Predicting Tissue Dynamics based on Stochastic Variations in Cell Stiffness and Spatial Clustering within the Tissue Environment
Abstract
Variations in the properties identifying individuals within a given population is the norm in biology. For example, every human being from a given population is different. This concept holds true at various hierarchical levels in biology all the way down to the individual cells that make up the tissues and organs of the body. Each cell is slightly different in its biochemical and biomechanical properties from the other cells that make up the tissue. This is an effect of the stochastic nature of biological processes that impart cells their properties. An important question that arises then is - if the extent of variations observed within a given population is known, can that information be used to predict the long-term dynamics of that particular population? In an attempt to answer this question, the proposed work will examine the role of variations in the biomechanical properties of cells within a tissue on the dynamical behavior of the tissue. It is hypothesized that these variations greatly influence the tissue dynamics during key biological processes such as embryogenesis, tissue regeneration, wound healing, aging and the growth of cancerous tumors. This project will focus on the role of variations in the biomechanical properties such as stiffness of cells comprising a healthy normal tissue on the likelihood of cancer occurrence and malignant tumor growth within these tissues over time. A mathematical model describing the interactions between cells of a tissue as a function of their biomechanical properties, as well as the effect of these interactions on cell fate will be developed. This mathematical model will then be used to study how the extent of variation in the mechanical stiffness of different cells influences the overall cell population dynamics and estimate the likelihood of a sub-population of cells growing rapidly and forming malignant tumors within this tissue environment. The model will also consider the effect of spatial distributions and clustering of cells with similar biomechanical properties on the overall tissue dynamics and the likelihood of malignant tumor growth. From a disease perspective, this work will provide an early stage marker for the occurrence of cancer within healthy tissues. From a tissue dynamics perspective, this work provides the framework to further understand the processes of tissue regeneration and wound healing. This is important from the Army and DOD perspective as it impacts soldier recovery and performance post injury. Apart from this, the proposed work will shed light on a fundamental aspect of biology - the variability found between individual elements, and its influence on the dynamics of biological systems across different scales.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 11, 2018
- Source ID
- W911NF1710413
Entities
People
- Parag Katira
Organizations
- Army Contracting Command
- Salk Institute for Biological Studies
- United States Army