Systems Biology

  1. Iterative community-driven development of a SARS-CoV-2 tissue simulator

    1. Michael Getz
    2. Yafei Wang
    3. Gary An
    4. Maansi Asthana
    5. Andrew Becker
    6. Chase Cockrell
    7. Nicholson Collier
    8. Morgan Craig
    9. Courtney L. Davis
    10. James R. Faeder
    11. Ashlee N. Ford Versypt
    12. Tarunendu Mapder
    13. Juliano F. Gianlupi
    14. James A. Glazier
    15. Sara Hamis
    16. Randy Heiland
    17. Thomas Hillen
    18. Dennis Hou
    19. Mohammad Aminul Islam
    20. Adrianne L. Jenner
    21. Furkan Kurtoglu
    22. Caroline I. Larkin
    23. Bing Liu
    24. Fiona Macfarlane
    25. Pablo Maygrundter
    26. Penelope A Morel
    27. Aarthi Narayanan
    28. Jonathan Ozik
    29. Elsje Pienaar
    30. Padmini Rangamani
    31. Ali Sinan Saglam
    32. Jason Edward Shoemaker
    33. Amber M. Smith
    34. Jordan J.A. Weaver
    35. Paul Macklin

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  2. An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

    1. Yiyue Ge
    2. Tingzhong Tian
    3. Suling Huang
    4. Fangping Wan
    5. Jingxin Li
    6. Shuya Li
    7. Xiaoting Wang
    8. Hui Yang
    9. Lixiang Hong
    10. Nian Wu
    11. Enming Yuan
    12. Yunan Luo
    13. Lili Cheng
    14. Chengliang Hu
    15. Yipin Lei
    16. Hantao Shu
    17. Xiaolong Feng
    18. Ziyuan Jiang
    19. Yunfu Wu
    20. Ying Chi
    21. Xiling Guo
    22. Lunbiao Cui
    23. Liang Xiao
    24. Zeng Li
    25. Chunhao Yang
    26. Zehong Miao
    27. Ligong Chen
    28. Haitao Li
    29. Hainian Zeng
    30. Dan Zhao
    31. Fengcai Zhu
    32. Xiaokun Shen
    33. Jianyang Zeng

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  3. Time-varying transmission dynamics of Novel Coronavirus Pneumonia in China

    1. Tao Liu
    2. Jianxiong Hu
    3. Jianpeng Xiao
    4. Guanhao He
    5. Min Kang
    6. Zuhua Rong
    7. Lifeng Lin
    8. Haojie Zhong
    9. Qiong Huang
    10. Aiping Deng
    11. Weilin Zeng
    12. Xiaohua Tan
    13. Siqing Zeng
    14. Zhihua Zhu
    15. Jiansen Li
    16. Dexin Gong
    17. Donghua Wan
    18. Shaowei Chen
    19. Lingchuan Guo
    20. Yan Li
    21. Limei Sun
    22. Wenjia Liang
    23. Tie Song
    24. Jianfeng He
    25. Wenjun Ma

    Reviewed by ScreenIT

    1 evaluationAppears in 1 listLatest version Latest activity

  4. Determining growth rates from bright-field images of budding cells through identifying overlaps

    1. Julian MJ Pietsch
    2. Alán F Muñoz
    3. Diane-Yayra A Adjavon
    4. Iseabail Farquhar
    5. Ivan BN Clark
    6. Peter S Swain

    • Curated by eLife

      eLife assessment

      In this interesting manuscript, Pietsch et al. develop innovative machine learning approaches for automated analysis of budding yeast live-cell imaging data obtained with a dedicated microfluidic device that retains mother cells. Developing such tools is crucial to enable high-throughput image analysis. These methods will be useful for researchers studying these cells, and may also inspire similar approaches for other types of cells.

    Reviewed by eLife

    5 evaluationsAppears in 1 listLatest version Latest activity

  5. Genetic and dietary modulators of the inflammatory response in the gastrointestinal tract of the BXD mouse genetic reference population

    1. Xiaoxu Li
    2. Jean-David Morel
    3. Giorgia Benegiamo
    4. Johanne Poisson
    5. Alexis Bachmann
    6. Alexis Rapin
    7. Jonathan Sulc
    8. Evan Williams
    9. Alessia Perino
    10. Kristina Schoonjans
    11. Maroun Bou Sleiman
    12. Johan Auwerx

    • Curated by eLife

      eLife assessment

      This fundamental study provides a framework for leveraging systems genetics data to dissect mechanisms of gut physiology. The authors provide compelling analyses to highlight diverse modes of interrogating intestinal inflammation, dietary response, and consequent impacts on inflammatory bowel disease. As a resource, it will have great utility for linking genetic variation and diet to gut-related pathophysiologies.

    Reviewed by eLife

    7 evaluationsAppears in 1 listLatest version Latest activity

  6. Leveraging genetic diversity to identify small molecules that reverse mouse skeletal muscle insulin resistance

    1. Stewart WC Masson
    2. Søren Madsen
    3. Kristen C Cooke
    4. Meg Potter
    5. Alexis Diaz Vegas
    6. Luke Carroll
    7. Senthil Thillainadesan
    8. Harry B Cutler
    9. Ken R Walder
    10. Gregory J Cooney
    11. Grant Morahan
    12. Jacqueline Stöckli
    13. David E James

    • Curated by eLife

      eLife assessment

      This fundamental study leverages natural genetic diversity in mice to discover candidate genes for insulin sensitivity, followed by experimental identification of compounds that can modulate insulin sensitivity, and finally initial mechanistic investigation of the mode of action. The generalized approach presented here – the integration of systems genetics data with drug discovery – supported by compelling evidence, will be a guide for others who seek to translate insights from mammalian genetics to drug discovery.

    Reviewed by eLife

    7 evaluationsAppears in 1 listLatest version Latest activity

  7. Mycobacterial Metabolic Model Development for Drug Target Identification

    1. Bridget P. Bannerman
    2. Alex Oarga
    3. Jorge Júlvez

    • Curated by GigaByte

      Editor’s Assessment

      This work has generated metabolic models for the human pathogens Mycobacterium leprae and Mycobacteroides abscessus, alongside a new computational tool that can be used to identify potential drug targets. The standardised genomic scale metabolic models have been developed using the systems biology community standards for quality control and evaluation of models. After providing more detail on reproducibility, comparative performance of the models, and reuse, these resources are now published and are available for reuse by the global scientific community via the GigaDB, Biomodels, and PatMeDB repositories.

      This assessment refers to version 1 of this preprint.

    Reviewed by GigaByte

    2 evaluationsAppears in 2 listsLatest version Latest activity

  8. A Multiscale Protein Abundance Structured Population Kinetic Model Systematically Explores the Design Space of Constitutive and Inducible CAR-T cells

    1. Harshana Rajakaruna
    2. Milie Desai
    3. Jayajit Das

    Reviewed by Review Commons

    3 evaluationsAppears in 2 listsLatest version Latest activity

  9. Predictive nonlinear modeling of malignant myelopoiesis and tyrosine kinase inhibitor therapy

    1. Jonathan Rodriguez
    2. Abdon Iniguez
    3. Nilamani Jena
    4. Prasanthi Tata
    5. Zhong-Ying Liu
    6. Arthur D Lander
    7. John Lowengrub
    8. Richard A Van Etten

    • Curated by eLife

      eLife assessment

      This is an important study that investigates the impact of tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia. Through a combination of pre-clinical in vivo measurements, clinical data, and computational modeling, the authors present solid evidence regarding the heterogeneous effects of TKIs in patients and how the response to treatment may be improved. With the assumptions about differences between normal and leukemic cells addressed, this study would be of interest to those working in the fields of mathematical oncology and cancer biology.

    Reviewed by eLife

    4 evaluationsAppears in 1 listLatest version Latest activity

  10. Missense variant interaction scanning reveals a critical role of the FERM domain for tumor suppressor protein NF2 conformation and function

    1. Christina S Moesslacher
    2. Elisabeth Auernig
    3. Jonathan Woodsmith
    4. Andreas Feichtner
    5. Evelyne Jany-Luig
    6. Stefanie Jehle
    7. Josephine M Worseck
    8. Christian L Heine
    9. Eduard Stefan
    10. Ulrich Stelzl

    Reviewed by Review Commons

    4 evaluationsAppears in 1 listLatest version Latest activity
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