ArticlePLoS computational biology2022
Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie.Genome research · 2026Article
- Learning collective multicellular dynamics with an interacting mean field neural SDE model.PLoS computational biology · 2026Article
- Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data.NPJ systems biology and applications · 2025Review
- Reconstructing gene network structure and dynamics from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Multicondition and multimodal temporal profile inference during mouse embryonic development.Genome research · 2025Article
- Quantifying Landscape and Flux from Single-Cell Omics: Unraveling the Physical Mechanisms of Cell Function.JACS Au · 2025Review
- ChromMovie: A Molecular Dynamics Approach for Simultaneous Modeling of Chromatin Conformation Changes from Multiple Single-Cell Hi-C Maps.bioRxiv : the preprint server for biology · 2025Article
- Computational modeling of single-cell dynamics data.Briefings in bioinformatics · 2025Review
- Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis.Entropy (Basel, Switzerland) · 2025Review
- scEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.BMC bioinformatics · 2024Article
- scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes.Genome biology · 2024Article
- Multi-condition and multi-modal temporal profile inference during mouse embryonic development.bioRxiv : the preprint server for biology · 2024Article
- Tipping points in epithelial-mesenchymal lineages from single-cell transcriptomics data.Biophysical journal · 2024Article
- A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data.Bioinformatics (Oxford, England) · 2024Article
- An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental research · 2024Article
- MDIC3: Matrix decomposition to infer cell-cell communication.Patterns (New York, N.Y.) · 2024Article
- Reconstructing growth and dynamic trajectories from single-cell transcriptomics data.Nature machine intelligence · 2024Article
- scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs.Cell systems · 2023Article
- Inferring structural and dynamical properties of gene networks from data with deep learning.NAR genomics and bioinformatics · 2022Article
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3 authors.
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Abstract
Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in a nonlinear quadratic term. We then recast the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the adjoint method of optimal control. We evaluated GraphFP on the time series scRNA-seq data set of embryonic murine cerebral cortex development. We illustrated that it 1) reconstructs cell state potential energy, which is a measure of cellular differentiation potency, 2) faithfully charts the probability flows between paired cell states over the dynamic processes of cell differentiation, and 3) accurately quantifies the stochastic dynamics of cell type frequencies on probability simplex in continuous time. We also illustrated that GraphFP is robust in terms of cluster labelling with different resolutions, as well as parameter choices. Meanwhile, GraphFP provides a model-based approach to delineate the cell-cell interactions that drive cell differentiation. GraphFP software is available at https://github.com/QiJiang-QJ/GraphFP.
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