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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint introduces SUDO, a simulation-free computational framework for unbalanced dynamic optimal transport that extends beyond quadratic penalties to general convex growth penalties for inferring cellular dynamics. The method matches existing analytical solutions on Wasserstein-Fisher-Rao benchmarks while enabling asymmetric proliferation-dominant penalties on synthetic and single-cell datasets, but lacks peer review and clinical validation.
Preprint. Single-cell transcriptomic datasets (synthetic and experimental); cellular dynamics inference from unpaired snapshots. Intervention: SUDO algorithm for unbalanced dynamic optimal transport with general convex growth penalties. Compared with: Analytical WFR solutions and simulation-based UDOT methods.
SUDO matches accuracy of analytical WFR solution-driven algorithms while outperforming simulation-based methods in computational speed Concave growth penalties lead to degenerate solutions where growth and transport are separated SUDO produces more plausible trajectories and growth estimates on synthetic and single-cell datasets when using asymmetric proliferation-dominant penalties
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This is a foundational computational method paper with potential to improve inference of cellular proliferation and apoptosis from single-cell RNA-seq data. Clinical utility remains to be demonstrated through application to disease-relevant datasets and validation against orthogonal measurements.
This is a methodological development paper presenting a novel computational algorithm (SUDO) for modeling cellular dynamics from single-cell data, demonstrated on synthetic and real datasets but lacking clinical validation or peer review.
As stated by the source record.
This is a foundational computational method paper with potential to improve inference of cellular proliferation and apoptosis from single-cell RNA-seq data. Clinical utility remains to be demonstrated through application to disease-relevant datasets and validation against orthogonal measurements.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and population growth or death. Unbalanced dynamic optimal transport (UDOT) addresses this by penalizing growth along transport paths, making the choice of growth penalty a key way to encode biological priors on proliferation and apoptosis. However, existing UDOT solvers either rely on computationally expensive NeuralODE simulations or depend on analytical solutions of conditional paths, restricting their efficiency solely to quadratic penalties, i.e. Wasserstein-Fisher-Rao (WFR) geodesics. To enable an efficient UDOT solver for general growth penalties, we first show that concave growth penalties lead to degenerate solutions where growth and transport are separated. We then introduce \textbf{S}imulation-free \textbf{U}nbalanced \textbf{D}ynamic \textbf{O}ptimal transport (SUDO), a simulation-free framework for UDOT with general non-quadratic convex growth penalties. SUDO learns the conditional paths and transport costs, solves the induced semi-coupling problem, and subsequently leverages unbalanced flow matching to achieve a simulation-free solution. On WFR benchmarks, SUDO matches the accuracy of efficient, analytical solution-driven algorithms while outperforming simulation-based methods in computational speed. Beyond WFR, SUDO supports asymmetric penalties that encode proliferation-dominant priors and produce more plausible trajectories and growth estimates on synthetic and single-cell datasets.
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