Systems Biology

De Novo multi-omics pathway analysis (DMPA) designed for prior data independent inference of cell signaling pathways

1. Katri Vaparanta
2. Johannes A. M. Merilahti
3. Veera K. Ojala
4. Klaus Elenius

• Curated by eLife

  eLife assessment:

  This manuscript describes development of a new algorithm for integrative analysis of multi-omics data. This work should be of potential interest to scientists performing bioinformatic pathway discovery in multi-omic datasets especially those that relate to signaling.

Reviewed by eLife

ChemChaste: Simulating spatially inhomogeneous biochemical reaction–diffusion systems for modeling cell–environment feedbacks

1. Connah G M Johnson
2. Alexander G Fletcher
3. Orkun S Soyer

Reviewed by GigaScience

Dynamics of co-substrate pools can constrain and regulate metabolic fluxes

1. Robert West
2. Hadrien Delattre
3. Elad Noor
4. Elisenda Feliu
5. Orkun S Soyer

• Curated by eLife

  eLife assessment

  This manuscript presents an important mathematical analysis on metabolic "co-substrates" and how their cycling can affect metabolic fluxes. Through mathematical analysis of simple network motifs, it shows the impact on constraining metabolic fluxes and the applied mathematical modeling/simulation approaches and the statistical analysis to compare predictions with data from previous studies offer convincing support for the potential biological relevance of co-substrate cycling. The work will be of interest to researchers who study microbial metabolism and metabolic engineering. However, part of this analysis remains unclear and would benefit from clarification.

Reviewed by eLife

Evolved bacterial resistance to the chemotherapy gemcitabine modulates its efficacy in co-cultured cancer cells

1. Serkan Sayin
2. Brittany Rosener
3. Carmen G Li
4. Bao Ho
5. Olga Ponomarova
6. Doyle V Ward
7. Albertha JM Walhout
8. Amir Mitchell

• Curated by eLife

  eLife assessment

  This fundamental work advances our understanding of how bacteria evolve to resist drugs used for cancer treatment and how this could potentially affect drug efficacy and treatment outcome. The data were collected and analyzed using a solid methodology and can be used as a starting point for functional studies of the interaction between microbiome interactions and cancer drug treatment. The findings will be of broad interest to microbiologists and organismal biologists interested in the role of microbiomes in drug responses.

Reviewed by eLife

Evolutionary conservation of sequence motifs at sites of protein modification

1. Shuang Li
2. Henrik G. Dohlman

Reviewed by Review Commons

PhysiPKPD: A pharmacokinetics and pharmacodynamics module for PhysiCell

1. Daniel Bergman
2. Lauren Marazzi
3. Mukti Chowkwale
4. Deepa Maheshvare M
5. Supriya Bidanta
6. Tarunendu Mapder
7. Jialun Li

Reviewed by GigaByte

Transcription-replication interactions reveal principles of bacterial genome regulation

1. Andrew W. Pountain
2. Peien Jiang
3. Tianyou Yao
4. Ehsan Homaee
5. Yichao Guan
6. Magdalena Podkowik
7. Bo Shopsin
8. Victor J. Torres
9. Ido Golding
10. Itai Yanai

Reviewed by Arcadia Science

Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function

1. Jeffrey Molendijk
2. Ronnie Blazev
3. Richard J Mills
4. Yaan-Kit Ng
5. Kevin I Watt
6. Daryn Chau
7. Paul Gregorevic
8. Peter J Crouch
9. James BW Hilton
10. Leszek Lisowski
11. Peixiang Zhang
12. Karen Reue
13. Aldons J Lusis
14. James E Hudson
15. David E James
16. Marcus M Seldin
17. Benjamin L Parker

• Curated by eLife

  eLife assessment

  This manuscript will be of broad interest to those working in the genetics of complex diseases, with the results strongly supporting the author's primary claims. Overall, this is an important study that demonstrates the power of proteomics-based systems genetics studies in the mouse.

Reviewed by eLife

Dynamic metabolome profiling uncovers potential TOR signaling genes

1. Stella Reichling
2. Peter F Doubleday
3. Tomas Germade
4. Ariane Bergmann
5. Robbie Loewith
6. Uwe Sauer
7. Duncan Holbrook-Smith

Reviewed by Review Commons

Patterns of interdivision time correlations reveal hidden cell cycle factors

1. Fern A Hughes
2. Alexis R Barr
3. Philipp Thomas

• Curated by eLife

  eLife assessment

  This work makes an important contribution to the study of the cell cycle and inferring mechanisms by studying correlations in division timing between single cells. By treating the problem in a general way and computing over lineage trees, the authors can infer timescales in the underlying mechanism. This approach is able to detect a general role of circadian rhythms in cell cycle control. The method is validated on data sets from bacterial and mammalian cells and can suggest when additional measurements are needed to distinguish competing models. This paper is of broad interest to scientists in the fields of cell growth, cell division, and cell-cycle control.

Reviewed by eLife