Literature DB >> 26086470

From analog to digital models of gene regulation.

Brian Munsky1, Gregor Neuert.   

Abstract

Recently, major progress has been made to develop computational models to predict and explain the mechanisms and behaviors of gene regulation. Here, we review progress on how these mechanisms and behaviors have been interpreted with analog models, where cell properties continuously modulate transcription, and digital models, where gene modulation involves discrete activation and inactivation events. We introduce recent experimental approaches, which measure these gene regulatory behaviors at single-cell and single-molecule resolution, and we discuss the integration of these approaches with computational models to reveal biophysical insight. By analyzing simple toy models in the context of existing experimental capabilities, we discuss the interplay between different experiments and different models to measure and interpret gene regulatory behaviors. Finally, we review recent successes in the development of predictive computational models for the control of gene regulation behaviors.

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Year:  2015        PMID: 26086470      PMCID: PMC4591055          DOI: 10.1088/1478-3975/12/4/045004

Source DB:  PubMed          Journal:  Phys Biol        ISSN: 1478-3967            Impact factor:   2.583


  63 in total

1.  Linking stochastic dynamics to population distribution: an analytical framework of gene expression.

Authors:  Nir Friedman; Long Cai; X Sunney Xie
Journal:  Phys Rev Lett       Date:  2006-10-19       Impact factor: 9.161

Review 2.  Collecting and organizing systematic sets of protein data.

Authors:  John G Albeck; Gavin MacBeath; Forest M White; Peter K Sorger; Douglas A Lauffenburger; Suzanne Gaudet
Journal:  Nat Rev Mol Cell Biol       Date:  2006-11       Impact factor: 94.444

Review 3.  Analyzing protein interaction networks using structural information.

Authors:  Christina Kiel; Pedro Beltrao; Luis Serrano
Journal:  Annu Rev Biochem       Date:  2008       Impact factor: 23.643

4.  Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells.

Authors:  Yuichi Taniguchi; Paul J Choi; Gene-Wei Li; Huiyi Chen; Mohan Babu; Jeremy Hearn; Andrew Emili; X Sunney Xie
Journal:  Science       Date:  2010-07-30       Impact factor: 47.728

Review 5.  Single-cell technologies sharpen up mammalian stem cell research.

Authors:  Philipp S Hoppe; Daniel L Coutu; Timm Schroeder
Journal:  Nat Cell Biol       Date:  2014-10       Impact factor: 28.824

Review 6.  A decade of systems biology.

Authors:  Han-Yu Chuang; Matan Hofree; Trey Ideker
Journal:  Annu Rev Cell Dev Biol       Date:  2010       Impact factor: 13.827

7.  Single yeast cells vary in transcription activity not in delay time after a metabolic shift.

Authors:  Anne Schwabe; Frank J Bruggeman
Journal:  Nat Commun       Date:  2014-09-02       Impact factor: 14.919

Review 8.  Identifying and mitigating bias in next-generation sequencing methods for chromatin biology.

Authors:  Clifford A Meyer; X Shirley Liu
Journal:  Nat Rev Genet       Date:  2014-09-16       Impact factor: 53.242

9.  Frequency-modulated nuclear localization bursts coordinate gene regulation.

Authors:  Long Cai; Chiraj K Dalal; Michael B Elowitz
Journal:  Nature       Date:  2008-09-25       Impact factor: 49.962

10.  Accurate prediction of gene feedback circuit behavior from component properties.

Authors:  Nitzan Rosenfeld; Jonathan W Young; Uri Alon; Peter S Swain; Michael B Elowitz
Journal:  Mol Syst Biol       Date:  2007-11-13       Impact factor: 11.429

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  5 in total

1.  Identification of gene regulation models from single-cell data.

Authors:  Lisa Weber; William Raymond; Brian Munsky
Journal:  Phys Biol       Date:  2018-05-18       Impact factor: 2.583

2.  Boolean gene regulatory network model of centromere function in Saccharomyces cerevisiae.

Authors:  Emir Haliki; Nursen Alpagut Keskin; Ozgur Masalci
Journal:  J Biol Phys       Date:  2019-06-07       Impact factor: 1.365

Review 3.  Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.

Authors:  Brian Munsky; Zachary Fox; Gregor Neuert
Journal:  Methods       Date:  2015-06-12       Impact factor: 3.608

4.  Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells.

Authors:  Guoliang Li; Gregor Neuert
Journal:  Sci Data       Date:  2019-06-17       Impact factor: 6.444

5.  Haematopoietic stem cells: entropic landscapes of differentiation.

Authors:  K Wiesner; J Teles; M Hartnor; C Peterson
Journal:  Interface Focus       Date:  2018-10-19       Impact factor: 3.906

  5 in total

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