Literature DB >> 25677419

How to train your microbe: methods for dynamically characterizing gene networks.

Sebastian M Castillo-Hair1, Oleg A Igoshin2, Jeffrey J Tabor3.   

Abstract

Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.
Copyright © 2015 Elsevier Ltd. All rights reserved.

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Year:  2015        PMID: 25677419      PMCID: PMC4380544          DOI: 10.1016/j.mib.2015.01.008

Source DB:  PubMed          Journal:  Curr Opin Microbiol        ISSN: 1369-5274            Impact factor:   7.934


  122 in total

1.  A microfluidic chemostat for experiments with bacterial and yeast cells.

Authors:  Alex Groisman; Caroline Lobo; HoJung Cho; J Kyle Campbell; Yann S Dufour; Ann M Stevens; Andre Levchenko
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2.  A mechanism for cell cycle regulation of sporulation initiation in Bacillus subtilis.

Authors:  Jan-Willem Veening; Heath Murray; Jeff Errington
Journal:  Genes Dev       Date:  2009-08-15       Impact factor: 11.361

Review 3.  Microfluidic devices for measuring gene network dynamics in single cells.

Authors:  Matthew R Bennett; Jeff Hasty
Journal:  Nat Rev Genet       Date:  2009-08-11       Impact factor: 53.242

4.  A systems-level analysis of perfect adaptation in yeast osmoregulation.

Authors:  Dale Muzzey; Carlos A Gómez-Uribe; Jerome T Mettetal; Alexander van Oudenaarden
Journal:  Cell       Date:  2009-07-10       Impact factor: 41.582

5.  Tunable signal processing through modular control of transcription factor translocation.

Authors:  Nan Hao; Bogdan A Budnik; Jeremy Gunawardena; Erin K O'Shea
Journal:  Science       Date:  2013-01-25       Impact factor: 47.728

6.  A LOV2 domain-based optogenetic tool to control protein degradation and cellular function.

Authors:  Christian Renicke; Daniel Schuster; Svetlana Usherenko; Lars-Oliver Essen; Christof Taxis
Journal:  Chem Biol       Date:  2013-04-18

7.  Pulsed feedback defers cellular differentiation.

Authors:  Joe H Levine; Michelle E Fontes; Jonathan Dworkin; Michael B Elowitz
Journal:  PLoS Biol       Date:  2012-01-31       Impact factor: 8.029

8.  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

9.  Programming gene expression with combinatorial promoters.

Authors:  Robert Sidney Cox; Michael G Surette; Michael B Elowitz
Journal:  Mol Syst Biol       Date:  2007-11-13       Impact factor: 11.429

10.  Engineering light-inducible nuclear localization signals for precise spatiotemporal control of protein dynamics in living cells.

Authors:  Dominik Niopek; Dirk Benzinger; Julia Roensch; Thomas Draebing; Pierre Wehler; Roland Eils; Barbara Di Ventura
Journal:  Nat Commun       Date:  2014-07-14       Impact factor: 14.919

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  10 in total

1.  Probe discovery: Disentangling gene networks.

Authors:  Finbarr Hayes
Journal:  Nat Chem Biol       Date:  2016-01       Impact factor: 15.040

2.  High-throughput microfluidics to control and measure signaling dynamics in single yeast cells.

Authors:  Anders S Hansen; Nan Hao; Erin K O'Shea
Journal:  Nat Protoc       Date:  2015-07-09       Impact factor: 13.491

3.  Optogenetic Module for Dichromatic Control of c-di-GMP Signaling.

Authors:  Min-Hyung Ryu; Anastasia Fomicheva; Oleg V Moskvin; Mark Gomelsky
Journal:  J Bacteriol       Date:  2017-08-22       Impact factor: 3.490

4.  Nonlinear feedback drives homeostatic plasticity in H2O2 stress response.

Authors:  Youlian Goulev; Sandrine Morlot; Audrey Matifas; Bo Huang; Mikael Molin; Michel B Toledano; Gilles Charvin
Journal:  Elife       Date:  2017-04-18       Impact factor: 8.140

5.  A yeast optogenetic toolkit (yOTK) for gene expression control in Saccharomyces cerevisiae.

Authors:  Jidapas My An-Adirekkun; Cameron J Stewart; Stephanie H Geller; Michael T Patel; Justin Melendez; Benjamin L Oakes; Marcus B Noyes; Megan N McClean
Journal:  Biotechnol Bioeng       Date:  2019-12-18       Impact factor: 4.530

6.  A predictive model of gene expression reveals the role of network motifs in the mating response of yeast.

Authors:  Amy E Pomeroy; Matthew I Peña; John R Houser; Gauri Dixit; Henrik G Dohlman; Timothy C Elston; Beverly Errede
Journal:  Sci Signal       Date:  2021-02-16       Impact factor: 8.192

7.  Oscillatory stimuli differentiate adapting circuit topologies.

Authors:  Sahand Jamal Rahi; Johannes Larsch; Kresti Pecani; Alexander Y Katsov; Nahal Mansouri; Krasimira Tsaneva-Atanasova; Eduardo D Sontag; Frederick R Cross
Journal:  Nat Methods       Date:  2017-08-28       Impact factor: 28.547

8.  Optogenetic control of Bacillus subtilis gene expression.

Authors:  Sebastian M Castillo-Hair; Elliot A Baerman; Masaya Fujita; Oleg A Igoshin; Jeffrey J Tabor
Journal:  Nat Commun       Date:  2019-07-15       Impact factor: 14.919

9.  A photoconversion model for full spectral programming and multiplexing of optogenetic systems.

Authors:  Evan J Olson; Constantine N Tzouanas; Jeffrey J Tabor
Journal:  Mol Syst Biol       Date:  2017-04-24       Impact factor: 11.429

10.  Exploiting natural chemical photosensitivity of anhydrotetracycline and tetracycline for dynamic and setpoint chemo-optogenetic control.

Authors:  Armin Baumschlager; Marc Rullan; Mustafa Khammash
Journal:  Nat Commun       Date:  2020-07-31       Impact factor: 14.919

  10 in total

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