Author

Kate E. Meeson

University of ManchesterORCID 0009-0009-1439-6302Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cancer Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Organism:human· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Jean‐Marc SchwartziD· 3 shared Joanne C. McGrailiD· 1 shared Magnus RattrayiD· 1 shared Rachel Gaffney· 1 shared Stephen S. TayloriD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 6 mutual co-author links

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3 shared papersSystems Biology

Jean‐Marc SchwartzORCID

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3 papers

Recent·Most cited

Systems BiologybioRxivPreprintJun 19, 2026

SIMOFF: Discovering the metabolic objective of the cell

Rachel Gaffney, Magnus Rattray, Jean‐Marc Schwartz, Kate E. Meeson

SIMOFF infers metabolic objectives from flux data, improving gene essentiality predictions in yeast and CHO cells.

DOI 10.64898/2026.06.18.733185

𝕏 PostBluesky

Cancer BiologyCancer & MetabolismPeer-reviewedMar 19, 2026

Transcriptome-Driven Constraint-Based Modelling Reveals Metabolic Targets for Ovarian Cancer

Kate E. Meeson, Joanne C. McGrail, Jean‐Marc Schwartz, Stephen S. Taylor

This study uses transcriptome-driven constraint-based modeling to identify metabolic targets in ovarian cancer, revealing TPI1 as a potential therapeutic target supported by experimental validation.

DOI 10.1186/s40170-026-00425-6

humanbulk RNA-seq

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BioinformaticsbioRxivPreprintMar 9, 2023

Constraint-based modelling predicts metabolic signatures of low- and high-grade serous ovarian cancer

Kate E. Meeson, Jean‐Marc Schwartz

This study uses constraint-based modeling to predict metabolic differences between low- and high-grade serous ovarian cancer subtypes, supported by CRISPR-Cas9 and literature data.

1 citation

humanbulk RNA-seq

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