Systems Biology

  1. Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

    1. Alexey Stukalov
    2. Virginie Girault
    3. Vincent Grass
    4. Ozge Karayel
    5. Valter Bergant
    6. Christian Urban
    7. Darya A. Haas
    8. Yiqi Huang
    9. Lila Oubraham
    10. Anqi Wang
    11. M. Sabri Hamad
    12. Antonio Piras
    13. Fynn M. Hansen
    14. Maria C. Tanzer
    15. Igor Paron
    16. Luca Zinzula
    17. Thomas Engleitner
    18. Maria Reinecke
    19. Teresa M. Lavacca
    20. Rosina Ehmann
    21. Roman Wölfel
    22. Jörg Jores
    23. Bernhard Kuster
    24. Ulrike Protzer
    25. Roland Rad
    26. John Ziebuhr
    27. Volker Thiel
    28. Pietro Scaturro
    29. Matthias Mann
    30. Andreas Pichlmair

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity

  2. In Silico Modeling of Virus Particle Propagation and Infectivity along the Respiratory Tract: A Case Study for SARS-COV-2

    1. Dixon Vimalajeewa
    2. Sasitharan Balasubramaniam
    3. Donagh P. Berry
    4. Gerald Barry

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. High-content screening of coronavirus genes for innate immune suppression reveals enhanced potency of SARS-CoV-2 proteins

    1. Erika J. Olson
    2. David M. Brown
    3. Timothy Z. Chang
    4. Lin Ding
    5. Tai L. Ng
    6. H. Sloane Weiss
    7. Yukiye Koide
    8. Peter Koch
    9. Nathan Rollins
    10. Pia Mach
    11. Tobias Meisinger
    12. Trenton Bricken
    13. Joshua Rollins
    14. Yun Zhang
    15. Colin Molloy
    16. Yun Zhang
    17. Bridget N. Queenan
    18. Timothy Mitchison
    19. Debora Marks
    20. Jeffrey C. Way
    21. John I. Glass
    22. Pamela A. Silver

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Control-theoretic immune tradeoffs explain SARS-CoV-2 virulence and transmission variation

    1. Anish A. Sarma
    2. Aartik Sarma
    3. Marie Csete
    4. Peter P. Lee
    5. John C. Doyle

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  5. Genome‐scale metabolic modeling reveals SARS‐CoV‐2‐induced metabolic changes and antiviral targets

    1. Kuoyuan Cheng
    2. Laura Martin‐Sancho
    3. Lipika R Pal
    4. Yuan Pu
    5. Laura Riva
    6. Xin Yin
    7. Sanju Sinha
    8. Nishanth Ulhas Nair
    9. Sumit K Chanda
    10. Eytan Ruppin

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  6. Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

    1. Sierra M Barone
    2. Alberta GA Paul
    3. Lyndsey M Muehling
    4. Joanne A Lannigan
    5. William W Kwok
    6. Ronald B Turner
    7. Judith A Woodfolk
    8. Jonathan M Irish

    • Curated by eLife

      Evaluation Summary:

      The three reviewers were overly positive about the stated goal of your T-REX method to characterize rare populations by cytometry. The potential applications in the context of analyzing antigen-specific T cells (as identified as tetramer-positive cells) were not missed on the reviewers and the use of the 2 timepoints-cohort of samples from rhinovirus-infected patients was judged clever. However, all three reviewers requested some edits and additional tests to really distinguish T-REX from other methods in terms of performance, and to better understand its analysis power. Reviewer #1 enjoined you to clarify the improvements of your method compared to previous methods. Reviewer #2 requested more stringent tests of your methods against functional datasets. Reviewer #3 inquired about corrections for batch effects, the result consistency for repeated down-sampling as well as the scalability of the method (especially when UMAP is being used).

      (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

    Reviewed by eLife, ScreenIT

    6 evaluationsAppears in 2 listsLatest version Latest activity

  7. A proteome-scale map of the SARS-CoV-2–human contactome

    1. Dae-Kyum Kim
    2. Benjamin Weller
    3. Chung-Wen Lin
    4. Dayag Sheykhkarimli
    5. Jennifer J. Knapp
    6. Guillaume Dugied
    7. Andreas Zanzoni
    8. Carles Pons
    9. Marie J. Tofaute
    10. Sibusiso B. Maseko
    11. Kerstin Spirohn
    12. Florent Laval
    13. Luke Lambourne
    14. Nishka Kishore
    15. Ashyad Rayhan
    16. Mayra Sauer
    17. Veronika Young
    18. Hridi Halder
    19. Nora Marín-de la Rosa
    20. Oxana Pogoutse
    21. Alexandra Strobel
    22. Patrick Schwehn
    23. Roujia Li
    24. Simin T. Rothballer
    25. Melina Altmann
    26. Patricia Cassonnet
    27. Atina G. Coté
    28. Lena Elorduy Vergara
    29. Isaiah Hazelwood
    30. Betty B. Liu
    31. Maria Nguyen
    32. Ramakrishnan Pandiarajan
    33. Bushra Dohai
    34. Patricia A. Rodriguez Coloma
    35. Juline Poirson
    36. Paolo Giuliana
    37. Luc Willems
    38. Mikko Taipale
    39. Yves Jacob
    40. Tong Hao
    41. David E. Hill
    42. Christine Brun
    43. Jean-Claude Twizere
    44. Daniel Krappmann
    45. Matthias Heinig
    46. Claudia Falter
    47. Patrick Aloy
    48. Caroline Demeret
    49. Marc Vidal
    50. Michael A. Calderwood
    51. Frederick P. Roth
    52. Pascal Falter-Braun

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  8. Deep Time Course Proteomics of SARS-CoV- and SARS-CoV-2-Infected Human Lung Epithelial Cells (Calu-3) Reveals Strong Induction of Interferon-Stimulated Gene Expression by SARS-CoV-2 in Contrast to SARS-CoV

    1. Marica Grossegesse
    2. Daniel Bourquain
    3. Markus Neumann
    4. Lars Schaade
    5. Jessica Schulze
    6. Christin Mache
    7. Thorsten Wolff
    8. Andreas Nitsche
    9. Joerg Doellinger

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  9. The Great Deceiver: miR-2392’s Hidden Role in Driving SARS-CoV-2 Infection

    1. J. Tyson McDonald
    2. Francisco Javier Enguita
    3. Deanne Taylor
    4. Robert J. Griffin
    5. Waldemar Priebe
    6. Mark R. Emmett
    7. Mohammad M. Sajadi
    8. Anthony D. Harris
    9. Jean Clement
    10. Joseph M. Dybas
    11. Nukhet Aykin-Burns
    12. Joseph W. Guarnieri
    13. Larry N. Singh
    14. Peter Grabham
    15. Stephen B. Baylin
    16. Aliza Yousey
    17. Andrea N. Pearson
    18. Peter M. Corry
    19. Amanda Saravia-Butler
    20. Thomas R. Aunins
    21. Sadhana Sharma
    22. Prashant Nagpal
    23. Cem Meydan
    24. Jonathan Foox
    25. Christopher Mozsary
    26. Bianca Cerqueira
    27. Viktorija Zaksas
    28. Urminder Singh
    29. Eve Syrkin Wurtele
    30. Sylvain V. Costes
    31. Gustavo Gastão Davanzo
    32. Diego Galeano
    33. Alberto Paccanaro
    34. Suzanne L. Meinig
    35. Robert S. Hagan
    36. Natalie M Bowman
    37. UNC COVID-19 Pathobiology Consortium
    38. Matthew C. Wolfgang
    39. Selin Altinok
    40. Nicolae Sapoval
    41. Todd J. Treangen
    42. Pedro M. Moraes-Vieira
    43. Charles Vanderburg
    44. Douglas C. Wallace
    45. Jonathan Schisler
    46. Christopher E. Mason
    47. Anushree Chatterjee
    48. Robert Meller
    49. Afshin Beheshti

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  10. A mathematical model for simulating the phase-based transmissibility of a novel coronavirus

    1. Tian-Mu Chen
    2. Jia Rui
    3. Qiu-Peng Wang
    4. Ze-Yu Zhao
    5. Jing-An Cui
    6. Ling Yin

    Reviewed by ScreenIT

    2 evaluationsAppears in 1 listLatest version Latest activity
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