European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

When Trajectory Inference Fails: The Challenges of Inferring Dynamical Models from Single-Cell RNA-seq Data

MS160-05

14 Jul 2026, 17:20

20m

11.01 - HS (University of Graz)

Minisymposium Talk Systems Biology and Biochemical Networks Stochastic Modelling for Inference with Gene Expression data: Methods and Applications

Speaker

Meichen Fang

Description

Single-cell RNA sequencing (scRNA-seq) enables inference of cellular trajectories from snapshots of differentiating cells. However, in many cases the inferred "pseudotime" does not have a clear mechanistic interpretation. We developed a principled trajectory inference framework based on biophysical models that can estimate interpretable parameters including process time and transcriptional rates \cite{f}. Based on this model, we demonstrate that trajectory inference from scRNA-seq data faces fundamental limitations. Through systematic analysis of simulated and real datasets, we characterise specific failure scenarios where insufficient dynamical information is embedded in the data. Key challenges include unmatched time scales, high measurement noise, and sparse sampling of intermediate states—limitations inherent to the static nature of scRNA-seq measurements. Our findings reveal critical gaps between the promise of trajectory inference and its practical limitations, emphasising the need for careful experimental design and rigorous model assessment.

Bibliography

@article{f,
title={Trajectory inference from single-cell genomics data with a process time model},
author={Fang, Meichen and Gorin, Gennady and Pachter, Lior},
journal={PLoS computational biology},
volume={21},
number={1},
pages={e1012752},
year={2025},
publisher={Public Library of Science San Francisco, CA USA}
}