Randomness inside cells, often termed as biological noise, can significantly influence whether cancer returns post-chemotherapy or if infections persist despite antibiotic treatment. Even genetically identical cells can behave differently due to unpredictable molecular fluctuations, creating rare “outlier” cells that evade medical interventions.
A pioneering study led by Professor KIM Jae Kyoung of KAIST and the IBS Biomedical Mathematics Group, in collaboration with KIM Jinsu of POSTECH and Professor CHO Byung-Kwan of KAIST, introduces a mathematical framework designed to tackle this issue. The researchers have developed a “Noise Controller” (NC), a system that aims to stabilize not only the average behavior of a cell population but also the unpredictable variations between individual cells.
Understanding the Limitations of Averages
The research highlights a crucial point: controlling the average behavior of cells can still leave dangerous exceptions that drive drug resistance and relapse, even when most cells respond as expected. This insight challenges the traditional focus on averages in biological systems.
Cells maintain stability, or homeostasis, by keeping key internal conditions steady despite external changes. Synthetic biologists attempt to mimic this by constructing gene circuits that regulate protein levels. However, focusing solely on the mean can be misleading, as individual cells may still exhibit significant variability.
“Standard control methods are like adjusting a shower,” the researchers explained. “You might get the water to average 40°C, but if that average is achieved by alternating between freezing cold and boiling hot water, you can’t take a shower.”
In medical contexts, these fluctuations are critical. A small fraction of cells can enter a protected state, surviving treatment and potentially leading to relapse.
Introducing the Noise Controller
The team’s work builds on the concept of robust perfect adaptation (RPA), which ensures a system returns to a target level after disturbances. While RPA can stabilize average outputs, it often fails to address the noise that causes variability among cells.
Professor KIM Jae Kyoung explained, “Instead of accepting that limit, our researchers designed a second layer of control that targets noise directly. The NC does not just sense how much protein is present. It aims to sense variation itself, using a statistic tied to the second moment of protein levels.”
The proposed mechanism relies on two ideas: dimerization, where two protein copies bind together, and degradation-based actuation, which actively breaks down specific proteins to stabilize the system. This approach allows the system to “measure” and reduce its own variability.
The result is “Noise Robust Perfect Adaptation,” or Noise RPA, where both average protein levels and fluctuations remain stable despite changes in conditions.
Practical Implications and Future Directions
The study uses the Fano factor, linking variance to mean, as a measure of noise. Simulations show that the combined control strategy can reduce noise to a Fano factor of 1, which the team considers a practical limit. Attempts to set targets below this value resulted in system instability.
The robustness of this approach is significant. It held up across various parameter changes and different reaction networks, including systems with bimolecular steps and dimerization. This adaptability is crucial since biological components rarely behave precisely as designed.
Noise can also lead to bimodality, where a population splits into distinct groups. The researchers tested this with a gene expression model and found that while a mean-only controller failed to prevent the split, the NC shifted behavior toward a single stable group.
In a practical scenario involving DNA repair in E. coli, the Noise Controller reduced the simulated failure rate from 20% to 7%, demonstrating its potential to minimize treatment-resistant outliers.
“This research demonstrates that cellular noise, often dismissed as luck or unavoidable randomness, can be brought into the realm of precise mathematical control,” said Professor Kyoung.
Professor KIM Jinsu added, “This achievement shows the power of mathematical modeling, starting from theoretical equations to design a mechanism that solves a fundamental biological problem.”
Broader Impact on Synthetic Biology and Medicine
The core takeaway is that treatment failure may not always stem from genetic resistance but can also arise from random molecular fluctuations. By offering a theoretical method to reduce these swings, the study suggests strategies to enhance the reliability of therapies. If future laboratory experiments can translate these designs into real gene circuits, the fraction of “survivor” cells that restart tumors or infections might be significantly reduced.
The research also provides a roadmap for synthetic biology. Many engineered microbes perform well on average but exhibit inconsistent behavior across individual cells. A noise-focused controller could improve the consistency of microbial systems, enhancing applications in biomanufacturing and environmental sensing.
Overall, the framework clarifies the potential and limitations of control in biological systems, guiding smarter experiments and safer designs. The findings are published in the journal Nature Communications.
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