Author

Ilias Toumpe

École Polytechnique Fédérale de LausanneORCID 0009-0008-6375-3567Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Systems Biology· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Ljubiša MiškovićiD· 3 shared Vassily Hatzimanikatis AiD· 3 shared Bharath NarayananiD· 1 shared Daniel WeilandtiD· 1 shared Georgios FengosiD· 1 shared Jakob Sebastian Behler· 1 shared María MasidiD· 1 shared Oussama Gabouj· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

9 top collaborators · 21 mutual co-author links

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3 shared papersSystems Biology

Ljubiša MiškovićORCID

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3 papers

Recent·Most cited

Scalable biophysical constraints for physiologically consistent metabolic states

Ilias Toumpe, Daniel Weilandt, Bharath Narayanan, Georgios Fengos, Vassily Hatzimanikatis A, Ljubiša Mišković

The paper introduces METEOR-K, a framework that incorporates metabolite turnover rates as constraints to improve the physiological consistency of steady-state metabolic models.

bioRxiv · Jul 3, 2026 · PreprintSystems Biology

Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration

Subham Choudhury, Ilias Toumpe, Oussama Gabouj, Jakob Sebastian Behler, Vassily Hatzimanikatis A, Ljubiša Mišković

This paper presents a framework for adapting generative neural networks to build kinetic models for different organisms and physiological contexts by exploring latent space.

Nature Communications · Apr 20, 2026Peer-reviewedSystems Biology

Multi-omics-driven kinetic modeling reveals metabolic adaptations and vulnerabilities in BRCA1-deficient ovarian cancer

Ilias Toumpe, María Masid, Vassily Hatzimanikatis A, Ljubiša Mišković

This study builds kinetic models of ovarian cancer metabolism to identify metabolic vulnerabilities and drug targets in BRCA1-deficient cells.

bioRxiv · Oct 22, 2025 · PreprintCancer Biology