Dynamical density-functional-theory-based modeling of tissue dynamics: Application to tumor growth

Area of Science:

• Biophysics
• Theoretical Biology
• Statistical Mechanics

Background:

• Understanding tissue and tumor dynamics requires bridging scales from individual cells to macroscopic structures.
• Existing models often lack a unified framework to incorporate cell-level details with emergent tissue properties.

Purpose of the Study:

• To present an extended dynamical density-functional theory (DDFT) framework for modeling cell populations in tissues and tumors.
• To provide a biophysically consistent approach for describing tissue structure and dynamics from the cellular level upwards.
• To analyze cell-pair interactions and their impact on tissue and tumor behavior, including inter-species competition.

Main Methods:

• Extension of dynamical density-functional theory (DDFT) to incorporate cellular processes.
• Modeling of cell-pair interactions, diverse cell types, phenotypes, and birth/death rates.
• Analysis of single-species and two-species systems, focusing on tumor-healthy cell competition.

Main Results:

• The extended DDFT framework successfully describes tissue structure and dynamics at the cellular level.
• Model predictions align with biological observations in various parameter regimes.
• Tumor growth divergence (metastatic vs. benign) is shown to be critically dependent on cell-pair interaction parameters.

Conclusions:

• The developed theoretical framework offers a powerful tool for studying complex biological systems like tissues and tumors.
• Cell-pair interactions are key determinants of emergent tumor growth patterns and characteristics.
• This approach provides insights into the biophysical mechanisms underlying tissue morphogenesis and disease progression.