Literature DB >> 19628860

Transcriptional regulatory circuits: predicting numbers from alphabets.

Harold D Kim1, Tal Shay, Erin K O'Shea, Aviv Regev.   

Abstract

Transcriptional regulatory circuits govern how cis and trans factors transform signals into messenger RNA (mRNA) expression levels. With advances in quantitative and high-throughput technologies that allow measurement of gene expression state in different conditions, data that can be used to build and test models of transcriptional regulation is being generated at a rapid pace. Here, we review experimental and computational methods used to derive detailed quantitative circuit models on a small scale and cruder, genome-wide models on a large scale. We discuss the potential of combining small- and large-scale approaches to understand the working and wiring of transcriptional regulatory circuits.

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Year:  2009        PMID: 19628860      PMCID: PMC2745280          DOI: 10.1126/science.1171347

Source DB:  PubMed          Journal:  Science        ISSN: 0036-8075            Impact factor:   47.728


  63 in total

1.  A yeast hybrid provides insight into the evolution of gene expression regulation.

Authors:  Itay Tirosh; Sharon Reikhav; Avraham A Levy; Naama Barkai
Journal:  Science       Date:  2009-05-01       Impact factor: 47.728

2.  High-resolution DNA-binding specificity analysis of yeast transcription factors.

Authors:  Cong Zhu; Kelsey J R P Byers; Rachel Patton McCord; Zhenwei Shi; Michael F Berger; Daniel E Newburger; Katrina Saulrieta; Zachary Smith; Mita V Shah; Mathangi Radhakrishnan; Anthony A Philippakis; Yanhui Hu; Federico De Masi; Marcin Pacek; Andreas Rolfs; Tal Murthy; Joshua Labaer; Martha L Bulyk
Journal:  Genome Res       Date:  2009-01-21       Impact factor: 9.043

3.  The DNA-encoded nucleosome organization of a eukaryotic genome.

Authors:  Noam Kaplan; Irene K Moore; Yvonne Fondufe-Mittendorf; Andrea J Gossett; Desiree Tillo; Yair Field; Emily M LeProust; Timothy R Hughes; Jason D Lieb; Jonathan Widom; Eran Segal
Journal:  Nature       Date:  2008-12-17       Impact factor: 49.962

4.  A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

Authors:  Irene Cantone; Lucia Marucci; Francesco Iorio; Maria Aurelia Ricci; Vincenzo Belcastro; Mukesh Bansal; Stefania Santini; Mario di Bernardo; Diego di Bernardo; Maria Pia Cosma
Journal:  Cell       Date:  2009-03-26       Impact factor: 41.582

5.  Diversity-based, model-guided construction of synthetic gene networks with predicted functions.

Authors:  Tom Ellis; Xiao Wang; James J Collins
Journal:  Nat Biotechnol       Date:  2009-04-19       Impact factor: 54.908

6.  Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.

Authors:  Chun Ye; Simon J Galbraith; James C Liao; Eleazar Eskin
Journal:  PLoS Comput Biol       Date:  2009-03-20       Impact factor: 4.475

7.  The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line.

Authors:  Harukazu Suzuki; Alistair R R Forrest; Erik van Nimwegen; Carsten O Daub; Piotr J Balwierz; Katharine M Irvine; Timo Lassmann; Timothy Ravasi; Yuki Hasegawa; Michiel J L de Hoon; Shintaro Katayama; Kate Schroder; Piero Carninci; Yasuhiro Tomaru; Mutsumi Kanamori-Katayama; Atsutaka Kubosaki; Altuna Akalin; Yoshinari Ando; Erik Arner; Maki Asada; Hiroshi Asahara; Timothy Bailey; Vladimir B Bajic; Denis Bauer; Anthony G Beckhouse; Nicolas Bertin; Johan Björkegren; Frank Brombacher; Erika Bulger; Alistair M Chalk; Joe Chiba; Nicole Cloonan; Adam Dawe; Josee Dostie; Pär G Engström; Magbubah Essack; Geoffrey J Faulkner; J Lynn Fink; David Fredman; Ko Fujimori; Masaaki Furuno; Takashi Gojobori; Julian Gough; Sean M Grimmond; Mika Gustafsson; Megumi Hashimoto; Takehiro Hashimoto; Mariko Hatakeyama; Susanne Heinzel; Winston Hide; Oliver Hofmann; Michael Hörnquist; Lukasz Huminiecki; Kazuho Ikeo; Naoko Imamoto; Satoshi Inoue; Yusuke Inoue; Ryoko Ishihara; Takao Iwayanagi; Anders Jacobsen; Mandeep Kaur; Hideya Kawaji; Markus C Kerr; Ryuichiro Kimura; Syuhei Kimura; Yasumasa Kimura; Hiroaki Kitano; Hisashi Koga; Toshio Kojima; Shinji Kondo; Takeshi Konno; Anders Krogh; Adele Kruger; Ajit Kumar; Boris Lenhard; Andreas Lennartsson; Morten Lindow; Marina Lizio; Cameron Macpherson; Norihiro Maeda; Christopher A Maher; Monique Maqungo; Jessica Mar; Nicholas A Matigian; Hideo Matsuda; John S Mattick; Stuart Meier; Sei Miyamoto; Etsuko Miyamoto-Sato; Kazuhiko Nakabayashi; Yutaka Nakachi; Mika Nakano; Sanne Nygaard; Toshitsugu Okayama; Yasushi Okazaki; Haruka Okuda-Yabukami; Valerio Orlando; Jun Otomo; Mikhail Pachkov; Nikolai Petrovsky; Charles Plessy; John Quackenbush; Aleksandar Radovanovic; Michael Rehli; Rintaro Saito; Albin Sandelin; Sebastian Schmeier; Christian Schönbach; Ariel S Schwartz; Colin A Semple; Miho Sera; Jessica Severin; Katsuhiko Shirahige; Cas Simons; George St Laurent; Masanori Suzuki; Takahiro Suzuki; Matthew J Sweet; Ryan J Taft; Shizu Takeda; Yoichi Takenaka; Kai Tan; Martin S Taylor; Rohan D Teasdale; Jesper Tegnér; Sarah Teichmann; Eivind Valen; Claes Wahlestedt; Kazunori Waki; Andrew Waterhouse; Christine A Wells; Ole Winther; Linda Wu; Kazumi Yamaguchi; Hiroshi Yanagawa; Jun Yasuda; Mihaela Zavolan; David A Hume; Takahiro Arakawa; Shiro Fukuda; Kengo Imamura; Chikatoshi Kai; Ai Kaiho; Tsugumi Kawashima; Chika Kawazu; Yayoi Kitazume; Miki Kojima; Hisashi Miura; Kayoko Murakami; Mitsuyoshi Murata; Noriko Ninomiya; Hiromi Nishiyori; Shohei Noma; Chihiro Ogawa; Takuma Sano; Christophe Simon; Michihira Tagami; Yukari Takahashi; Jun Kawai; Yoshihide Hayashizaki
Journal:  Nat Genet       Date:  2009-04-19       Impact factor: 38.330

8.  Bead-based profiling of tyrosine kinase phosphorylation identifies SRC as a potential target for glioblastoma therapy.

Authors:  Jinyan Du; Paula Bernasconi; Karl R Clauser; D R Mani; Stephen P Finn; Rameen Beroukhim; Melissa Burns; Bina Julian; Xiao P Peng; Haley Hieronymus; Rebecca L Maglathlin; Timothy A Lewis; Linda M Liau; Phioanh Nghiemphu; Ingo K Mellinghoff; David N Louis; Massimo Loda; Steven A Carr; Andrew L Kung; Todd R Golub
Journal:  Nat Biotechnol       Date:  2008-12-21       Impact factor: 54.908

9.  Environment-specific combinatorial cis-regulation in synthetic promoters.

Authors:  Jason Gertz; Barak A Cohen
Journal:  Mol Syst Biol       Date:  2009-02-17       Impact factor: 11.429

10.  Analysis of combinatorial cis-regulation in synthetic and genomic promoters.

Authors:  Jason Gertz; Eric D Siggia; Barak A Cohen
Journal:  Nature       Date:  2008-11-23       Impact factor: 49.962

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  87 in total

1.  Bacteria determine fate by playing dice with controlled odds.

Authors:  Eshel Ben-Jacob; Daniel Schultz
Journal:  Proc Natl Acad Sci U S A       Date:  2010-07-21       Impact factor: 11.205

2.  Biological role of noise encoded in a genetic network motif.

Authors:  Mark Kittisopikul; Gürol M Süel
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-28       Impact factor: 11.205

Review 3.  Experimental strategies for studying transcription factor-DNA binding specificities.

Authors:  Marcel Geertz; Sebastian J Maerkl
Journal:  Brief Funct Genomics       Date:  2010-09-23       Impact factor: 4.241

Review 4.  Systems vaccinology: learning to compute the behavior of vaccine induced immunity.

Authors:  Helder I Nakaya; Shuzhao Li; Bali Pulendran
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2011-10-19

Review 5.  Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data.

Authors:  Ming Wu; Christina Chan
Journal:  Brief Bioinform       Date:  2011-05-26       Impact factor: 11.622

6.  Towards a dynamical network view of brain ischemia and reperfusion. Part II: a post-ischemic neuronal state space.

Authors:  Donald J Degracia
Journal:  J Exp Stroke Transl Med       Date:  2010

7.  Evidence that intraspecific trait variation among nasal bacteria shapes the distribution of Staphylococcus aureus.

Authors:  Ben Libberton; Rosanna E Coates; Michael A Brockhurst; Malcolm J Horsburgh
Journal:  Infect Immun       Date:  2014-06-30       Impact factor: 3.441

Review 8.  Next-generation sequencing in aging research: emerging applications, problems, pitfalls and possible solutions.

Authors:  João Pedro de Magalhães; Caleb E Finch; Georges Janssens
Journal:  Ageing Res Rev       Date:  2009-11-10       Impact factor: 10.895

9.  Towards a rigorous assessment of systems biology models: the DREAM3 challenges.

Authors:  Robert J Prill; Daniel Marbach; Julio Saez-Rodriguez; Peter K Sorger; Leonidas G Alexopoulos; Xiaowei Xue; Neil D Clarke; Gregoire Altan-Bonnet; Gustavo Stolovitzky
Journal:  PLoS One       Date:  2010-02-23       Impact factor: 3.240

10.  A top-performing algorithm for the DREAM3 gene expression prediction challenge.

Authors:  Jianhua Ruan
Journal:  PLoS One       Date:  2010-02-04       Impact factor: 3.240

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