Saez-Rodriguez Group

Glad to contribute to this collaborative community platform.
A case study shows MCP servers working together - combining BioContextAI Knowledgebase with our omnipath omnipathdb.org MCP (work in progress) to showcase interoperability. Looking forward to see how the ecosystem evolves!

Finally, thanks also to C. Schmidt, M. Matabuena, D. Dimitrov, J. Carbonell, M. Kalxdorf, A. Nilsson, B. Dalpedri, S. Müller-Dott, I. Rigato, B. Wadie, F. Jourdan and N. Poupin for valuable feedback!

We’d like to thank Victoria Hatch (@ebi.embl.org) for her excellent article on CORNETO, “Machine learning to decode complex omics data.” www.ebi.ac.uk/about/news/r...

Special thanks to our funders @PerMedCoE and @deciderproject, and also to David Gomez-Cabrero & Jesper N. Tegnér for the nice N&W.

🤝 Acknowledgements

This work was led by @pablormier.bsky.social in collaboration with @martingarridorc.bsky.social and Attila Gabor, supervised by @juliosaezrod.bsky.social @saezlab.bsky.social

🔗 Ecosystem

CORNETO is powering LIANA+ (liana-py.readthedocs.io), linking cell–cell communication events to intracellular signaling (@scverse.bsky.social). It’s also at the core of NetworkCommons (networkcommons.readthedocs.io), our community-driven platform for network methods.

🚀 Projects using CORNETO

As part of @PerMedCoE, CORNETO helped automate the curation of cell-level simulation models for personalized medicine. We’re now applying CORNETO in @deciderproject to decipher signaling rewiring linked to ovarian cancer drug resistance.

👥 Why multi-sample learning?

Independent network inference per sample can inflate differences and miss shared mechanisms. CORNETO optimises across all samples, revealing common structures, reducing noise and overfitting.

📚 What can you do with CORNETO?

• Extend flux balance analysis (FBA) to multi-sample scenarios
• Infer metabolic networks, intracellular signalling, and protein interaction modules
• Construct biologically-informed neural networks, bridging network biology & machine learning

✨ Key features of CORNETO:

• Unified framework for context-specific network inference
• Supports diverse omics data & network types
• Enables joint multi-sample analysis for robust, interpretable results
• Flexible: add your own constraints, hypotheses, or network models

🍦 What is CORNETO?

CORNETO is a general framework implemented in Python for inferring biological networks, such as signalling, metabolic, or protein-protein interactions, by integrating omics data with prior biological knowledge using a unified, optimisation-based approach.

🎉 The revised version of CORNETO, our unified Python framework for knowledge-driven network inference from omics data, is published in peer reviewed form
🔗 Paper: www.nature.com/articles/s42...
📖 News & Views: www.nature.com/articles/s42...
💻 Code: corneto.org
🧵 Thread 👇

Four more days to apply to our postdoctoral position 👇

Four more days to apply to our curator position 👇

Don't miss talks in our sponsored Dialogue for Reverse Engineering Assessment and Methods – DREAM – session, kicking off with a talk by our Head of Research, @juliosaezrod.bsky.social on benchmarking foundation models in biology

www.iscb.org/ismbeccb2025...

#ISMB2025

Our group @ebi.embl.org‬ is a host for the ARISE2 MSCA-funded post-doc fellowship, in collab with @bioimagearchive.bsky.social‬ and ‪@scilifelab.se (esp. A Mezger and S. Giacomello) to support the development of next-gen spatial data infrastructures. Apply by 30/09
www.embl.org/training/ari...

Postdoctoral Fellow in computational modelling of NeuroImmune Mechanisms of Neurodegeneration (Open Targets) Your role As a Postdoctoral Fellow on the NeuroImmune Open Targets project, you will develop and apply integrative computational pipelines to dissect immune-cell phenotypes and cell–cell interactions ...

Postdoc position in our group at @ebi.embl.org about analyzing single cell data sets to better understand and treat neurodegen diseases, in collab with A Bassett, M Lotfollahi, @bayraktarlab.bsky.social, M Strauss and others within Open Targets. Deadline 20/07.

Please share

tinyurl.com/87wj466m

Scientific Database Curator – Fibrosis Transcriptomics We are seeking a technically skilled and biologically literate Scientific Database Curator to join a focused initiative on fibrosis research, hosted at EMBL-EBI in collaboration with the Saez-Rodrigue...

We have a curator position at @ebi.embl.org to work with GSK within a project to understand fibrotic processes (no PhD required). Deadline 20th July.

Please share 🙏

embl.wd103.myworkdayjobs.com/en-US/EMBL/j...

Leveraging data as a patient–scientist: frustrations and opportunities - Nature Reviews Nephrology The transition from data scientist to patient–scientist has given me new perspectives into clinical research and strengthened my commitment to open science. Although limitations on data availability h...

I wrote a short piece on how becoming an IgA Nephropathy patient has changed my perspective on biomedical research, developing an appreciation for the challenges in data interpretability & availability and the importance of patient engagement www.nature.com/articles/s41...

This paper was reviewed through @reviewcommons.org— great experience + highly recommended!

This study was led by Nadine Tuechler and @miraburtscher.bsky.social and we thank everyone involved, @martingarridorc.bsky.social, @juliosaezrod.bsky.social, @savitski-lab.bsky.social, @rkramann.bsky.social, + those not on Bsky, sp the Pepperkok lab, and EMBL CFs.

This study offers a comprehensive multi-omic dataset and molecular network models of kidney fibrosis, freely available to advance community-driven insights into #CKD in future.

Using #siRNA knockdowns we validated these predictions and uncovered molecular processes modulating FLI1 and E2F1 transcriptional activity and their downstream effects.

We inferred transcription factor and kinase activities using #DecoupleR tinyurl.com/decouple-R, integrated them w/ the secretomics data and generated mechanistic hypotheses related to transcriptional events modulating fibrotic progression using #COSMOS tinyurl.com/cosmos-R

We profile fibrotic progression in response to #TGFb by quantifying COL1 deposition over time in combination with molecular data using #transcriptomics, #phosphoproteomics and #secretomics.

The final version of our multi-omics study on kidney fibrosis is out now (tinyurl.com/kidneyfibMSB). Together w/ Pepperkok + Savitski labs @embl.org, we present a time-resolved #multiomics + computational network modeling approach in combination w/ phenotypic assays to study #kidneyfibrosis

👋 Wishing @paubadiam.bsky.social all the best as he leaves our lab to join the Stanford Department of Genetics in @anshulkundaje.bsky.social lab‬! Looking forward to his future contributions to understanding gene regulation through sequence-to-function deep learning models 💻

🔧 decoupler 2.0.6 is out thanks to @paubadiam.bsky.social‬, now as a core @scverse.bsky.social package scverse.org

This release includes a streamlined API aligned with scverse standards, updated vignettes, and a new one on pseudotime enrichment analysis 👇
decoupler.readthedocs.io/en/latest/

What is the current bottleneck in mapping molecular interaction networks? Network biologists today have access to a rich assortment of interaction networks produced by assays such as affinity purification-mass spectrometry (AP-MS), yeast two-hybrid (Y2H) screening, co-fract...

In this Voices article, our @martingarridorc.bsky.social & @juliosaezrod.bsky.social discuss key strategies for building context-specific molecular networks: knowledge and data-driven methods, + future directions in the field bridging to structures and perturbations tinyurl.com/z8t6yych

We also gratefully acknowledge the support from @lisymcancer.bsky.social and all other funders 🙏 and would like to thank all method developers for making the tools available to the community 💙