Why does cancer sometimes recur even after successful treatment, or why do some bacteria survive despite the use of powerful antibiotics? One of the key culprits identified is “biological noise”—random fluctuations occurring inside cells.
Even when cells share the same genes, the amount of protein varies in each, creating “outliers” that evade drug treatments and survive. Until now, scientists could only control the average values of cell populations; controlling the irregular variability of individual cells remained a long-standing challenge.
A joint research team—led by Professor Jae Kyoung Kim (Department of Mathematical Sciences, KAIST), Professor Jinsu Kim (Department of Mathematics, POSTECH), and Professor Byung-Kwan Cho (Graduate School of Engineering Biology, KAIST)—has theoretically established a “noise control principle.” Through mathematical modeling, they have found a way to eliminate biological noise and precisely govern cellular destiny.
This achievement in securing precision control technology at the single-cell level is expected to be a new milestone in solving challenges in cancer treatment and synthetic biology.
The paper is published in the journal Nature Communications.
While cells in our bodies strive to maintain homeostasis for survival, their internal environments are constantly changing. Existing genetic circuit technologies could regulate the average protein levels of a cell population but often ended up amplifying the “noise”—the variance between individual cells.
The research team compared this to a “shower that fluctuates between boiling and freezing.” Even if the average water temperature is set to 40°C, a normal shower is impossible if the water alternates between scalding and icy. Similarly, a small number of cells that escape control due to this “trap of the average” become the primary cause of cancer recurrence or antibiotic resistance. To solve this, the team devised a new mathematical model called the noise controller (NC).
The researchers first investigated whether they could control the variance of outputs—which differs from cell to cell—using a “dimerization reaction,” where the final products of a system bind together to form pairs. In the process, they confirmed that the dimerization reaction could act as a sensor to detect fluctuations (noise) in the cellular state. However, initial attempts showed that this method alone had limits in reducing differences between cells.
Consequently, they determined that a device was needed to immediately reduce substances if they were overproduced. They combined this with a “degradation-based actuation” principle, which promptly breaks down proteins when they become excessive. As a result, they theoretically implemented “noise robust perfect adaptation (Noise RPA),” which maintains a constant noise level despite external environmental changes. Through this, they succeeded in suppressing cell-to-cell deviation to a Fano factor of 1—the minimum level achievable by universal biological systems.
The research team proved the model’s performance by virtually applying it to the DNA repair system of E. coli. In the existing system, the amount of DNA-repairing proteins varied so greatly between cells that approximately 20% of the cells failed to repair and died. However, by applying the NC to unify protein levels across all cells, the team decreased the mortality rate to 7%.
The team significantly boosted cell survival rates through sophisticated mathematical principles alone. This is highly significant as it moves beyond the average control paradigm to realize single-cell control, dealing with each cell with precision.
Professor Jae Kyoung Kim, who led the research, stated, “The significance lies in bringing cellular noise—which was previously dismissed as luck or coincidence in biological phenomena—into the realm of controllable factors through mathematical design. It will play a vital role in fields requiring precise cellular control, such as overcoming cancer treatment resistance and developing high-efficiency smart microorganisms.”
Co-corresponding author Professor Jinsu Kim of POSTECH emphasized, “This research demonstrates the power of mathematical modeling, starting from theoretical formulas of intracellular noise using reaction network theory and leading to the design of actual biological mechanisms.”
More information:
Dongju Lim et al, Toward single-cell control: noise-robust perfect adaptation in biomolecular systems, Nature Communications (2025). DOI: 10.1038/s41467-025-67736-y
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