Systems Biology

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

1. Nikolaos Meimetis
2. Douglas A. Lauffenburger
3. Avlant Nilsson

Reviewed by preLights

Evolutionary druggability for low-dimensional fitness landscapes toward new metrics for antimicrobial applications

1. Rafael F Guerrero
2. Tandin Dorji
3. Ra'Mal M Harris
4. Matthew D Shoulders
5. C Brandon Ogbunugafor

• Curated by eLife

  eLife assessment

  This manuscript introduces two valuable new metrics - "variant vulnerability" and "drug applicability" - that would be of use to identify candidate drugs for treating infections while considering longer-term, evolution-based treatment outcomes. Despite the intuitive appeal of the metrics and their potential, the study remains incomplete, as it fails to demonstrate the generality of the approach. The work could be improved by analysing a broader range of data in a systematic way and directly tying the metrics to outcomes, which would make it possible to better assess their impact and utility.

Reviewed by eLife

A unified approach to dissecting biphasic responses in cell signaling

1. Vaidhiswaran Ramesh
2. J Krishnan

• Curated by eLife

  eLife assessment

  This study presents a useful mathematical analysis of different signaling networks in an attempt to provide general rules that give rise to biphasic responses, a widely observed behavior in biology in which the outputs of the network depend non-monotonically on the inputs. Determining general conditions that underlie this behavior would be useful in engineering synthetic biological systems and for mechanistically understanding biphasic responses in biological systems. However, whereas the mathematical approach and methods are solid, as they stand, the analyses are inadequate to assess how these findings are applicable in nature and which are general.

Reviewed by eLife

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

1. Jeanette A.I. Johnson
2. Genevieve L. Stein-O’Brien
3. Max Booth
4. Randy Heiland
5. Furkan Kurtoglu
6. Daniel R. Bergman
7. Elmar Bucher
8. Atul Deshpande
9. André Forjaz
10. Michael Getz
11. Ines Godet
12. Melissa Lyman
13. John Metzcar
14. Jacob Mitchell
15. Andrew Raddatz
16. Heber Rocha
17. Jacobo Solorzano
18. Aneequa Sundus
19. Yafei Wang
20. Danielle Gilkes
21. Luciane T. Kagohara
22. Ashley L. Kiemen
23. Elizabeth D. Thompson
24. Denis Wirtz
25. Pei-Hsun Wu
26. Neeha Zaidi
27. Lei Zheng
28. Jacquelyn W. Zimmerman
29. Elizabeth M. Jaffee
30. Young Hwan Chang
31. Lisa M. Coussens
32. Joe W. Gray
33. Laura M. Heiser
34. Elana J. Fertig
35. Paul Macklin

Reviewed by preLights

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

1. Zeyneb Kurt
2. Jenny Cheng
3. Rio Barrere-Cain
4. Caden N McQuillen
5. Zara Saleem
6. Neil Hsu
7. Nuoya Jiang
8. Calvin Pan
9. Oscar Franzén
10. Simon Koplev
11. Susanna Wang
12. Johan Björkegren
13. Aldons J Lusis
14. Montgomery Blencowe
15. Xia Yang

• Curated by eLife

  eLife assessment

  In this important study, the authors integrated genetic and genomic datasets from humans and mice to unveil shared networks and pathways associated with coronary artery disease. Their compelling analysis led to the identification of new regulatory genes and pathways in vascular tissues and in the liver, allowing for a more in-depth understanding of the pathogenesis of coronary artery disease.

Reviewed by eLife

The PP2A-like phosphatase Ppg1 mediates assembly of the Far complex to balance gluconeogenic outputs and enables adaptation to glucose depletion

1. Shreyas Niphadkar
2. Lavanya Karinje
3. Sunil Laxman

Reviewed by Review Commons

Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae

1. Maksim A. Yakovlev
2. Ke Liang
3. Carolyn R. Zaino
4. Daniel J. Vanselow
5. Andrew L. Sugarman
6. Alex Y. Lin
7. Patrick J. La Riviere
8. Yuxi Zheng
9. Justin D. Silverman
10. John C. Leichty
11. Sharon X. Huang
12. Keith C. Cheng

• Curated by eLife

  eLife assessment

  Tissue phenotyping is central to nearly all areas of biology. In this study, the authors use an advanced form of micro-CT (X-ray histotomography) in zebrafish to phenotype blood cells in the intact animal. These approaches build upon prior work from this group and others showing this is a scalable imaging method that could readily be applied to other cell types, and provide an excellent complement to histological analysis of tissues. This is important work, as it demonstrates that the method can provide an approach that is orthogonal to conventional histology. The strength of the presented data is compelling, with description of both the hardware and software needed to implement the protocol, which will make it accessible to other researchers in the field.

Reviewed by eLife

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

1. Idan Hekselman
2. Assaf Vital
3. Maya Ziv-Agam
4. Lior Kerber
5. Ido Yairi
6. Esti Yeger-Lotem

• Curated by eLife

  eLife assessment

  The study presents analyses linking cell-types to monogenic disorders using over-expression of known disease-associated genes in single-cell data to identify 110 disease-affected cell types for 714 Mendelian diseases. Overall this important study combines multiple data analyses to quantify the connection between cell types and human disorders. While some of the analyses are compelling, updates to the method are needed to ensure that statistical inference is appropriately stringent and rigorous.

Reviewed by eLife

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

1. Ricardo Omar Ramirez Flores
2. Jan David Lanzer
3. Daniel Dimitrov
4. Britta Velten
5. Julio Saez-Rodriguez

Reviewed by Review Commons

Homeostasis, injury, and recovery dynamics at multiple scales in a self-organizing mouse intestinal crypt

1. Louis Gall
2. Carrie Duckworth
3. Ferran Jardi
4. Lieve Lammens
5. Aimee Parker
6. Ambra Bianco
7. Holly Kimko
8. David Mark Pritchard
9. Carmen Pin

• Curated by eLife

  eLife assessment

  The authors developed a valuable mathematical model that describes the spatiotemporal dynamics of cells in the intestinal crypt. The proposed model makes an important contribution to the field, allowing a better understanding of the formation and response dynamics of the intestinal crypt through the effective evaluation of health, disease, and treatment conditions. The authors provided solid evidence of the validity of their model and their conclusions, but some minor claims are not properly justified in the current manuscript. This paper is meant for computational biologists and cancer researchers working on oncotherapies for the intestinal epithelium.

Reviewed by eLife