European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Inferring mass isotopomer distribution dynamics from partial isotopic labeling data: Implications for flux estimation

MS158-04

14 Jul 2026, 11:40

20m

01.15 - HS (University of Graz)

Minisymposium Talk Numerical, Computational, and Data-Driven Methods Methods to integrate -omics data into mechanistic models

Speaker

Anika Küken (Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam)

Description

Isotopically non-stationary metabolic flux analysis (INST-MFA) enables the estimation of intracellular fluxes from time-resolved labeling data but remains limited to medium-scale networks due to experimental constraints on metabolite coverage. Extending INST-MFA toward large-scale models with high confidence in flux estimates requires extensive labeling datasets. Here, we employ a neural network approach to infer complete mass isotopomer distribution (MID) dynamics from partial $^{13}$C-isotopic labeling data. The model is trained on synthetic time-resolved MID datasets generated from a large-scale network of Chlamydomonas central metabolism. We show that MID trajectories can be accurately reconstructed for a broad set of metabolites from limited observable inputs. Furthermore, we apply the approach to labeling data from different green algae and investigate to what extent predicted complete MID datasets can support downstream flux estimation by comparison to previously published flux estimates derived using scale-free constrained regression and a state-of-the-art tool for INST-MFA, INCA \cite{SFCR}. Together, our findings highlight the potential of neural network based MID prediction as a tool toward precise large-scale flux estimation from isotopic labeling data. By augmenting incomplete labeling datasets with predicted MID dynamics, this approach provides a pathway to overcome current limitations in metabolite coverage and improve the reliability of flux estimates in INST-MFA.

Bibliography

@article{SFCR,
title = {A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae},
volume = {14},
issn = {1664-462X},
url = {https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1140829/full},
doi = {10.3389/fpls.2023.1140829},
urldate = {2026-03-20},
journal = {Frontiers in Plant Science},
author = {Küken, Anika and Treves, Haim and Nikoloski, Zoran},
month = nov,
year = {2023},
keywords = {Metabolic Flux Analysis, INST-MFA, regression, 13C labeling, algae},
}