A Korea Advanced Institute of Science and Technology (KAIST) research team has uncovered the root cause of individual cells' different degrees of response to the same external stimuli, such as antibiotics, using mathematics.
Cells in a person’s body have a signal transduction system that responds to various external stimuli such as antibiotics and changes in osmotic pressure. These signaling systems play a key role in how cells interact with their external environment.
However, when there is the same external stimulus to cells, the degree of response is different, resulting in heterogeneous reactions to drugs and persisting cells with strong drug resistance. Notably, the cell-to-cell heterogeneity responses to external stimuli are a cause of preventing the complete death of cancer cells when applying chemotherapy during cancer treatment.
Therefore, there have been many attempts to find the cause of the intercellular heterogeneity that causes this phenomenon. Still, it remained a challenge as it was impossible to observe all intermediate processes experimentally with current technology directly.
To solve this problem, the team, led by Professor Kim Jae-kyung of the Mathematical Sciences Department, developed a queuing model that describes the intracellular signal transduction system.
Based on the developed queuing model, the team developed a Moment-based Bayesian Inference method that can analyze the signal system without observing the intermediate process of the signal system by combining the Bayesian model and the Mixed-effects model, which are statistical estimation methodologies.
As a result of this analysis, the research team revealed that the heterogeneity between cells in response to external stimuli is proportional to the number of rate-limiting steps constituting the signal transduction system.
“The study confirmed that as the number of rate-limiting steps in the signal transduction system increases, the signal transmitted can be more diverse even in the same genetically same cell population,” Professor Kim said. “We have not only laid the theoretical foundation for mathematical model analysis but also verified the theoretical results using actual E. coli antibiotic response experimental data.”
These findings will help suggest a new paradigm for the study of antibiotic-resistant bacteria, Kim added.
Science Advances published the study results in its March 18 online edition.