Biology subjects

Green, S. G.

Publications and source records attributed to Green, S. G..

3 recordsLinked to original sources

bioRxiv2026

Label-free flow operators and stability operators from single-cell time courses: what local dynamical operators can and cannot identify

Local Jacobians of single-cell dynamics are used to locate unstable cells along trajectories, but what they identify from measured data has rarely been tested against independent truth. We present scJDO2, which separates two per-cell operators by the time information they require: a score operator, the Hessian of a score-matched log density, available from any snapshot; and a flow operator, the Jacobian of a drift fitted to a measured velocity, here estimated label-free from a sampled time course by entropic optimal transport. Both are summarized in a shared operator space of magnitude-free descriptors, with the invariance of each stated, and evaluated with pre-registered tests. On synthetic data calibrated to six real datasets, the flow operator from the time course alone separated fate-bifurcation cells from committed cells on held-out seeds (AUC 0.89-1.00) with a-priori settings; tuning on development seeds overfit. On LARRY lineage-traced hematopoiesis, its fitted drift predicted the clonal displacement of barcoded cells beyond population movement (Spearman 0.56, permutation p < 0.001; 0.50 after removing the mean shift) and a held-out sampling day better than mean-displacement and no-movement baselines (energy distance 0.69, 95 % CI 0.66-0.73, vs 0.87 and 0.88); lineage thus validates the velocity, not {lambda}. Mini-batch transport reproduced dense transport (velocity cosine 0.9998) and scaled to all 130,887 cells (transport 5 min and 2.5 GB; drift fit and Jacobians 21 min and 1.3 GB). On an independent simulator it detected two of three bifurcations and two of three cycles and never reported instability on linear trajectories. Several readings failed. Neither operator recovers the values of the true Jacobian; the flow operators leading eigenvalue ranks branching cells empirically. The flow operator depends on the transport metric and inverts on Factor Analysis. Operator archetypes isolated bifurcation cells but did not outperform expression clustering and were unstable across fit seeds. The score operator did not predict CRISPRi knockdown responses or sister-cell fate coherence. A confirmatory test of transition timing could not be constructed, and diffusion is not identifiable at typical sampling intervals. The released software reports these diagnostics.

bioinformatics↗

bioRxiv2026

Time-resolved operator archetypes characterize dynamical sensitivity during cell-state transitions

During development, cells traverse gene expression states where their local dynamical sensitivity changes sharply, yet existing computational methods provide limited access to when and where this sensitivity peaks along a trajectory. Here we introduce scJDO (single-cell Jacobian Differential Operators), a framework that characterizes how local dynamical sensitivity evolves during cell fate transitions, together with an explicit account of what that representation can and cannot recover from snapshot data. scJDO treats time-indexed Jacobians as explicit analytical objects, projecting the temporal sequence of operators into a shared subspace and decomposing it into recurrent operator archetypes with interpretable temporal activation profiles. Unlike methods that derive Jacobians from splicing-kinetic vector fields, scJDO learns a neural drift field directly from cell-state geometry via diffusion score matching, enabling Jacobian analysis on trajectory-resolved scRNA-seq datasets regardless of splicing-data availability. Applied to a dense time-course of induced pluripotent stem cell (iPSC) reprogramming, scJDO resolves a quantitative operator-level signature that distinguishes diverted from productive fate: the productive trajectory executes a sequential handoff from an early MEF-exit operator regime to a late pluripotency-associated regime, whereas the diverted trajectory maintains the early regime and instead activates a distinct stress-associated archetype. We validate scJDO across four settings: synthetic benchmarks with analytically known ground truth, branching hematopoiesis, dense real time-course reprogramming, and a perturbational setting using Schrodinger bridges in K562 CRISPRi Perturb-seq. We compare against the two most widely used single-cell Jacobian methods on a dataset where all three are runnable, finding that scJDO shares significantly more gene-level and directional operator structure with Dynamo than expected by chance while providing operator-level analysis on datasets without splicing kinetics. We further characterize the boundary of the representation directly. At a fate-decision saddle, eight mathematically distinct readouts of the same learned drift field are consistent with a single explanation: a drift field fit to snapshot density reproduces density-dominant separation between committed branches rather than the low-variance transverse instability that defines the decision. Together, scJDO provides an operator-level view of single-cell dynamics and an explicit characterization of its own identifiability boundary.

bioinformatics↗

bioRxiv2026

A Unified Dynamical-Systems and Control-Theoretic Model for Single-Cell Fate Dynamics

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.

systems biology↗