Systems Biology

Interplay of host regulatory network on SARS-CoV-2 binding and replication machinery

1. Shiek SSJ Ahmed
2. Prabu Paramasivam
3. Kamal Raj
4. Vishal Kumar
5. Ram murugesan
6. Ramakrishnan

Reviewed by ScreenIT

Mechanistic modeling of the SARS-CoV-2 disease map

1. Kinza Rian
2. Marina Esteban-Medina
3. Marta R. Hidalgo
4. Cankut Çubuk
5. Matias M. Falco
6. Carlos Loucera
7. Devrim Gunyel
8. Marek Ostaszewski
9. María Peña-Chilet
10. Joaquín Dopazo

Reviewed by ScreenIT

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis

1. Nilesh Kumar
2. Bharat Mishra
3. Adeel Mehmood
4. Mohammad Athar
5. M. Shahid Mukhtar

Reviewed by ScreenIT

Functional Immune Deficiency Syndrome via Intestinal Infection in COVID-19

1. Erica T. Prates
2. Michael R. Garvin
3. Mirko Pavicic
4. Piet Jones
5. Manesh Shah
6. Christiane Alvarez
7. David Kainer
8. Omar Demerdash
9. B Kirtley Amos
10. Armin Geiger
11. John Pestian
12. Kang Jin
13. Alexis Mitelpunkt
14. Eric Bardes
15. Bruce Aronow
16. Daniel Jacobson

Reviewed by ScreenIT

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

A data-driven 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. Hui Yang
8. Lixiang Hong
9. Nian Wu
10. Enming Yuan
11. Lili Cheng
12. Yipin Lei
13. Hantao Shu
14. Xiaolong Feng
15. Ziyuan Jiang
16. Ying Chi
17. Xiling Guo
18. Lunbiao Cui
19. Liang Xiao
20. Zeng Li
21. Chunhao Yang
22. Zehong Miao
23. Haidong Tang
24. Ligong Chen
25. Hainian Zeng
26. Dan Zhao
27. Fengcai Zhu
28. Xiaokun Shen
29. Jianyang Zeng

Reviewed by ScreenIT

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

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

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

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