Random fluctuations in molecular activity, termed biological noise, can occur even among genetically identical cells. This phenomenon has been implicated in outcomes such as cancer recurrence after chemotherapy and bacterial survival following antibiotic treatment.
Biological systems inherently exhibit noise due to random processes in molecular production, degradation, and interaction. Addressing how these systems manage such fluctuations has been an ongoing challenge in systems and synthetic biology.
While modern biology can regulate the average behavior of cell populations, controlling the unpredictable fluctuations of individual cells, especially rare "outlier" cells, has remained difficult. These outliers, driven by stochastic variation, can behave divergently from the majority and influence system-level results. Traditional feedback control methods designed to stabilize average behavior can sometimes amplify variability, leading to noisier systems rather than more stable ones.
A joint research team comprising Professor KIM Jae Kyoung (KAIST, IBS Biomedical Mathematics Group), KIM Jinsu (POSTECH), and Professor CHO Byung-Kwan (KAIST) has developed a mathematical framework named the "Noise Controller" (NC). This development aims to provide single-cell precision control.
To address the issue of uncontrolled fluctuations where average stability does not guarantee individual cell stability, the team designed a new gene regulatory circuit using mathematical modeling. Unlike previous controllers that monitored only protein abundance, the NC incorporates a feedback loop that senses the "noise" itself, specifically the second moment of protein levels. The core mechanism involves dimerization (the binding of two proteins) coupled with degradation-based actuation (active breakdown of specific proteins). This configuration enables cells to "measure" and reduce their internal noise.
The outcome is a state referred to as "Noise Robust Perfect Adaptation" (Noise RPA). This technology facilitates a regime where both the average protein level and the magnitude of stochastic fluctuations remain stable, even under varying conditions. The model indicates that noise can be reduced to a Fano factor of 1, considered a fundamental physical limit imposed by stochastic molecular processes.
The team validated the technology through in silico experiments using the DNA repair system of E. coli. In standard simulations, approximately 20% of bacteria failed to activate their DNA repair mechanisms due to internal noise, resulting in cell death. Upon applying the Noise Controller, the system synchronized the cells, and the failure rate decreased from 20% to 7%, increasing the survival rate. This suggests that mathematical control can theoretically influence cell behavior, potentially reducing the occurrence of outliers that contribute to treatment failures.
This research represents a conceptual shift from population-level regulation to single-cell precision control in stochastic biological systems. The study clarifies what is mathematically achievable regarding noise control, establishing a basis for future experimental and computational efforts in synthetic biology. The technology is expected to contribute to the development of engineered microbes and strategies for overcoming drug resistance in cancer therapy.