270360 Multi-Agent Systems for Modeling Vascularization and Tissue Growth within Porous Biodegradable Scaffolds
Biological and biomedical phenomena such as tissue growth are highly complex processes in which tissue constructs self-assemble as a result of the collective behaviors of individually acting cells. Such complex phenomena involve a combination of biodegradable natural or synthetic scaffold materials, numerous signaling molecules (growth factors, proteins, etc) and various cell types, integrated together in a well-orchestrated system that is designed to stimulate, promote, and at the same time control the formation of mature vascularized tissues.
To design functional replacement tissues, tissue engineers must be able to understand how a combination of chemical, mechanical, and physical factors and environments influences the coordinated growth and differentiation of multiple cell types. Ongoing scientific advancements have increased our ability to better control the properties of engineered tissues, however tissue engineers still have limited ability to predict how tissues grow over time.
While experimental studies provide in-depth insight into this process, it is well understood that experimentation alone would not be sufficient to address challenges ahead. Developing a computational model that can realistically simulate the behavior of such systems over time would be invaluable for many different theoretical and practical purposes. These purposes include, but are not limited to, better understanding the mechanisms involved, manipulating or measuring the important variables of the system and enabling us to qualitatively or quantitatively predict their effect on system level behaviors, and ideally in longer term, providing us with a tool for investigating ways of intervening in such systems.
Agent-based systems (ABS) are naturally suitable for modeling biological systems as they are comprised of individual discrete micro-scale constituents (e.g. cells) that interact with each other and their environment (e.g. extracellular microstructure) to form non-homogeneous macro-scale bodies (e.g. tissues and organs). The central idea in agent-based modeling (ABM) is to define software agents that can represent the building blocks of a system and to develop rules that regulate their interactions. The rules originate from the vast available knowledge gained through many years of studying the individual components of these biological systems.
In this paper, a multi-layer multi-agent ABM framework is developed to model the process of tissue growth within biodegradable non-vascularized porous scaffolds. As scaffolds have dimensions in the order of hundred micrometers, tissue cells require functional blood vessel networks to provide them with required nutrients and hence it is necessary to consider vascularization of the scaffold at the same time as tissue growth. As a result, the model includes separate layers to simulate the scaffold structure, the developing blood vessel network (that invades inside the scaffold), and the tissue cells that grow, migrate, and increase in number. Depending on the cell type, cell differentiation may also be considered. Different layers in the code dynamically interact together and result in a complex system.
The developed framework is implemented in Java, using Repast (Recursive Porous Agent Simulation Toolkit) which is an open-source agent-based modeling and simulation platform with features such as event scheduling procedures and visualization tools. Two different types of software agents are developed to represent tissue cells and blood vessels in the context of a spatially heterogeneous multi-cell tissue environment. These agents interact together and with their micro-environment, leading to tissue growth and formation of new blood vessel capillaries within scaffold structure. Tissue cell agents can perform various actions including sensing their environment, growth, migration and proliferation. Depending on cell type, cell differentiation or apoptosis may also be considered.
This computational framework couples two different cell types and cellular and tissue level cell scales. Using this simulation framework, we can quantify the dynamic impact of cellular level events and actions on a complex multi-cellular system. Also challenges in modeling systems with a large number of rapidly increasing interacting agents are addressed. This framework is then used to study tissue patterning processes which are an important step during tissue regeneration.
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