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  • Computational eco‐systems biology in Tara Oceans: translating data into knowledge
    <div xmlns="http://www.w3.org/1999/xhtml">Computational eco‐systems biology in <em>Tara </em>Oceans: translating data into knowledge</div>
    1. Shinichi Sunagawa1,
    2. Eric Karsenti1,2,
    3. Chris Bowler2 and
    4. Peer Bork (bork{at}embl.de) 1,3
    1. 1European Molecular Biology Laboratory, Heidelberg, Germany
    2. 2Ecole Normale Supérieure Institut de Biologie de l'ENS (IBENS), and Inserm U1024, and CNRS UMR 8197, Paris, France
    3. 3Max‐Delbrück‐Centre for Molecular Medicine, Berlin, Germany

    The computational analysis of the data collected by Tara Oceans represents a formidable challenge that will push the boundaries of our understanding of ecosystems at a planetary scale.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Shinichi Sunagawa, Eric Karsenti, Chris Bowler, Peer Bork
  • The making of Tara Oceans: funding blue skies research for our Blue Planet
    <div xmlns="http://www.w3.org/1999/xhtml">The making of <em>Tara</em> Oceans: funding blue skies research for our Blue Planet</div>
    1. Eric Karsenti (karsenti{at}embl-heidelberg.de) 1
    1. 1 Scientific Director Tara Oceans

    Transforming a romantic idea into the large‐scale Tara Oceans project was made possible by risk‐taking funders that provided seed support to a self‐organized community of highly committed researchers.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Eric Karsenti
  • Genome‐wide study of mRNA degradation and transcript elongation in Escherichia coli
    <div xmlns="http://www.w3.org/1999/xhtml">Genome‐wide study of mRNA degradation and transcript elongation in <em>Escherichia coli</em></div>
    Huiyi Chen, Katsuyuki Shiroguchi, Hao Ge, Xiaoliang Sunney Xie
  • Fractional killing arises from cell‐to‐cell variability in overcoming a caspase activity threshold
    Fractional killing arises from cell‐to‐cell variability in overcoming a caspase activity threshold
    1. Jérémie Roux13,
    2. Marc Hafner1,,
    3. Samuel Bandara1,
    4. Joshua J Sims1,
    5. Hannah Hudson2,
    6. Diana Chai2 and
    7. Peter K Sorger*,1
    1. 1Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    2. 2Merrimack Pharmaceuticals, Cambridge, MA, USA
    3. 3Faculté de Médecine, Institute for Research on Cancer and Aging Nice (IRCAN) ‐ CNRS UMR7284 INSERM U1081 Université de Nice‐Sophia Antipolis, Nice, France
    1. *Corresponding author. Tel: +1 617 432 6901; E‐mail: peter_sorger{at}hms.harvard.edu
    1. These authors contributed equally to this work

    Non‐genetic cell‐to‐cell variability results in fractional killing by TRAIL and therapeutic antibody agonists, limiting their effectiveness as anti‐cancer drugs. A simple model of initiator caspase dynamics reveals a threshold in caspase activity that separates dying and surviving cells.

    Synopsis

    Non‐genetic cell‐to‐cell variability results in fractional killing by TRAIL and therapeutic antibody agonists, limiting their effectiveness as anti‐cancer drugs. A simple model of initiator caspase dynamics reveals a threshold in caspase activity that separates dying and surviving cells.

    • A model of initiator caspase activity is predictive of fractional killing by TRAIL.

    • The model identifies a caspase activity threshold that is constant across dose and type of agonist.

    • Therapeutic antibodies targeting TRAIL receptors kill poorly because this threshold is not crossed.

    • The caspase activity threshold and classic MOMP threshold interact to determine cellular sensitivity to apoptosis inducers.

    • anti‐cancer therapeutic antibodies
    • apoptosis
    • DR4, DR5 receptors
    • programmed cell death
    • TRAIL

    Mol Syst Biol. (2015) 11: 803

    • Received July 15, 2014.
    • Revision received March 17, 2015.
    • Accepted March 27, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Jérémie Roux, Marc Hafner, Samuel Bandara, Joshua J Sims, Hannah Hudson, Diana Chai, Peter K Sorger
  • Tuning noise in gene expression
    Tuning noise in gene expression
    1. Sanjay Tyagi (sanjay.tyagi{at}rutgers.edu) 1
    1. 1Public Health Research Institute, Rutgers University, Newark, NJ, USA

    The relative contribution of promoter architecture and the associated chromatin environment in regulating gene expression noise has remained elusive. In their recent work, Arkin, Schaffer and colleagues (Dey et al, 2015) show that mean expression and noise for a given promoter at different genomic loci are uncorrelated and influenced by the local chromatin environment.

    See also: SS Dey et al (May 2015)

    The relative contribution of promoter architecture and the associated chromatin environment in regulating gene expression noise has remained elusive. Arkin, Schaffer and colleagues (Dey et al, 2015) show that mean expression and noise are uncorrelated and influenced by the chromatin environment.

    Mol Syst Biol. (2015) 11: 805

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Sanjay Tyagi
  • Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
    Orthogonal control of expression mean and variance by epigenetic features at different genomic loci
    1. Siddharth S Dey1,28,
    2. Jonathan E Foley3,,
    3. Prajit Limsirichai4,
    4. David V Schaffer*,1,2,3,5 and
    5. Adam P Arkin*,3,5,6,7
    1. 1Department of Chemical and Biomolecular Engineering and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
    2. 2Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
    3. 3Department of Bioengineering, University of California, Berkeley, CA, USA
    4. 4Department of Plant and Microbial Biology, University of California, Berkeley, CA, USA
    5. 5Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    6. 6Virtual Institute of Microbial Stress and Survival, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    7. 7DOE, Joint BioEnergy Institute Lawrence Berkeley National Laboratory, Berkeley, CA, USA
    8. 8Hubrecht Institute‐KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Cancer Genomics Netherlands, CT Utrecht, the Netherlands
    1. * Corresponding author. Tel: +1 510 643 5963; Fax: +1 510 642 4778; Email: schaffer{at}berkeley.edu

      Corresponding author. Tel: +1 510 495 2366; Fax: +1 510 486 6059; E‐mail: aparkin{at}lbl.gov

    1. These authors contributed equally to this work

    Analyses of the molecular basis of gene expression noise by smFISH and flow cytometry show that in mammalian cells, mean expression and noise are uncorrelated across genomic locations and are affected by the local chromatin environment.

    Synopsis

    Analyses of the molecular basis of gene expression noise by smFISH and flow cytometry show that in mammalian cells, mean expression and noise are uncorrelated across genomic locations and are affected by the local chromatin environment.

    • Using a dual‐reporter lentiviral system, the influence of the promoter sequence is deconvolved to systematically study how the chromatin environment regulates gene expression noise.

    • Analysis of 418 single‐integration clones reveals that the mean expression is uncorrelated with the coefficient of variation (CV).

    • Single‐molecule mRNA FISH distributions are fit to a two‐state model of gene expression to show orthogonal control of mean expression by burst size and gene expression noise (CV) by burst frequency.

    • DNase I sensitivity assays reveal that promoters within more repressed chromatin are associated with reduced burst frequency and increased gene expression noise.

    • chromatin environment
    • gene expression noise
    • single‐cell biology
    • single‐molecule RNA FISH

    Mol Syst Biol. (2015) 11: 806

    • Received August 19, 2014.
    • Revision received March 25, 2015.
    • Accepted March 31, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Siddharth S Dey, Jonathan E Foley, Prajit Limsirichai, David V Schaffer, Adam P Arkin
  • Systematic discovery of drug interaction mechanisms
    Systematic discovery of drug interaction mechanisms
    1. Guillaume Chevereau12 and
    2. Tobias Bollenbach*,1
    1. 1IST Austria, Klosterneuburg, Austria
    2. 2INSA de Strasbourg, Strasbourg, France
    1. *Corresponding author. Tel: +43 2243 9000 4101; E‐mail: tb{at}ist.ac.at

    A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.

    Synopsis

    A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations. Rare violations of this principle expose cellular functions that control drug interactions and can be targeted by small molecules to alter drug interactions in predictable ways.

    • Drug interactions between antibiotics are highly robust to genetic perturbations.

    • A general principle of bacterial growth enables the prediction of mutant growth rates under drug combinations.

    • Rare violations of this principle expose cellular functions that control drug interactions.

    • Diverse drug interactions are controlled by recurring cellular functions, including LPS synthesis and ATP synthesis.

    • antibiotics
    • drug combination design
    • drug interaction mechanisms
    • Escherichia coli
    • general principles of biological systems

    Mol Syst Biol. (2015) 11: 807

    • Received February 12, 2015.
    • Revision received April 9, 2015.
    • Accepted April 15, 2015.

    This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

    Guillaume Chevereau, Tobias Bollenbach