Systems Biology

Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages

1. C. F. Rowlatt
2. M. A. J. Chaplain
3. D. J. Hughes
4. S. H. Gillespie
5. D. H. Dockrell
6. I. Johannessen
7. R. Bowness

Reviewed by ScreenIT

Robotic search for optimal cell culture in regenerative medicine

1. Genki N Kanda
2. Taku Tsuzuki
3. Motoki Terada
4. Noriko Sakai
5. Naohiro Motozawa
6. Tomohiro Masuda
7. Mitsuhiro Nishida
8. Chihaya T Watanabe
9. Tatsuki Higashi
10. Shuhei A Horiguchi
11. Taku Kudo
12. Motohisa Kamei
13. Genshiro A Sunagawa
14. Kenji Matsukuma
15. Takeshi Sakurada
16. Yosuke Ozawa
17. Masayo Takahashi
18. Koichi Takahashi
19. Tohru Natsume

• Curated by eLife

  Evaluation Summary:

  The manuscript by Kanda GN, Natsume T et al. describes a robotic artificial intelligence system with a batch Bayesian optimization algorithm that allows to optimise and reliably repeat cell culture protocols. The authors utilise induced pluripotent stem cell-derived retinal pigment epithelial cells as a model culture system of broad interest in regenerative medicine. They demonstrate that the robotic system with Bayesian algorithm accelerates the optimisation of cell culture protocols and increases the quality and quantity of cell products, compared with manual operations - these results will likely inform and strongly impact modern cell culture strategies in regenerative medicine.

  (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 #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

Reviewed by eLife

Modeling COVID-19 disease biology to identify drug treatment candidates

1. Walter J. Jessen
2. Stefan Diaz Gaisenband
3. M’Lissa Quintanilla
4. Sadiq Lula
5. Patrick McLeroth
6. Adam Sullivan
7. Stanley Letovsky

Reviewed by ScreenIT

Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

1. Ines Thiele
2. Ronan M.T. Fleming

Reviewed by ScreenIT

Network design principle for robust oscillatory behaviors with respect to biological noise

1. Lingxia Qiao
2. Zhi-Bo Zhang
3. Wei Zhao
4. Ping Wei
5. Lei Zhang

• Curated by eLife

  Evaluation Summary:

  The authors study the important problem of how to achieve accurate oscillation robustly in biological networks where noise level may be high. The authors adopted a comprehensive approach and study how different network configurations affect oscillation. This work makes an important contribution to the field, as it offers the first comprehensive survey of networks motifs capable of oscillation, with further characterization of their robustness.

  (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. The reviewers remained anonymous to the authors.)

Reviewed by eLife

Integrated network-based multiple computational analyses for identification of co-expressed candidate genes associated with neurological manifestations of COVID-19

1. Suvojit Hazra
2. Alok Ghosh Chaudhuri
3. Basant K. Tiwary
4. Nilkanta Chakrabarti

Reviewed by ScreenIT

Protein degradation sets the fraction of active ribosomes at vanishing growth

1. Ludovico Calabrese
2. Jacopo Grilli
3. Matteo Osella
4. Christopher P. Kempes
5. Marco Cosentino Lagomarsino
6. Luca Ciandrini

• Curated by eLife

  Evaluation Summary:

  This work will be of interest to theorists in microbial systems biology. It shows that taking protein degradation into account improves theoretical predictions of bacterial growth laws at low growth rates. The theoretical aspects of this work are solid. Some underlying assumptions of the model and key predictions remain to be validated experimentally.

  (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. The reviewers remained anonymous to the authors.)

Reviewed by eLife

The metabolome of Mexican cavefish shows a convergent signature highlighting sugar, antioxidant, and Ageing-Related metabolites

1. J Kyle Medley
2. Jenna Persons
3. Tathagata Biswas
4. Luke Olsen
5. Robert Peuß
6. Jaya Krishnan
7. Shaolei Xiong
8. Nicolas Rohner

• Curated by eLife

  Evaluation Summary:

  Medley et al. study A. mexicanus, an extreme-adapted organism with important connections to human health. The authors test metabolic responses in this natural model of elevated blood glucose and extensive body fat deposits, conditions generally expected to predispose to higher risk for metabolic syndrome and higher frailty. The work is rigorous and will provide a reference for future studies aimed at dissecting the mechanistic basis underlying metabolic shifts in this uniquely attractive model. The authors also provide an open and accessible window into their data and analyses by sharing a Shiny app.

  (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 #1 agreed to share their name with the authors.)

Reviewed by eLife

Landscape of epithelial–mesenchymal plasticity as an emergent property of coordinated teams in regulatory networks

1. Kishore Hari
2. Varun Ullanat
3. Archana Balasubramanian
4. Aditi Gopalan
5. Mohit Kumar Jolly

• Curated by eLife

  Evaluation Summary:

  In this paper, the authors identify topological metrics in gene-regulatory networks that potentially predict the kinds of phenotypic steady-states that the network allows. In particular, they apply their results to the epithelial-mesenchymal plasticity, showing that the relevant gene regulatory networks are structured as ‘teams' that may be 'strong', yielding stable phenotypes, or 'weak', yielding unstable phenotypes prone to plasticity. The work would be of interest to researchers interested in systems biology and the nonlinear dynamics of biological systems, as well as biologists interested in gene regulatory networks and their (mis)functioning in cancer cells.

  (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 #1 agreed to share their name with the authors.)

Reviewed by eLife

Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics

1. Mayank Baranwal
2. Ryan L Clark
3. Jaron Thompson
4. Zeyu Sun
5. Alfred O Hero
6. Ophelia S Venturelli

• Curated by eLife

  Evaluation Summary:

  The ultimate goal of this work is to apply machine learning to learn from experimental data on temporal dynamics and functions of microbial communities to predict their future behavior and design new communities with desired functions. Using a significant amount of experimental data, the authors suggest that their method outperforms a commonly used approach. Overall, the work is potentially of broad interest to those working on microbiome prediction and design.

  (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 agreed to share their name with the authors.)

Reviewed by eLife