Systems Biology

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 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

A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell Model

1. James D Pearce
2. Sara E Simmonds
3. Gita Mahmoudabadi
4. Lakshmi Krishnan
5. Giovanni Palla
6. Ana-Maria Istrate
7. Alexander Tarashansky
8. Benjamin Nelson
9. Omar Valenzuela
10. Donghui Li
11. Stephen R. Quake
12. Theofanis Karaletsos

Reviewed by Arcadia Science

Amino acid repeat signatures underlying human-pathogen interactions

1. Anjali Kumari Singh
2. Nagashree Rachote
3. Anushka Agrawal
4. Vaidehi Sharma
5. Keertana Sai Kappagantula
6. Rajashekar Varma Kadumuri
7. Sreenivas Chavali

Reviewed by Arcadia Science

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

Perturbation-response analysis of in silico metabolic dynamics revealed hard-coded responsiveness in the cofactors and network sparsity

1. Yusuke Himeoka
2. Chikara Furusawa

• Curated by eLife

  eLife Assessment

  This valuable study uses dynamic metabolic models to compare perturbation responses in a bacterial system, analyzing whether they return to their steady state or amplify beyond the initial perturbation. The evidence supporting the emergent properties of perturbed metabolic systems to network topology and sensitivity to specific metabolites is solid.

Reviewed by eLife

Revealing global stoichiometry conservation architecture in cells from Raman spectral patterns

1. Ken-ichiro F Kamei
2. Koseki J Kobayashi-Kirschvink
3. Takashi Nozoe
4. Hidenori Nakaoka
5. Miki Umetani
6. Yuichi Wakamoto

• Curated by eLife

  eLife Assessment

  This paper reports the fundamental finding of how Raman spectral patterns correlate with proteome profiles. The authors then go further to show that this can be used to infer global stochiometric regulation of the proteomes. These findings are likely general and the authors provide compelling evidence by analyzing bacterial and human cells but there are some suggestions provided below to make the work clearer and more accessible for it to reach a broader audience.

Reviewed by eLife, Arcadia Science