Predicting and Controlling Collective Fate in Multicellular Systems
Bibliographic record
Abstract
Abstract Collective behavior is a defining property of multicellular systems, where coordinated outcomes emerge from local cell–cell interactions. Yet the quantitative rules linking single-cell decision-making to tissue-scale organization remain poorly resolved. Here, we develop a quantitative framework that defines an order parameter predicting when initially disordered colonies undergo a transition to ordered fate alignment and when minimal, localized inputs can redirect their collective state. This analysis reveals a distinct control regime in which multicellular assemblies become susceptible to a single engineered “guide” cell. We validate these predictions by introducing guide cells that integrate into unperturbed colonies and redirect fate patterns within the theoretically defined control windows. Together, these results connect single-cell decision rules to emergent tissue-level organization and establish a generalizable biological control strategy in which a minority engineered subset can reliably redirect the developmental trajectory of a much larger multicellular population. Abstract Figure Summary figure: Emergence, Scaling, and Control of Multicellular Collective Order. ( A ) Cell-number–dependent collective order. Sparse colonies lack coordination and yield disordered fate distributions; dense colonies exhibit coordinated, ordered outcomes. ( B ) Collective order rises with colony compactness (∝ cell density) and collapses across colony radii R onto a single curve. ( C ) Once coordination emerges, a single “guide cell” can steer fate-analogous to a sheepdog guiding a flock. The herding efficacy exhibits a biphasic window versus cell number.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".