Systems Biology

Identifying and targeting abnormal mitochondrial localization associated with psychosis

1. Marzieh Haghighi
2. Donna McPhie
3. Mohammad Rohban
4. Erin Weisbart
5. David J Logan
6. Kyle W Karhohs
7. Suzann M Babb
8. Jessica D Ewald
9. Johan Fredin Haslum
10. Beth A Cimini
11. Shantanu Singh
12. Bruce M Cohen
13. Anne E Carpenter

Reviewed by Review Commons

The lipidomic architecture of the mouse brain

1. Luca Fusar Bassini
2. Halima Hannah Schede
3. Laura Capolupo
4. Leila Haj Abdullah Alieh
5. Francesca Venturi
6. Alessandro Valente
7. Colas Droin
8. Daniel Trejo Banos
9. Irina Khven
10. Ece Z. Asirim
11. Anita Nasrallah
12. Irmak Kaysudu
13. Ekaterina Krymova
14. Giovanni D'Angelo
15. Gioele La Manno

Reviewed by preLights

Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz

1. Christina Schmidt
2. Denes Turei
3. Dimitrios Prymidis
4. Macabe Daley
5. Christian Frezza
6. Julio Saez-Rodriguez

Reviewed by Review Commons

Benchmarking of signaling networks generated by large language models

1. Jeevan Tewari
2. Benjamin W Dahl
3. Jeffrey J Saucerman

• Curated by eLife

  eLife Assessment

  The authors address a hard question and propose a pipeline for using Large Language Models to reconstruct signalling networks as well as to benchmark future models. The findings are valuable for a defined subfield, as the proposed framework allows for assessing such approaches systematically. The overall support is solid, although the present evaluation remains limited in scope and would benefit from a wider range of networks and performance metrics.

Reviewed by eLife

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

Ultrasensitive proteomics uncovers nociceptor diversity and novel pain targets

1. Sampurna Chakrabarti
2. Anuar Makhmut
3. Atena Mohammadi
4. Wenhan Luo
5. Lin Wang
6. Gary R. Lewin
7. Fabian Coscia

Reviewed by PREreview

From Noise to Models to Numbers: Evaluating Negative Binomial Models and Parameter Estimations in Single-Cell RNA-seq

1. Yiling Wang
2. Zhanpeng Shu
3. Zhixing Cao
4. Ramon Grima

Reviewed by Review Commons

Simulation based-inference of epidemiological and phylodynamic models via Neural Posterior Estimation

1. Francesco Pinotti
2. Julien Thézé
3. Xavier Bailly
4. Guillaume Fournié

Reviewed by Arcadia Science

Thymic selection of the T cell receptor repertoire is biased toward autoimmunity in females

1. Hélène Vantomme
2. Valentin Quiniou
3. Leslie Adda
4. Charline Jouannet
5. Vanessa Mhanna
6. Céline Albalaa
7. Pierre Barennes
8. Nicolas Coatnoan
9. Vimala Diderot
10. Johanna Dubois
11. Gwladys Fourcade
12. Kenz Le Gouge
13. Otriv Frédéric Nguekap Tchoumba
14. Martin Pezous
15. Paul Stys
16. Adrien Six
17. Encarnita Mariotti-Ferrandiz
18. David Klatzmann

• Curated by eLife

  eLife Assessment

  This study provides useful insights into addressing the question of whether the prevalence of autoimmune disease could be driven by sex differences in the T cell receptor (TCR) repertoire, correlating with higher rates of autoimmune disease in females. The authors compare male and female TCR repertoires using bulk RNA sequencing, from sorted thymocyte subpopulations in pediatric and adult human thymuses; however, the results do not provide sufficient analytical rigor and incompletely support the central claims.

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 using Raman spectra of E. coli cells from different physiological conditions and found global stoichiometric regulation on proteomes. The authors' findings provide compelling evidence that stoichiometric regulation of proteomes is general through analysis of both bacterial and human cells. In the future, similar methodology can be applied on various tissue types and microbial species for studying proteome composition with Raman spectral patterns.

Reviewed by eLife, Arcadia Science