Systems Biology

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

1. Zhiwei Huang
2. Songhao Luo
3. Zihao Wang
4. Zhenquan Zhang
5. Benyuan Jiang
6. Qing Nie
7. Jiajun Zhang

• Curated by eLife

  eLife Assessment

  This study presents DeepTX, a valuable methodological tool that integrates mechanistic stochastic models with single-cell RNA sequencing data to infer transcriptional burst kinetics at genome scale. The approach is broadly applicable and of interest to subfields such as systems biology, bioinformatics, and gene regulation. The evidence supporting the findings is solid, with appropriate validation on synthetic data and thoughtful discussion of limitations related to identifiability and model assumptions.

Reviewed by eLife

Exploiting fluctuations in gene expression to detect causal interactions between genes

1. Euan Joly-Smith
2. Mir Mikdad Talpur
3. Paige Allard
4. Fotini Papazotos
5. Laurent Potvin-Trottier
6. Andreas Hilfinger

• Curated by eLife

  eLife Assessment

  By taking advantage of noise in gene expression, this important study introduces a new approach for detecting directed causal interactions between two genes without perturbing either. The main theoretical result is supported by a proof. Preliminary simulations and experiments on small circuits are solid, but further investigations are needed to demonstrate the broad applicability and scalability of the method.

Reviewed by eLife

A push-pull system of repressors matches levels of glucose transporters to extracellular glucose in budding yeast

1. Luis Fernando Montaño-Gutierrez
2. Marc Sturrock
3. Iseabail Farquhar
4. Kevin Correia
5. Vahid Shahrezaei
6. Peter S. Swain

Reviewed by Review Commons

The zoo of the gene networks capable of pattern formation by extracellular signaling

1. Kevin Martinez-Anhom
2. Isaac Salazar-Ciudad

• Curated by eLife

  eLife Assessment

  The study presents valuable theoretical insights by attempting to classify pattern-forming gene subnetworks and exploring their potential mechanisms. However, the results are incomplete, as they rely on oversimplified models, limited classifications, and assumptions that may not hold in more complex or realistic scenarios.

Reviewed by eLife

Single-mRNA imaging and modeling reveal coupled translation initiation and elongation rates

1. Irene Lamberti
2. Jeffrey A Chao
3. Cédric Gobet
4. Felix Naef

• Curated by eLife

  eLife Assessment

  This important study provides evidence for dynamic coupling between translation initiation and elongation that can help maintain low ribosome density and translational homeostasis. The authors combine single-molecule imaging with a new approach to analyze mRNA translation kinetics using Bayesian modeling. This work is overall solid, but certain key aspects and model assumptions could be strengthened.

Reviewed by eLife

Ribosome demand links transcriptional bursts to protein expression noise

1. Sampriti Pal
2. Upasana Ray
3. Riddhiman Dhar

• Curated by eLife

  eLife Assessment

  This study focuses on a previously reported positive correlation between translational efficiency and protein noise. Using mathematical modeling and analysis of experimental data the authors reach the valuable conclusion that this phenomenon arises due to ribosomal demand. While some aspects of the work appear to be incomplete, the results have the potential to be of value and interest to the field of gene expression.

Reviewed by eLife

The Product neutrality function defining genetic interactions emerges from mechanistic models of cell growth

1. Lucas Fuentes Valenzuela
2. Paul Francois
3. Jan M Skotheim

• Curated by eLife

  eLife Assessment

  The paper addresses the question of gene epistasis and asks what is the correct null model for which we should declare no epistasis. By reanalyzing synthetic gene array datasets regarding single and double-knockout yeast mutants, and considering two theoretical models of cell growth, the authors reach the valuable conclusion that the product function is a good null model. While the justification of some assumptions is incomplete, the results have the potential to be of value to the field of gene epistasis.

Reviewed by eLife

Designing biochemical circuits with tree search

1. Pranav S Bhamidipati
2. Matthew Thomson

• Curated by eLife

  eLife Assessment

  This manuscript presents a valuable computational tool for identifying 3-5 gene regulatory network topologies capable of generating oscillatory dynamics. The application of Monte Carlo Tree Search to circuit design is novel and effectively expands the scale at which non-linear behaviours can be explored in silico. The efficiency of the proposed algorithm is convincing, and the work will be of interest to the systems and synthetic biology communities. While the evolutionary implications remain unclear, the methodological contribution represents a significant advance in the field.

Reviewed by eLife

A mathematical model for ketosis-prone diabetes suggests the existence of multiple pancreatic β-cell inactivation mechanisms

1. Sean A Ridout
2. Priyathama Vellanki
3. Ilya Nemenman

• Curated by eLife

  eLife Assessment

  This theoretical study makes a useful contribution to our understanding of a subtype of type 2 diabetes - ketosis-prone diabetes mellitus (KPD) - with a potential impact on our broader understanding of diabetes and glucose regulation. The article presents an ordinary differential equation-based model for KPD that incorporates a number of distinct timescales - fast, slow, as well as intermediate, incorporating a key hypothesis of reversible beta cell deactivation. The presented evidence is solid and shows that observed clinical disease trajectories may be explained by a simple mathematical model in a particular parameter regime.

Reviewed by eLife

Dynamic sensitivity analysis of a mathematical model describing the effect of the macroalgae Asparagopsis taxiformis on rumen fermentation and methane production under in vitro continuous conditions

1. Paul Blondiaux
2. Tristan Senga Kiessé
3. Maguy Eugène
4. Rafael Muñoz-Tamayo

Reviewed by Peer Community in Animal Science