Cellular Development Follows the Path of Minimum Action

2025-04-10

AI Summary

The physical principles governing cellular developmental trajectories remain poorly understood. This work introduces, for the first time, the principle of least action into developmental biology, proposing a unified theoretical framework that integrates thermodynamics and information theory. Leveraging single-cell RNA-seq data, we develop a Transformer-based computational model to quantitatively characterize three key metrics—entropy production, trajectory curvature, and irreversibility—which respectively reflect cellular differentiation plasticity, mechanical elasticity, and trans-/dedifferentiation dynamics. Our methodology combines the maximum entropy principle, stochastic dynamical systems modeling, and trajectory-level thermodynamic analysis. Validation across multiple single-cell and embryonic development datasets demonstrates that the framework accurately quantifies developmental asymmetry and uncovers latent thermodynamic and informational constraints underlying cell fate decisions. It provides the first physically interpretable, unified dynamical description of developmental processes.

Technology Category

• Machine Learning: Information Theory
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: User model development and evaluation
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Cellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.

Problem

• Model cellular development using least action principles
• Quantify entropy and information flow in RNA data
• Reveal thermodynamic constraints on cell fate decisions

Innovation

• Transformers model least action in development
• Quantify entropy and curvature in RNA data
• Unified metrics for cellular fate decisions