Department of

Chemical Engineering

Designing molecular technology for the 21st century with biology and chemistry


 


Associate Professor Antonios Armaou | Research




Development of multiscale models of tumor progression


Tumor progression is the ultimate outcome of several time and space dependent interacting processes which entail the combined intracellular and extracellular events that govern cell survival, proliferation, and migration, as well as angiogenic, inflammatory, and immune responses. In this project we are developing a mathematical model that captures the complexity of tumor progression under biologically relevant conditions. Our agent-based model describes tumor growth and invasion as the outcome of proliferation and migration of individual cancer cells. The models consist of three interconnected components which describe the spatio-temporal distribution of nutrients and key signaling molecules (PDEs), the intracellular signaling pathway of each tumor cell (ODEs), and the state of each tumor cell (i.e., its phenotype, cellular mass, location)(Figure 1 below).

Components of the agent-based model of tumor progression.

Figure 1. Components of the agent-based model of tumor progression.



Enlarged segment of the image above - Components of the agent-based model of tumor progression.

Enlarged segment of the image above "Components of the agent-based model of tumor progression".



We use the model to explore hypothesis regarding the mechanisms underlying tumor progression. For instance, our simulation results suggest that the sensitivity of migrating tumor cells to chemoattractant gradients is an important determinant of the morphology and growth and invasion rate of the tumor (Figure 2 below).

Effect on chemotaxis on tumor progression.

Figure 2. Effect on chemotaxis on tumor progression.



Our overall goal is to develop a set of mathematical and computational tools for applications in cancer research. These applications include:

  • Integrating and organizing experimental data,
  • Exploratory experiments in silico to get insights and proposed new experiments,
  • Preliminary experiments speed up and guide target identification studies,
  • Design of optimal drug scheduling policies.

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