genet., 75, 424-435, (2004)
[9] Dunn, S.M.; Constantinides, A.; Moghe, P.V., Numerical methods in biomedical engineering, (2006), Elsevier Academic Press Amsterdam, Boston
[10] Foteinou, P., Yang, E., Saharidis, G., Ierapetritou, M., Androulakis, I.., 2008. A mixed-integer optimization framework for the synthesis and analysis of regulatory networks. J. Global Optim., doi:10.1007/s10898-007-9266-6. · Zbl 1279.90119
[11] Frenkel, J.; Sherman, D.; Fein, A.; Schwartz, D.; Almog, N.; Kapon, A.; Goldfinger, N.; Rotter, V., Accentuated apoptosis in normally developing p53 knockout mouse embryos following genotoxic stress, Oncogene, 18, 2901-2907, (1999)
[12] Gao, F.; Foat, B.C.; Bussemaker, H.J., Defining transcriptional networks through integrative modeling of mrna expression and transcription factor binding data, BMC bioinformatics, 5, 31, (2004)
[13] Gardner, T.S.; di Bernardo, D.; Lorenz, D.; Collins, J.J., Inferring genetic networks and identifying compound mode of action via expression profiling, Science, 301, 102-105, (2003)
[14] Gealy, C.; Humphreys, C.; Dickinson, V.; Stinski, M.; Caswell, R., An activation-defective mutant of the human cytomegalovirus IE2p86 protein inhibits NF-kappab-mediated stimulation of the human interleukin-6 promoter, J. gen. virol., 88, 2435-2440, (2007)
[15] Goss, P.J.; Peccoud, J., Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets, Proc. natl. acad. sci. USA, 95, 6750-6755, (1998)
[16] Guthke, R.; Moller, U.; Hoffmann, M.; Thies, F.; Topfer, S., Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection, Bioinformatics, 21, 1626-1634, (2005)
[17] Harbison, C.T.; Gordon, D.B.; Lee, T.I.; Rinaldi, N.J.; Macisaac, K.D.; Danford, T.W.; Hannett, N.M.; Tagne, J.B.; Reynolds, D.B.; Yoo, J.; Jennings, E.G.; Zeitlinger, J.; Pokholok, D.K.; Kellis, M.; Rolfe, P.A.; Takusagawa, K.T.; Lander, E.S.; Gifford, D.K.; Fraenkel, E.; Young, R.A., Transcriptional regulatory code of a eukaryotic genome, Nature, 431, 99-104, (2004)
[18] Hartemink, A.J., Reverse engineering gene regulatory networks, Nat. biotechnol., 23, 554-555, (2005)
[19] Hatzigeorgiou, D.E.; He, S.; Sobel, J.; Grabstein, K.H.; Hafner, A.; Ho, J.L., IL-6 down-modulates the cytokine-enhanced antileishmanial activity in human macrophages, J. immunol., 151, 3682-3692, (1993)
[20] Haverty, P.M.; Hansen, U.; Weng, Z., Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification, Nucleic acids res., 32, 179-188, (2004)
[21] Hemberg, M.; Barahona, M., Perfect sampling of the master equation for gene regulatory networks, Biophys. J., 93, 401-410, (2007)
[22] Hoffmann, A.; Levchenko, A.; Scott, M.L.; Baltimore, D., The ikappab-NF-kappab signaling module: temporal control and selective gene activation, Science, 298, 1241-1245, (2002)
[23] Kao, K.C.; Yang, Y.L.; Liao, J.C.; Boscolo, R.; Sabatti, C.; Roychowdhury, V., Network component analysis of Escherichia coli transcriptional regulation, Abstr. pap. am. chem. soc., 227, U216-U217, (2004)
[24] Kao, K.C.; Yang, Y.L.; Boscolo, R.; Sabatti, C.; Roychowdhury, V.; Liao, J.C., Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis, Proc. natl. acad. sci. USA, 101, 641-646, (2004)
[25] Kauffman, S.; Peterson, C.; Samuelsson, B.; Troein, C., Random Boolean network models and the yeast transcriptional network, Proc. natl. acad. sci. USA, 100, 14796-14799, (2003)
[26] King, K.R.; Wang, S.; Irimia, D.; Jayaraman, A.; Toner, M.; Yarmush, M.L., A high-throughput microfluidic real-time gene expression living cell array, Lab chip, 7, 77-85, (2007)
[27] King, K.R.; Wang, S.; Jayaraman, A.; Yarmush, M.L.; Toner, M., Microfluidic flow-encoded switching for parallel control of dynamic cellular microenvironments, Lab chip, 8, 107-116, (2008)
[28] Kluger, Y.; Basri, R.; Chang, J.T.; Gerstein, M., Spectral biclustering of microarray data: coclustering genes and conditions, Genome. res., 13, 703-716, (2003)
[29] Kyrmizi, I.; Hatzis, P.; Katrakili, N.; Tronche, F.; Gonzalez, F.J.; Talianidis, I., Plasticity and expanding complexity of the hepatic transcription factor network during liver development, Genes dev., 20, 2293-2305, (2006)
[30] Lee, T.I.; Rinaldi, N.J.; Robert, F.; Odom, D.T.; Bar-Joseph, Z.; Gerber, G.K.; Hannett, N.M.; Harbison, C.T.; Thompson, C.M.; Simon, I.; Zeitlinger, J.; Jennings, E.G.; Murray, H.L.; Gordon, D.B.; Ren, B.; Wyrick, J.J.; Tagne, J.B.; Volkert, T.L.; Fraenkel, E.; Gifford, D.K.; Young, R.A., Transcriptional regulatory networks in saccharomyces cerevisiae, Science, 298, 799-804, (2002)
[31] Leva, A.; Piroddi, L., Model-specific autotuning of classical regulators: a neural approach to structural identification, Control eng. pract., 4, 1381-1391, (1996)
[32] Levy, D.E., Interferon induction of gene expression through the jak-stat pathway, Semin. virol., 6, 181-189, (1995)
[33] Liao, J.C.; Boscolo, R.; Yang, Y.L.; Tran, L.M.; Sabatti, C.; Roychowdhury, V.P., Network component analysis: reconstruction of regulatory signals in biological systems, Proc. natl. acad. sci. USA, 100, 15522-15527, (2003)
[34] Mangan, S.; Alon, U., Structure and function of the feed-forward loop network motif, Proc. natl. acad. sci. USA, 100, 11980-11985, (2003)
[35] Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U., Network motifs: simple building blocks of complex networks, Science, 298, 824-827, (2002)
[36] Mjolsness, E.; Sharp, D.H.; Reinitz, J., A connectionist model of development, J. theor. biol., 152, 429-453, (1991)
[37] Nelson, D.E.; See, V.; Nelson, G.; White, M.R., Oscillations in transcription factor dynamics: a new way to control gene expression, Biochem. soc. trans., 32, 1090-1092, (2004)
[38] Piroddi, L.; Leva, A., Step response classification for model-based autotuning via polygonal curve approximation, J. process control, 17, 641-652, (2007)
[39] Rao, C.V.; Arkin, A.P., Control motifs for intracellular regulatory networks, Annu. rev. biomed. eng., 3, 391-419, (2001)
[40] Rice, J.; Rosenblatt, M., Smoothing splines: regression, derivatives and deconvolution, Ann. stat., 11, 141-156, (1983) · Zbl 0535.41019
[41] Saklatvala, J.; Kaur, P.; Guesdon, F., Phosphorylation of the small heat-shock protein is regulated by interleukin 1, tumour necrosis factor, growth factors, bradykinin and ATP, Biochem. J., 277, Pt 3, 635-642, (1991)
[42] Samet, J.M.; Silbajoris, R.; Huang, T.; Jaspers, I., Transcription factor activation following exposure of an intact lung preparation to metallic particulate matter, Environ. health perspect., 110, 985-990, (2002)
[43] Sass, G.; Koerber, K.; Tiegs, G., TNF tolerance and cytotoxicity in the liver: the role of interleukin-1beta, inducible nitric oxide-synthase and heme oxygenase-1 in \scd-galactosamine-sensitized mice, Inflammation res., 51, 229-235, (2002)
[44] Schmitt, W.A.; Raab, R.M.; Stephanopoulos, G., Elucidation of gene interaction networks through time-lagged correlation analysis of transcriptional data, Genome. res., 14, 1654-1663, (2004)
[45] Segal, E.; Shapira, M.; Regev, A.; Pe’er, D.; Botstein, D.; Koller, D.; Friedman, N., Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nat. genet., 34, 166-176, (2003)
[46] Shmulevich, I.; Dougherty, E.R.; Kim, S.; Zhang, W., Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks, Bioinformatics, 18, 261-274, (2002)
[47] Somorjai, R.L.; Dolenko, B.; Baumgartner, R., Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, caveats, cautions, Bioinformatics, 19, 1484-1491, (2003)
[48] Thompson, D.A.; King, K.R.; Wieder, J.; Toner, M.; Yarmush, M.L.; Jayaraman, A., Dynamic gene expression profiling using a microfabricated living cell array, Ann. chem., 76, 4098-4103, (2004)
[49] Thompson, D.M.; King, K.R.; Wieder, K.J.; Toner, M.; Yarmush, M.L.; Jayaraman, A., Dynamic gene expression profiling using a microfabricated living cell array, Anal. chem., 76, 4098-4103, (2004)
[50] Tootle, T.L.; Rebay, I., Post-translational modifications influence transcription factor activity: a view from the ETS superfamily, Bioessays, 27, 285-298, (2005)
[51] Tran, L.M.; Brynildsen, M.P.; Kao, K.C.; Suen, J.K.; Liao, J.C., Gnca: a framework for determining transcription factor activity based on transcriptome: identifiability and numerical implementation, Metab. eng., 7, 128-141, (2005)
[52] Van Someren, E.P., Wessels, L.F.A., Reinders, M.J.T., Baker, E., 2001. Searching for limited connectivity in genetic network models. In: Proceedings of the International Conference on Systems Biology, Pasadena, CA 2001.
[53] Wieder, K.J.; King, K.R.; Thompson, D.M.; Zia, C.; Yarmush, M.L.; Jayaraman, A., Optimization of reporter cells for expression profiling in a microfluidic device, Biomed. microdevices, 7, 213-222, (2005)
[54] Xie, Y.; Chen, C.; Stevenson, M.A.; Auron, P.E.; Calderwood, S.K., Heat shock factor 1 represses transcription of the IL-1beta gene through physical interaction with the nuclear factor of interleukin 6, J. biol. chem., 277, 11802-11810, (2002)
[55] Yamada, Y.; Kirillova, I.; Peschon, J.J.; Fausto, N., Initiation of liver growth by tumor necrosis factor: deficient liver regeneration in mice lacking type I tumor necrosis factor receptor, Proc. natl. acad. sci. USA, 94, 1441-1446, (1997)
[56] Yang, E.; Foteinou, P.T.; King, K.R.; Yarmush, M.L.; Androulakis, I.P., A novel non-overlapping bi-clustering algorithm for network generation using living cell array data, Bioinformatics, 23, 2306-2313, (2007)
[57] Yoon, S.; Nardini, C.; Benini, L.; De Micheli, G., Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams, IEEE/ACM trans. comput. biol. bioinformatics, 2, 339-354, (2005)
[58] Zhu, X.; Gerstein, M.; Snyder, M., Getting connected: analysis and principles of biological networks, Genes. dev., 21, 1010-1024, (2007)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.