GRAPHIC APPAREL SHOPScientists are closing in on powerful new ways to instruct living cells to grow into complex forms as easily as coding a computer program. Thanks to groundbreaking work at Harvard, where researchers have developed a new computational approach to guide how cells organize themselves, wea re moving beyond traditional trial-and-error methods and into data driven optimized production pathways.
A Computational Leap: Turning Cell Growth Into Optimization
At the core of this advance is a computational framework from Harvard’s School of Engineering and Applied Sciences. Graduate student Ramya Deshpande and postdoctoral researcher Francesco Mottes, under Professor Michael Brenner, have reconceptualized the way cells form structures, treating the underlying biological rules as an optimization problem.
By leveraging automatic differentiation, a technique widely used to train neural networks in artificial intelligence, the team can predict with remarkable accuracy how small genetic or chemical tweaks will influence the collective behavior of cells.
This novel method enables scientists to move from laborious experiments to computational design, making it possible to uncover the precise genetic or chemical instructions needed for cells to assemble into tissues with specific shapes and functions.
Simulating the Evolution of Living Tissues
The Harvard team built a model that simulates networks of genes and the interactions between cells, capturing both chemical signaling and physical forces. Through repeated simulations, the system “learns” which rules optimize the desired outcome, whether that’s elongating a tissue or concentrating cell division at a particular point.
- Source cells emit growth factors that guide neighboring cells’ behavior.
- Proliferating cells respond by dividing or stopping, depending on local chemical cues.
- The model can visualize how to fine-tune gene networks to sculpt cell clusters into target shapes.
Though still a proof of concept, this approach lays the foundation for combining predictive computational models with biological experiments. In the near future, scientists could harness these tools to engineer tissues, or even entire organs, with custom properties.
Transforming Bioengineering and Medicine
By framing morphogenesis as a math problem, researchers are unlocking new possibilities in synthetic biology and regenerative medicine. As Francesco Mottes notes, the ultimate ambition is to allow scientists to specify a tissue’s architecture and then calculate exactly how to program cells to build it.
Harvard’s team has already applied similar computational techniques to diverse fields, from designing self-assembling materials to protein engineering. Now, they’re scaling these concepts to the complexity of living systems, edging closer to the “holy grail” of bioengineering: reliably guiding the growth of living tissues and organs.
Looking Forward
The technology is still evolving, but its impact could be transformative. With further refinement and integration of experimental data, this computational framework could reshape approaches in tissue engineering, developmental biology, and cancer research. Supported by the Office of Naval Research and the NSF AI Institute of Dynamic Systems, the work marks a milestone toward a future where engineering life is as systematic as building machines.
Key Takeaway
By redefining cellular self-organization as a tractable optimization challenge, Harvard researchers are opening the door to programmable biology. This paradigm shift could revolutionize medicine, biotechnology, and deepen our understanding of life itself.
Source: Harvard John A. Paulson School of Engineering and Applied Sciences
Harvard Researchers Are Programming Cells to Shape Themselves