One controller can manage a group of similar cells instead of each individually.

Scientists want to control gene expression inside living cells. Doing this for large cell populations uses too much computing power because cells naturally differ from one another. To fix this, researchers created a shortcut based on how similar cells act. Instead of spending time learning exact biological models for every cell, their system watches input-output data to group cells with similar dynamics. Next, they built a leader-follower control setup. They design just one controller for an entire group and apply it to every cell in that cluster. This strategy sharply cuts down the calculations needed. It also keeps the closed-loop control stable. In gene expression simulations, this framework delivered scalable, reliable control across diverse cells.

Why it matters

Industries like drug development and biomanufacturing need to control large cell populations, but current methods are computationally expensive and slow. This approach reduces the number of controllers needed, making it feasible to scale up production or experiments.

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