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  • Robust synchronization of coupled circadian and cell cycle oscillators in single mammalian cells
    1. Jonathan Bieler1,,
    2. Rosamaria Cannavo1,,
    3. Kyle Gustafson1,
    4. Cedric Gobet1,
    5. David Gatfield2 and
    6. Felix Naef*,1
    1. 1The Institute of Bioengineering, School of Life Sciences Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
    2. 2Center for Integrative Genomics, Génopode, University of Lausanne, Lausanne, Switzerland
    1. *Corresponding author. Tel: +41 21 693 1621; E‐mail: felix.naef{at}epfl.ch
    1. These authors contributed equally to this work

    Single‐cell time‐lapse analyses in mouse cells show that circadian and cell cycles are robustly synchronized. This state reflects a predominant unilateral influence of the cell cycle on the circadian oscillator.

    Synopsis

    Single‐cell time‐lapse analyses in mouse cells show that circadian and cell cycles are robustly synchronized. This state reflects a predominant unilateral influence of the cell cycle on the circadian oscillator.

    • Circadian and cell cycles in mouse NIH3T3 cells proceed in tight synchrony that is highly robust over a wide range of conditions.

    • The synchronized state reflects predominant influence of the cell cycle on the circadian cycle.

    • Timing of divisions relative to the circadian cycle is predicted by the period mismatch of the two cycles.

    • Stochastic modeling of two interacting phase oscillators identifies the parameters of the coupling functions.

    • cell cycle
    • circadian cycle
    • single cells
    • synchronization
    • time‐lapse imaging

    Mol Syst Biol. (2014) 10: 739

    • Received February 20, 2014.
    • Revision received June 5, 2014.
    • Accepted June 5, 2014.

    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.

    Jonathan Bieler, Rosamaria Cannavo, Kyle Gustafson, Cedric Gobet, David Gatfield, Felix Naef
  • A system‐level model for the microbial regulatory genome
    1. Aaron N Brooks1,2,,
    2. David J Reiss*,1,,
    3. Antoine Allard3,
    4. Wei‐Ju Wu1,
    5. Diego M Salvanha1,4,
    6. Christopher L Plaisier1,
    7. Sriram Chandrasekaran1,
    8. Min Pan1,
    9. Amardeep Kaur1 and
    10. Nitin S Baliga*,1,2,5,6
    1. 1Institute for Systems Biology, Seattle, WA, USA
    2. 2Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
    3. 3Département de Physique, de Génie Physique et d'Optique, Université Laval, Québec, QC, Canada
    4. 4LabPIB, Department of Computing and Mathematics FFCLRP‐USP, University of Sao Paulo, Ribeirao Preto, Brazil
    5. 5Departments of Microbiology and Biology, University of Washington, Seattle, WA, USA
    6. 6Lawrence Berkeley National Laboratories, Berkeley, CA, USA
    1. * Corresponding author. Tel: +1 206 732 1391; Fax: +1 206 732 1299; E‐mail: dreiss{at}systemsbiology.org

      Corresponding author. Tel: +1 206 732 1266; Fax: +1 206 732 1299; E‐mail: nbaliga{at}systemsbiology.org

    1. These authors contributed equally to this work

    Genome‐scale reconstruction of microbial gene regulatory networks using genome sequence and transcriptional profiles reveals condition‐dependent co‐regulated modules (corems) and predicts the underlying cis‐regulatory mechanisms.

    Synopsis

    Genome‐scale reconstruction of microbial gene regulatory networks using genome sequence and transcriptional profiles reveals condition‐dependent co‐regulated modules (corems) and predicts the underlying cis‐regulatory mechanisms.

    • Genome‐wide map of gene regulatory elements (GREs) and their condition‐specific activities

    • Model predicts which mechanisms mediate responses to specific environments

    • Operons and regulons are conditionally partitioned and re‐associated into co‐regulated modules or “corems”.

    • Corems group together genes from different operons and regulons that have highly similar fitness consequences.

    • EGRIN
    • gene regulatory networks
    • systems biology
    • transcriptional regulation

    Mol Syst Biol. (2014) 10: 740

    • Received January 28, 2014.
    • Revision received June 6, 2014.
    • Accepted June 11, 2014.

    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.

    Aaron N Brooks, David J Reiss, Antoine Allard, Wei‐Ju Wu, Diego M Salvanha, Christopher L Plaisier, Sriram Chandrasekaran, Min Pan, Amardeep Kaur, Nitin S Baliga
  • Intercellular network structure and regulatory motifs in the human hematopoietic system
    1. Wenlian Qiao1,
    2. Weijia Wang1,
    3. Elisa Laurenti2,3,
    4. Andrei L Turinsky4,
    5. Shoshana J Wodak4,5,
    6. Gary D Bader3,6,7,
    7. John E Dick2,3 and
    8. Peter W Zandstra*,1,7,8,9,10
    1. 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
    2. 2Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
    3. 3Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
    4. 4The Hospital for Sick Children, Toronto, ON, Canada
    5. 5Department of Biochemistry, University of Toronto, Toronto, ON, Canada
    6. 6Department of Computer Science, University of Toronto, Toronto, ON, Canada
    7. 7The Donnelly Centre, University of Toronto, Toronto, ON, Canada
    8. 8Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
    9. 9McEwen Centre for Regenerative Medicine, University of Health Network, Toronto, ON, Canada
    10. 10Heart & Stroke/Richard Lewar Centre of Excellence, Toronto, ON, Canada
    1. *Corresponding author. Tel: +1 416 978 8888; E‐mail: peter.zandstra{at}utoronto.ca

    A directional cell‐cell communication network of human hematopoietic cells reveals mechanisms of hematopoietic cell feedback in HSC fate regulation and provides insight into the design principles of the human hematopoietic system.

    Synopsis

    A directional cell‐cell communication network of human hematopoietic cells reveals mechanisms of hematopoietic cell feedback in HSC fate regulation and provides insight into the design principles of the human hematopoietic system.

    • Ligand production by hematopoietic cells is cell type‐dependent, whereas ligand binding is promiscuous.

    • Cell frequency modulation and compartmentalization establish specificity in HSC fate regulation.

    • Differentiated blood cells influence HSC fate through cell type‐specific feedback signals.

    • Pathway analysis identifies intracellular pathway nodes enriched in cell type and ligand coupled responses.

    • feedback regulation
    • hematopoietic stem cell
    • intercellular signaling

    Mol Syst Biol. (2014) 10: 741

    • Received January 20, 2014.
    • Revision received June 9, 2014.
    • Accepted June 17, 2014.

    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.

    Wenlian Qiao, Weijia Wang, Elisa Laurenti, Andrei L Turinsky, Shoshana J Wodak, Gary D Bader, John E Dick, Peter W Zandstra
  • Measuring error rates in genomic perturbation screens: gold standards for human functional genomics
    1. Traver Hart1,
    2. Kevin R Brown1,
    3. Fabrice Sircoulomb2,
    4. Robert Rottapel2,3,4 and
    5. Jason Moffat*,1,5
    1. 1Donnelly Centre and Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada
    2. 2Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Hospital University Health Network, Toronto, ON, Canada
    3. 3Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
    4. 4Division of Rheumatology, Department of Medicine, St. Michael's Hospital, Toronto, ON, Canada
    5. 5Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
    1. *Corresponding author. Tel: +1 416 978 4019; E‐mail: j.moffat{at}utoronto.ca

    This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.

    Synopsis

    This study provides a gold‐standard set for essential and nonessential human genes in cancer cell lines. The ‘Daisy model’ for core versus context‐specific essentiality provides a method to evaluate data quality in genome‐scale RNAi and CRISPR screens.

    • Gold‐standard reference sets of human essential and nonessential genes are leveraged to improve analyses of RNAi and CRISPR screens.

    • Characteristics of human essential genes are derived from the cumulative analysis of RNAi screens.

    • The Daisy model of gene essentiality is derived from the difference between core and context‐specific cell line essentials.

    • A computational framework is presented for the prediction of human essential genes from reverse genetic screening data.

    • cancer
    • CRISPR
    • essential genes
    • RNAi
    • shRNA

    Mol Syst Biol. (2014) 10: 733

    • Received February 20, 2014.
    • Revision received April 10, 2014.
    • Accepted April 24, 2014.

    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.

    Traver Hart, Kevin R Brown, Fabrice Sircoulomb, Robert Rottapel, Jason Moffat
  • An integrative, multi‐scale, genome‐wide model reveals the phenotypic landscape of Escherichia coli
    1. Javier Carrera14,
    2. Raissa Estrela2,
    3. Jing Luo1,
    4. Navneet Rai1,
    5. Athanasios Tsoukalas1,3 and
    6. Ilias Tagkopoulos*,1,3
    1. 1UC Davis Genome Center, University of California, Davis, CA, USA
    2. 2Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
    3. 3Department of Computer Science, University of California, Davis, CA, USA
    4. 4 Department of Bioengineering, Stanford University, Stanford, CA, USA
    1. *Corresponding author. Tel: +1 530 752 7707; Fax: +1 530 752 4767; E‐mail: iliast{at}ucdavis.edu

    A data‐driven, integrative modeling methodology is presented that unifies signal transduction, gene expression, and metabolic processes under a common framework. Training on an aggregated dataset results in improved prediction of regulatory connections and measured phenotypes.

    Synopsis

    A data‐driven, integrative modeling methodology is presented that unifies signal transduction, gene expression, and metabolic processes under a common framework. Training on an aggregated dataset results in improved prediction of regulatory connections and measured phenotypes.

    • A curated Escherichia coli dataset combining gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction metabolic pathways, and growth data is constructed.

    • Gene expression, signal transduction, and metabolic datasets are incorporated into a novel integrative framework for genome‐scaling modeling.

    • Training of the genome‐scale model with the integrated dataset leads to high correlation between predicted and measured phenotypes and reveals new regulatory links.

    • A model enrichment technique identifies under‐represented and highly variable knockouts to drive experimentation.

    • genome engineering
    • genome‐scale model
    • model‐driven experimentation
    • predictive modeling and integration
    • systems and synthetic biology

    Mol Syst Biol. (2014) 10: 735

    • Received January 8, 2014.
    • Revision received May 2, 2014.
    • Accepted May 13, 2014.

    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.

    Javier Carrera, Raissa Estrela, Jing Luo, Navneet Rai, Athanasios Tsoukalas, Ilias Tagkopoulos
  • Phenotypic bistability in Escherichia coli's central carbon metabolism
    1. Oliver Kotte1,,
    2. Benjamin Volkmer1,,
    3. Jakub L Radzikowski2 and
    4. Matthias Heinemann*,1,2
    1. 1Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
    2. 2Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, The Netherlands
    1. *Corresponding author. Tel: +31 50 363 8146; E‐mail: m.heinemann{at}rug.nl
    1. These authors contributed equally to this work

    Upon nutrient change, a homogeneous E. coli population can split into a growing and a non‐growing persister phenotype. Stochastic variation in metabolic flux is responsible for this responsive diversification.

    Synopsis

    Upon nutrient change, a homogeneous E. coli population can split into a growing and a non‐growing persister phenotype. Stochastic variation in metabolic flux is responsible for this responsive diversification.

    • Responsive diversification offers an explanation for lag phases in bacterial cultures

    • Flux‐induced phenotypic bistability generalizes to central metabolism

    • Conditional bet‐hedging balances fast glycolytic growth and ability for gluconeogenic growth

    • Limited carbon influx is a major trigger for persistence

    • bistability
    • flux sensing
    • metabolism
    • noise
    • persisters

    Mol Syst Biol. (2014) 10: 736

    • Received November 27, 2013.
    • Revision received May 22, 2014.
    • Accepted May 23, 2014.

    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.

    Oliver Kotte, Benjamin Volkmer, Jakub L Radzikowski, Matthias Heinemann
  • Minimal metabolic pathway structure is consistent with associated biomolecular interactions
    1. Aarash Bordbar1,
    2. Harish Nagarajan2,
    3. Nathan E Lewis1,3,4,
    4. Haythem Latif1,
    5. Ali Ebrahim1,
    6. Stephen Federowicz2,
    7. Jan Schellenberger2 and
    8. Bernhard O Palsson*,1,2,5
    1. 1Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
    2. 2Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
    3. 3Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA
    4. 4Wyss Institute for Biologically Inspired Engineering and Department of Genetics, Harvard Medical School, Boston, MA, USA
    5. 5Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
    1. *Corresponding author. Tel: +1 858 534 5668; Fax: +1 858 822 3120; E‐mail: palsson{at}ucsd.edu

    The MinSpan algorithm is presented that defines the shortest functional metabolic pathways at the genome scale, based on whole network function and parsimonious use of cellular components. The pathways are biologically supported by biomolecular interaction networks.

    Synopsis

    The MinSpan algorithm is presented that defines the shortest functional metabolic pathways at the genome scale, based on whole network function and parsimonious use of cellular components. The pathways are biologically supported by biomolecular interaction networks.

    • Pathways are traditionally defined by biochemical experimentation and are the universal paradigm for describing cellular processes.

    • The MinSpan algorithm defines pathways at the genome scale using metabolic network reconstructions based on a principle of minimal use of biochemical transformations.

    • MinSpan derived pathways are as or more representative of the underlying protein–protein, positive genetic, and transcriptional regulatory interactions compared to traditional pathways.

    • The MinSpan pathways are used in conjunction with constraint‐based modeling to predict transcriptional regulation in E. coli.

    • constraint‐based modeling
    • genetic interactions
    • pathway analysis
    • protein‐protein interactions
    • transcriptional regulatory networks

    Mol Syst Biol. (2014) 10: 737

    • Received February 28, 2014.
    • Revision received May 20, 2014.
    • Accepted May 26, 2014.

    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.

    Aarash Bordbar, Harish Nagarajan, Nathan E Lewis, Haythem Latif, Ali Ebrahim, Stephen Federowicz, Jan Schellenberger, Bernhard O Palsson