Biological systems comprise diverse collections of cellular and non-cellular components with intricate relationships and dynamic interactions. To gain system-level understanding, we must be able to accurately model these systems, both experimentally and computationally. Agent-based models (ABMs) in particular are a uniquely intuitive, modular, and flexible framework capable of supporting multi-scale, multi-class models that capture the complexity of these biological systems. In this work, I develop and apply a unique multi-scale, multi-class ABM for gaining intuition about complex biological systems. First, I review the use of multi-scale and multi-class computational models for studying biological systems, highlighting current use cases of the ABM framework in systems medicine at both the cell-scale and the people-scale. Then, I introduce my custom ABM framework designed with a focus on extensibility and modularity. I apply a tissue cell implementation of the framework in case studies on intracellular module complexity, growth context, cell competition, and intracellular and intercellular heterogeneity. Next, I extend the framework with additional environmental components to explore the relationship between microenvironment structure, function, and emergent behavior. Finally, I conclude by discussing applications of the ABM, the utility of interactive data visualization, and the importance of an interdisciplinary approach in advancing biology.

Creator

• Yu, Jessica

DOI

• https://doi.org/10.21985/n2-th9r-6335

Subject

• Biology
• Chemical engineering
• Computer science

Language

• en

Alternate Identifier

• etdadmin_upload_743010
• http://dissertations.umi.com/northwestern:15106

Keyword

• emergent behavior
• systems biology
• agent-based modeling

Date created

• 2020-01-01

Resource type

• Dissertation

Rights statement

• In Copyright