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  • Revisiting biomarker discovery by plasma proteomics

    Philipp E Geyer, Lesca M Holdt, Daniel Teupser and Matthias Mann

    The performance of mass spectrometry (MS)‐based proteomics has reached a sensitivity and dynamic range that makes it suitable for biomarker studies. This Review discusses plasma proteome profiling strategies and how they can be translated into clinical practice.

  • Pervasive coexpression of spatially proximal genes is buffered at the protein level

    Georg Kustatscher, Piotr Grabowski and Juri Rappsilber

    Housekeeping genes are clustered in the human genome, which minimizes stochastic silencing but leads to partial co‐expression of thousands of functionally unrelated genes. This non‐functional mRNA co‐expression is buffered at the protein level.

  • Network analyses identify liver‐specific targets for treating liver diseases

    Sunjae Lee, Cheng Zhang, Zhengtao Liu, Jan Boren, Adil Mardinoglu and colleagues

    Network analyses identify liver‐specific drug targets that can be used to effectively treat liver diseases including nonalcoholic fatty liver disease and hepatocellular carcinoma.

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  • Open Access
    Article
    Modeling signaling‐dependent pluripotency with Boolean logic to predict cell fate transitions
    Modeling signaling‐dependent pluripotency with Boolean logic to predict cell fate transitions
    1. Ayako Yachie‐Kinoshita1,2,3,
    2. Kento Onishi1,2,
    3. Joel Ostblom1,2,
    4. Matthew A Langley1,2,
    5. Eszter Posfai4,
    6. Janet Rossant4 and
    7. Peter W Zandstra (Peter.Zandstra{at}UBC.ca)*,1,2,5,6
    1. 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
    2. 2The Donnelly Centre, University of Toronto, Toronto, ON, Canada
    3. 3The Systems Biology Institute, Minato, Tokyo, Japan
    4. 4Program in Developmental and Stem Cell Biology, Hospital for Sick Children Research Institute, Toronto, ON, Canada
    5. 5Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
    6. 6Medicine by Design, A Canada First Research Excellence Program at the University of Toronto, Toronto, ON, Canada
    1. ↵*Corresponding author. Tel: +1 604 822 4838; E‐mail: Peter.Zandstra{at}UBC.ca

    This study reports a computational framework that simulates gene regulatory network (GRN) specified cell fate transitions, and cell compositions, from uniform input signals to successfully predict cellular decision processes from signal‐perturbed mouse pluripotent stem cells.

    Synopsis

    This study reports a computational framework that simulates gene regulatory network (GRN) specified cell fate transitions, and cell compositions, from uniform input signals to successfully predict cellular decision processes from signal‐perturbed mouse pluripotent stem cells.

    • A novel Boolean simulation framework is developed for predicting signal‐controlled GRN logic and stem cell fate decisions.

    • Novel quantitative metrics facilitate the comparison of GRN properties in the context of different cell states.

    • Micro‐environmental signals and feedback are incorporated into a GRN simulation framework.

    • Applying this novel framework to an inferred mouse pluripotent stem cell GRN model accurately predicts experimentally observed cell fate outcomes upon exposure to complex exogenous signals.

    • asynchronous Boolean simulation
    • embryonic stem cell
    • gene regulatory network
    • heterogeneity
    • pluripotency

    Mol Syst Biol. (2018) 14: e7952

    • Received August 24, 2017.
    • Revision received November 21, 2017.
    • Accepted December 20, 2017.
    • © 2018 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Ayako Yachie‐Kinoshita, Kento Onishi, Joel Ostblom, Matthew A Langley, Eszter Posfai, Janet Rossant, Peter W Zandstra
    Published online 29.01.2018
    • Development & Differentiation
    • Network Biology
    • Stem Cells
  • Open Access
    Article
    Cell‐specific responses to the cytokine TGFβ are determined by variability in protein levels
    Cell‐specific responses to the cytokine TGFβ are determined by variability in protein levels
    1. Jette Strasen1,†,
    2. Uddipan Sarma2,†,
    3. Marcel Jentsch1,3,†,
    4. Stefan Bohn3,
    5. Caibin Sheng1,3,
    6. Daniel Horbelt4,
    7. Petra Knaus4,
    8. Stefan Legewie (s.legewie{at}imb-mainz.de)*,2 and
    9. Alexander Loewer (loewer{at}bio.tu-darmstadt.de)*,1,3
    1. 1Berlin Institute for Medical Systems Biology, Max Delbrueck Center in the Helmholtz Association, Berlin, Germany
    2. 2Institute of Molecular Biology (IMB), Mainz, Germany
    3. 3Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
    4. 4Institute for Chemistry and Biochemistry, Freie Universität Berlin, Berlin, Germany
    1. ↵* Corresponding author. Tel: +49 6131 39 21430; E‐mail: s.legewie{at}imb-mainz.de
      Corresponding author. Tel: +49 6151 16 28060; E‐mail: loewer{at}bio.tu-darmstadt.de
    1. ↵† These authors contributed equally to this work

    Single‐cell measurements and mathematical modeling reveal that the levels of defined signaling proteins determine cell‐specific responses to the cytokine TGFβ, leading to the decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome.

    Synopsis

    Single‐cell measurements and mathematical modeling reveal that the levels of defined signaling proteins determine cell‐specific responses to the cytokine TGFβ, leading to the decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome.

    • Using live‐cell microscopy and constrained dynamic time warping, signaling dynamics of thousands of cells are quantitatively compared and grouped into distinct signaling classes.

    • A three‐tiered mathematical modeling strategy describes heterogeneous single‐cell responses and identifies sources of variability.

    • Negative feedback regulators such as SMAD7 control the response in a cell‐specific manner and fine‐tune TGFβ signaling in a subpopulation of cells.

    • cellular heterogeneity
    • mathematical modeling
    • signaling dynamics
    • single‐cell analysis
    • TGFβ‐SMAD signaling

    Mol Syst Biol. (2018) 14: e7733

    • Received May 6, 2017.
    • Revision received December 12, 2017.
    • Accepted December 15, 2017.
    • © 2018 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Jette Strasen, Uddipan Sarma, Marcel Jentsch, Stefan Bohn, Caibin Sheng, Daniel Horbelt, Petra Knaus, Stefan Legewie, Alexander Loewer
    Published online 25.01.2018
    • Quantitative Biology & Dynamical Systems
    • Signal Transduction
  • Open Access
    Method
    Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens
    Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens
    1. Reinoud de Groot1,
    2. Joel Lüthi1,2,
    3. Helen Lindsay1,
    4. René Holtackers1 and
    5. Lucas Pelkmans (lucas.pelkmans{at}imls.uzh.ch)*,1
    1. 1Institute of Molecular Life Sciences, University of Zürich, Zürich, Switzerland
    2. 2Systems Biology PhD program, Life Science Zürich Graduate School ETH Zürich and University of Zürich, Zürich, Switzerland
    1. ↵*Corresponding author. Tel: +41 44 63 53 123; E‐mail: lucas.pelkmans{at}imls.uzh.ch

    The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes.

    Synopsis

    The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes.

    • CRISPR‐Cas9 mediated gene perturbation by transient transfection of a single targeting plasmid is combined with large‐scale, image‐based profiling.

    • Methods are developed for the construction of arrayed CRISPR‐Cas9 screening libraries.

    • Single cell phenotypes are profiled by training computational classifiers between transfected and non‐transfected cells from the same well.

    • Profiling of a marker of the nuclear pore complex identifies several classes of phenotypic perturbations.

    • arrayed library
    • CRISPR‐Cas9
    • functional genomics
    • nuclear pore complex
    • single‐cell phenotypic profiling

    Mol Syst Biol. (2018) 14: e8064

    • Received October 23, 2017.
    • Revision received December 18, 2017.
    • Accepted December 21, 2017.
    • © 2018 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Reinoud de Groot, Joel Lüthi, Helen Lindsay, René Holtackers, Lucas Pelkmans
    Published online 23.01.2018
    • Chromatin, Epigenetics, Genomics & Functional Genomics
    • Genome-Scale & Integrative Biology
    • Methods & Resources
  • Open Access
    Article
    Assigning function to natural allelic variation via dynamic modeling of gene network induction
    Assigning function to natural allelic variation via dynamic modeling of gene network induction
    1. Magali Richard (magali.richard{at}univ-grenoble-alpes.fr)*,1,2,
    2. Florent Chuffart1,
    3. Hélène Duplus‐Bottin1,
    4. Fanny Pouyet1,
    5. Martin Spichty1,
    6. Etienne Fulcrand1,
    7. Marianne Entrevan1,
    8. Audrey Barthelaix1,
    9. Michael Springer3,
    10. Daniel Jost (daniel.jost{at}univ-grenoble-alpes.fr)*,2 and
    11. Gaël Yvert (gael.yvert{at}ens-lyon.fr)*,1
    1. 1Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université Lyon 1 Université de Lyon, Lyon, France
    2. 2Univ. Grenoble Alpes, CNRS CHU Grenoble Alpes Grenoble INP TIMC‐IMAG, Grenoble, France
    3. 3Department of Systems Biology, Harvard Medical School, Boston, MA, USA
    1. ↵* Corresponding author. Tel: +33 4 56 52 00 68; E‐mail: magali.richard{at}univ-grenoble-alpes.fr
      Corresponding author. Tel: +33 4 56 52 00 69; E‐mail: daniel.jost{at}univ-grenoble-alpes.fr
      Corresponding author. Tel: +33 4 72 72 80 00; E‐mail: gael.yvert{at}ens-lyon.fr

    An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants.

    Synopsis

    An approach based on genotype‐specific gene regulatory network models is used to examine the functional consequences of yeast GAL3 sequence variants. This framework can be more generally applied to the mechanistic interpretation of genetic variants.

    • The principle of the proposed approach is linking genetic variation to informative changes of parameter values of a regulatory network model.

    • Experimental analyses of the yeast GAL network shows that GAL3 natural variation is sufficient to convert a gradual response into a binary switch.

    • Dynamic network modeling successfully maps alleles to specific locations of the parameter space, allowing functional inference of DNA polymorphisms.

    • galactose
    • personalized medicine
    • SNP function
    • stochastic model
    • yeast

    Mol Syst Biol. (2018) 14: e7803

    • Received June 7, 2017.
    • Revision received December 15, 2017.
    • Accepted December 18, 2017.
    • © 2018 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Magali Richard, Florent Chuffart, Hélène Duplus‐Bottin, Fanny Pouyet, Martin Spichty, Etienne Fulcrand, Marianne Entrevan, Audrey Barthelaix, Michael Springer, Daniel Jost, Gaël Yvert
    Published online 15.01.2018
    • Methods & Resources
    • Network Biology
    • Quantitative Biology & Dynamical Systems
  • You have accessRestricted access
    Corrigendum
    Non‐genetic diversity modulates population performance
    Non‐genetic diversity modulates population performance
    Adam James Waite, Nicholas W Frankel, Yann S Dufour, Jessica F Johnston, Junjiajia Long, Thierry Emonet
    Published online 10.01.2018
  • Open Access
    Article
    A Sizer model for cell differentiation in Arabidopsis thaliana root growth
    A Sizer model for cell differentiation in <em>Arabidopsis thaliana</em> root growth
    1. Irina Pavelescu1,2,
    2. Josep Vilarrasa‐Blasi1,4,
    3. Ainoa Planas‐Riverola1,
    4. Mary‐Paz González‐García1,5,
    5. Ana I Caño‐Delgado (ana.cano{at}cragenomica.es)*,1,† and
    6. Marta Ibañes (mibanes{at}ub.edu)*,2,3,†
    1. 1Department of Molecular Genetics, Center for Research in Agricultural Genomics (CRAG), CSIC‐IRTA‐UAB‐UB, Campus UAB Bellaterra (Cerdanyola del Vallès), Barcelona, Spain
    2. 2Departament de Física de la Matèria Condensada, Universitat de Barcelona, Barcelona, Spain
    3. 3Universitat de Barcelona Institute of Complex Systems (UBICS) Universitat de Barcelona, Barcelona, Spain
    4. 4Present Address: Carnegie Institution for Science Department of Plant Biology, Stanford, CA, USA
    5. 5Present Address: Centro Nacional de Biotecnología‐CSIC, Madrid, Spain
    1. ↵* Corresponding author. Tel: +34 93 563 66 00 Ext. 3210; Fax: +34 93 563 66 01; E‐mail: ana.cano{at}cragenomica.es
      Corresponding author. Tel: +34 93 403 91 77; E‐mail: mibanes{at}ub.edu
    1. ↵† These authors contributed equally to this work

    Mathematical modeling and quantitative data on phenotypic variability from wild‐type Arabidopsis roots indicate that cells measure their length to stop elongating in primary roots.

    Synopsis

    Mathematical modeling and quantitative data on phenotypic variability from wild‐type Arabidopsis roots indicate that cells measure their length to stop elongating in primary roots.

    • Cell length quantification in single roots along the meristem and the elongation zone allows exploring relationships between phenotypic traits.

    • Computational analyses evaluate the plausibility of three models to stop cell elongation in roots: whether cells measure distances, time, or cell size.

    • The primary root growth is consistent with a Sizer mechanism, in which cells stop elongating when reaching a threshold cell length.

    • Brassinosteroid signaling at the meristem is sufficient to set the mature cell length.

    • Arabidopsis root zonation
    • brassinosteroids
    • cell differentiation
    • computational analysis
    • phenotypic variability

    Mol Syst Biol. (2018) 14: e7687

    • Received April 12, 2017.
    • Revision received November 21, 2017.
    • Accepted November 27, 2017.
    • © 2018 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Irina Pavelescu, Josep Vilarrasa‐Blasi, Ainoa Planas‐Riverola, Mary‐Paz González‐García, Ana I Caño‐Delgado, Marta Ibañes
    Published online 10.01.2018
    • Development & Differentiation
    • Plant Biology
    • Quantitative Biology & Dynamical Systems
  • Open Access
    Article
    Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer
    Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer
    1. Anisha M Perez1,†,
    2. Marcella M Gomez2,†,
    3. Prashant Kalvapalle3,
    4. Erin O'Brien‐Gilbert1,
    5. Matthew R Bennett1,4 and
    6. Yousif Shamoo (shamoo{at}rice.edu)*,1
    1. 1Department of Biosciences, Rice University, Houston, TX, USA
    2. 2Department of Applied Mathematics & Statistics, University of California, Santa Cruz, CA, USA
    3. 3Systems, Synthetic, and Physical Biology Graduate Program, Rice University, Houston, TX, USA
    4. 4Department of Bioengineering, Rice University, Houston, TX, USA
    1. ↵*Corresponding author. Tel: +1 713 348 5493; E‐mail: shamoo{at}rice.edu
    1. ↵† These authors contributed equally to this work

    A physiological model uses cellular fitness as a proxy to predict the biochemical properties of major facilitator superfamily tetracycline efflux pump, TetB, and a family of variants. The model incorporates drug diffusion, growth inhibition by the drug, and active drug transport by TetB.

    Synopsis

    A physiological model uses cellular fitness as a proxy to predict the biochemical properties of major facilitator superfamily tetracycline efflux pump, TetB, and a family of variants. The model incorporates drug diffusion, growth inhibition by the drug, and active drug transport by TetB.

    • Cellular fitness as a function of drug concentration is modeled to reveal biochemical properties of TetB and variants relating to Vmax and Km.

    • A mathematical approximation allows for the decoupling of changes in variant cellular fitness due to substrate binding affinity and pumping efficiency after incorporation of protein levels.

    • Desirable candidates from a plasmid variant library are quickly screened using only cellular fitness as a function of drug concentration.

    • The model results are in good agreement with current knowledge of MFS transporter structure‐function relationship.

    • antibiotic resistance
    • efflux pump
    • major facilitator superfamily
    • structure function
    • tetracycline

    Mol Syst Biol. (2017) 13: 964

    • Received March 15, 2017.
    • Revision received November 29, 2017.
    • Accepted December 1, 2017.
    • © 2017 The Authors. Published under the terms of the CC BY 4.0 license

    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.

    Anisha M Perez, Marcella M Gomez, Prashant Kalvapalle, Erin O'Brien‐Gilbert, Matthew R Bennett, Yousif Shamoo
    Published online 22.12.2017
    • Microbiology, Virology & Host Pathogen Interaction
    • Pharmacology & Drug Discovery
    • Quantitative Biology & Dynamical Systems
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December 2017 Cover

Genomics, Systems Genetics & Functional Genomics

Highlights of research recently published in Molecular Systems Biology:

  • Allelic expression mapping across cellular lineages
  • Genome annotation by bidirectional HMMs
  • Defining a Minimal Genome
  • Natural network involved in autism
  • Heritability of human plasma proteins
  • Negative feedback buffers regulatory variants
  • Genetic variation impacting antisense transcription
  • Bar-ChIP for high-throughput chromatin profiling

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