Cellular Dynamics and Morphogenesis Modeling

Understanding how cells organise into functional tissues has been transformed by computational modelling across scales. Models range from discrete agent-based frameworks to continuum descriptions, enabling the study of individual cell behaviours, cell–cell interactions and emergent tissue morphogenesis. Agent-based methods such as the Cellular Potts model and vertex models capture mechanical and adhesive interactions, while phase-field and continuum elasticity models describe interface dynamics and tissue deformation. Hybrid multiscale approaches integrate intracellular signalling networks with mechanical feedback, providing mechanistic insight into processes from pattern formation to organogenesis. Recent advances have incorporated stochastic thermodynamics to assign explicit timescales to events and deep-learning frameworks to enhance image segmentation and model validation. These methodologies reveal the interplay between genetic programmes, mechanical forces and environmental cues that drive shape change in development and disease. By bridging quantitative simulation with experimental data, the field offers predictive platforms for tissue engineering, regenerative medicine and understanding tumour invasion.

Research from Nature Portfolio

A recent study introduced a biophysical simulation framework to generate realistic three-dimensional cellular images for deep-learning segmentation. The approach integrates membrane mechanics and nuclear morphology into a generative adversarial network, producing synthetic datasets with matching ground-truth labels. Quantitative evaluation demonstrates improved segmentation accuracy over manual annotation, emphasising the utility of combining biophysical principles with AI to accelerate and refine single-cell analysis in complex tissue models.

Cellular Dynamics and Morphogenesis Modeling publication trend

The graph below shows the total number of articles in cellular dynamics and morphogenesis modeling across all publications each year (not limited to Nature Index journals).

Technical terms

Morphogenesis: The process whereby cells and tissues acquire organised shape and structure during development.

Cellular Potts model: A lattice-based computational framework representing cells as pixel domains that evolve to minimise an energy function reflecting biophysical constraints.

Vertex model: A polygonal representation of epithelial tissues where cell interfaces are resolved by shared geometric vertices to simulate mechanical interactions.

Phase-field model: A continuum approach using field variables to implicitly track interfaces and capture dynamic boundary evolution without explicit contour mapping.

Convergent extension: A collective cell movement mechanism in which tissues elongate along one axis through cell intercalation and directional migration.

Generative adversarial network (GAN): A machine-learning framework featuring dual neural networks that generate and assess synthetic data to improve realism.

Poissonian kinetics: A stochastic modelling technique that employs Poisson processes to assign explicit physical timescales to discrete simulation events.

References

1. Poissonian Cellular Potts Models Reveal Nonequilibrium Kinetics of Cell Sorting. Physical Review Letters (2024).
2. CellularPotts.jl: simulating multiscale cellular models in Julia. Bioinformatics (2023).
3. Improving 3D deep learning segmentation with biophysically motivated cell synthesis. Communications Biology (2025).
4. The shapes of elongating gastruloids are consistent with convergent extension driven by a combination of active cell crawling and differential adhesion. PLOS Computational Biology (2024).