Systems Biology

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

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

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

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

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

De Novo Multi-Omics Pathway Analysis 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