Decoding the Mechanome: How AI is helping us understand the language of force in our cells
A Future Where Mechanics Guide Medicine
Imagine diagnosing disease not just from genes or proteins, but from a cell’s mechanical fingerprint—its stiffness, tension, and microscopic architecture. That’s the future I’m working toward. By decoding the relationship between mechanical forces and cellular behavior (what we call the mechanome), we can uncover how physical forces shape health and disease, and design treatments that work not only at the molecular level but at the mechanical level as well.
Every cell in the body—whether in bone, breast, or brain—is constantly pushing and pulling against its surroundings. Cells must sense cues like stiffness, stretch, and pressure and translate those signals into biochemical decisions. Yet the “mechanical worlds” cells inhabit differ by orders of magnitude; bone is thousands of times stiffer than brain. How cells interpret such diverse physical environments is still one of the fundamental open questions in biology.
In the Mouneimne Lab, I study this question. And thanks to recent advances in artificial intelligence, we now have powerful new ways to read what we’re calling the Mechanome: the biological language cells use to sense and respond to physical force. Unlocking this language could reshape how we diagnose, treat, and even prevent disease.
Feeling the Force
Cells are not just soft blobs—they are tense, dynamic structures. They constantly probe the extracellular matrix, pull on it, and adjust their own internal skeleton in response. These forces guide essential decisions: how a cell grows, moves, differentiates, or repairs tissue.
In cancer, this system goes off script. Tumors often live in rigid, scarred environments that push cells toward invasive, drug-resistant states. My research aims to figure out why this happens—what changes inside a cell when it transitions from a soft matrix to a stiff one, and how those mechanical shifts drive disease progression.
Where Biology Meets Machine Learning
A single mechanobiology experiment can generate thousands of measurements across imaging, RNA sequencing, and biophysical profiling. Humans can’t manually sort through patterns at that scale, but AI can.
Working jointly in the Mouneimne and McWhite Labs, I use machine learning to identify hidden relationships linking mechanical forces to cellular behavior. By integrating multiple forms of data together, I can build the atlas of the mechanome—a map of how cells across tissues sense, process, and adapt to their mechanical environments.
This atlas could explain why a breast cancer cell becomes more aggressive in a stiff tumor, why immune cells fail inside fibrotic tumors, or why our tissues become too rigid in the first place. Most importantly, it may reveal how to reverse those changes—providing mechanical, molecular, and computational entry points for future therapies.