Metastasize vs Mathematize

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An aluminum panel mural combining painted form and projected mathematics to examine cancer through both biological and analytical lenses.

About the Project

Metastasize vs Mathematize is a mini mural composed of an aluminum frame and panel with a metal flake finish. The work depicts a tumor composed of mutant cells and flow cells, overlaid with an animated projection of a mathematical formula developed to detect mutations at an early stage.

The title reflects a conceptual tension between uncontrolled biological growth and mathematical intervention. The projected formula represents research that seeks to identify cancer through cell free DNA signals before disease progression becomes advanced. Embedded within the artwork are actual flow cells used in laboratory research. These elements stand in for human presence, underscoring the idea that cancer is a condition with universal relevance.

By combining painted imagery with live projection, the piece creates a layered visual environment where cancer biology and mathematical analysis coexist, offering a contemplative view of how scientific approaches can confront complex disease processes.

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Artpiece dimensions
48″ x 72″ x 60″

Artpiece price
$4,200
Projector not included

Together, they built a shared language between disciplines, translating data, material, and emotion into new forms of expression.

THE TEAM
ARx connects artists and researchers through residencies, exhibitions, and education.
Phoenix Bioscience Core
Get to know PBC Art Committee

WHERE Creativity Image of an Art piece Meets Research • 

Jeremie “Bacpac” Franko
bacpac is an aerosol mural artist originally from New York City. My art is usually focused on engineering and machines, not nature. I painted on movie sets, rock videos and custom cars before coming to Arizona.
Kamel Lahouel
Dr. Kamel Lahouel develops mathematical and statistical models for biology, focusing on machine learning with cell-free DNA signals for cancer detection. His work includes stochastic processes, non-parametric statistics, and pattern recognition in latent dynamical systems. He joined TGen in 2022 after a Ph.D. and postdoc at Johns Hopkins University.