Speaker
Description
Cellular phenotypes exhibit remarkable diversity, reflecting the complex functional states of individual cells. Although phenotypic diversity has traditionally been assessed at the transcriptomic level, recent advances in single-cell technologies have shifted attention toward metabolomic phenotyping, which provides a more direct reflection of cellular function. Nonetheless, the high dimensionality of the metabolome poses significant challenges for both measurement and computational classification. A fundamental question therefore arises: which subset of species in a reaction network suffices to represent the system's overall state? To address this, we develop a novel theory that relies solely on the structural topology of chemical reaction networks to identify indicator species—those whose concentrations uniquely determine all others and thereby distinguish multistable equilibria. An efficient algorithm implementing this theory is applied to biochemical pathway databases. Numerical experiments show that phenotypic classification using only these indicator species achieves accuracy that matches or exceeds that of the full metabolite set, while demonstrating superior robustness to measurement noise. These results establish a rigorous, topology-based foundation for indicator species identifications, advancing metabolomic phenotyping and biomarker discovery.
Bibliography
@article{Huang_Mochizuki_Okada_2025,
title={Identifying Phenotype-Indicative Molecules from the Structure of Biochemical Reaction Networks},
url={http://biorxiv.org/lookup/doi/10.1101/2025.10.21.683796}, DOI={10.1101/2025.10.21.683796},
author={Huang, Yong-Jin and Mochizuki, Atsushi and Okada, Takashi},
year={2025}, month=oct, language={en}
}
@article{Huang_Okada_Mochizuki_2025,
title={Uncovering bifurcation behaviors of biochemical reaction systems from network topology},
volume={15},
ISSN={2045-2322},
url={https://www.nature.com/articles/s41598-025-10688-6},
DOI={10.1038/s41598-025-10688-6},
number={1},
journal={Scientific Reports},
author={Huang, Yong-Jin and Okada, Takashi and Mochizuki, Atsushi},
year={2025}, month=july, pages={27596}, language={en}
}
