Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state...

Area of Science:

• Biophysics
• Computational Biology
• Tissue Engineering

Background:

• Cells interact with the extracellular matrix (ECM) to drive emergent behaviors.
• Cell-ECM interactions are crucial for processes like wound healing and tissue development.
• Predicting collective cell-ECM interactions computationally is challenging due to complexity and nonlinearity.

Purpose of the Study:

• To develop a computationally efficient method for predicting nonlinear behaviors of multiple cells interacting mechanically within a 3D ECM.
• To enable accurate simulation of collective cell-ECM dynamics as cell numbers increase.

Main Methods:

• Introduced Dual-Faceted Linearization to accurately linearize highly nonlinear cell-ECM interactions.
• Recasted nonlinear dynamics in an augmented space using auxiliary variables for linearization.
• Employed superposition of single-cell computational models to predict multicellular behaviors.
• Reduced the order of the augmented linear system using principal component analysis.

Main Results:

• The developed method is computationally efficient compared to traditional nonlinear dynamic simulations.
• The method demonstrates high accuracy, outperforming standard Taylor expansion linearization.
• Successfully reproduced experimental results of multi-cell induced ECM compaction.

Conclusions:

• The Dual-Faceted Linearization and superposition approach provides an efficient and accurate computational tool for studying collective cell-ECM interactions.
• This method advances the ability to model complex biological processes involving multicellular coordination and matrix remodeling.