A Unified Dynamical-Systems and Control-Theoretic Model for Single-Cell Fate Dynamics
Redd, D. M.; Green, S. G.; Terooatea, T. W.
Show abstract
Single-cell technologies now resolve cell-fate transitions, yet most analyses remain descriptive rather than predictive. This model unifies pseudotime (geometry), RNA velocity (local direction), optimal transport (OT; distributional evolution) and Schrodinger-bridge approaches (stochastic trajectories) within a stochastic dynamical-systems and control lens under partial observability1,2,3,4. We provide a minimal bridge from Chemical Master Equation models to SDE/Fokker-Planck descriptions and quasi-potentials, clarify what can and cannot be identified from snapshot data, and outline practical experimental design (time points, modalities, perturbations)5,6,7. We summarize method-specific assumptions, strengths and failure modes; present case studies (iPSC reprogramming, pancreas endocrinogenesis, hematopoiesis at scale); and detail a 10-step workflow with uncertainty propagation and reporting standards3,8,9. Finally, we recast intervention as a control problem: the realistic objective is probabilistic programmability-shifting terminal fate distributions with minimal inputs and preserved viability-rather than deterministic state-to-state command10.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation 94%
- Estimating single cell clonal dynamics in human blood using coalescent theory 93%
- ARTEMIS integrates autoencoders and schrodinger bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell data 93%
Similar papers in this journal
Similar papers in this journal
- CellChat for systematic analysis of cell-cell communication from single-cell and spatially resolved transcriptomics 92%
- Jointly Defining Cell Types from Multiple Single-Cell Datasets Using LIGER 91%
- Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R, and GenePattern Notebook implementations of CoGAPS 91%
Similar papers in this journal
- OmicVerse: A single pipeline for exploring the entire transcriptome universe 94%
- GRouNdGAN: GRN-guided simulation of single-cell RNA-seq data using causal generative adversarial networks 94%
- Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.