A dynamical systems treatment of transcriptomic trajectories in hematopoiesis
Bibliographic record
Abstract
Inspired by Waddington’s illustration of an epigenetic landscape, cell-fate transitions have been envisioned as bifurcating dynamical systems, wherein the dynamics of an exogenous signal couples to a cell’s enormously complex signaling and transcriptional machinery, eliciting a qualitative transition in the collective state of a cell – its fate. It remains unclear, however, whether the dynamical systems framework can go beyond a word-based caricature of the system and provide sharp quantitative insights that further our understanding of differentiation. Single-cell RNA sequencing (scRNA-seq), which measures the distributions of possible transcriptional states in large populations of differentiating cells, provides an alternate view, in which development is marked by the individual concentration variations of a myriad of genes. Here, starting from formal mathematical derivations, we challenge these transcriptomic trajectories to a rigorous statistical evaluation of whether they display signatures consistent with bifurcations. After pinpointing bifurcations along transcriptomic trajectories of the neutrophil branch of hematopoeitic differentiation we are able to further leverage the primitive features of a linear instability to identify the single-direction in gene expression space along which the bifurcation unfolds and identify possible gene contributors. This scheme identifies transcription factors long viewed to play a crucial role in the process of neutrophil differentiation in addition to identifying a host of other novel genetic players. Most broadly speaking, we provide evidence that, though very high-dimensional, a bifurcating dynamical systems formalism might be appropriate for the process of cellular differentiation and that it can be leveraged to provide insights. Ambitiously, our work attempts to take a step beyond data-analysis and towards the construction of falsifiable mathematical models that describe the dynamics of the entire transcriptome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".