GEO series

Improving genome-scale metabolic model simulations by measuring exchange fluxes during exponential growth phase

GSE293588 Homo sapiens (human) RNA-seq (gene expression) 3 samples Submitted 2025/04/07 Platform GPL30173

At a glance

RNA-seq (gene expression) · human · 3 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 3 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper: Improved flux profiling in genome-scale modeling of human cell metabolismCell reports methods · 2026

Summary (written by the submitters)

With the state-of-the-art techniques currently available, measuring all metabolic reactions in a cell remains impossible. Therefore, systems biology approaches such as genome-scale metabolic models (GEMs) should be used to get a holistic view of metabolism. To reduce the solution space in GEM simulations, models are often constrained with exchange fluxes.

Read the full summary ↓

However, previous literature that used exchange flux constraints violate the steady-state assumption necessary for GEMs, by inferring the flux from a single metabolomic sample. Here, we present the regression during exponential growth phase (REGP) method, which calculates exchange fluxes only during the log phase, thereby assuring measurements are conducted during metabolic steady-state. The REGP method was tested this method using MCF10A breast epithelial cells. We report that exchange fluxes are generally higher using the REGP method, highlighting the importance of measuring exchange fluxes during steady-state. Even changes in flux direction were observed, which indicates a metabolic shift between the lag phase and the exponential growth phase. While models constrained with previously reported exchange fluxes were infeasible or failed to predict the experimental growth rate, the REGP-constrained GEM accurately predicted the correct growth rate. Flux variability analysis revealed that even with constraints on the exchange of metabolites in central carbon metabolism and amino acid metabolism, these pathways still have intermediate variability in reaction flux. The results of this study suggest that future research should use the REGP method to determine exchange fluxes when these are used as constraints for GEMs, this will provide a better understanding of the complex mechanisms involved in metabolism.

Published in

Improved flux profiling in genome-scale modeling of human cell metabolism

Huijer CAM, Jiao X, Chen Y et al. · Cell reports methods 2026 · PMID 41529686 · doi:10.1016/j.crmeth.2025.101275

This dataset

Open GSE293588 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE293588_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1245327 and SRA study SRP575980. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41529686) ↗

Samples in this study

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+ 3 more — browse all 3 samples with per-sample file links →

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