Presentation Information
[SS22-02]Mathematical Modeling of Protein Dynamics in Cell signaling with Negative Interactions
*Minsoo Kim1, Eunjung Kim2 (1. National Korea Maritime and Ocean University (Korea), 2. Korea Institute of Science and Technology (Korea))
Keywords:
Stochastic Process,Extended Boolean Network,Synergistic Combination Effect,Negative Interaction
This study presents an improved mathematical model that incorporates negative interaction mechanisms to predict the dynamics of cell signaling pathways. By employing stochastic differential equations (SDEs) and the Euler-Maruyama method, we simulate the responses of proteins within the Mitogen-Activated Protein Kinase (MAPK) and oxytocin signaling pathways over time. Our findings indicate that the inhibition of upstream proteins such as MEK1/2 leads to a rapid decrease in ERK1/2 activation while causing a compensatory increase in other proteins like SOS, RAS, and RAF. Furthermore, we explore the synergistic effects of combination therapies, demonstrating that targeting multiple signaling pathways can enhance therapeutic efficacy. Through the application of the Bliss Independence Index, we assess the effectiveness of these therapeutic combinations. Additionally, we identified the effects of abnormal activation increases caused by mutations on downstream proteins and the resulting changes in balance due to negative interaction. Overall, our enhanced mathematical model serves as a valuable tool for simulating cell signaling dynamics and developing innovative therapeutics. Future research should validate these findings with experimental data and explore additional therapeutic combinations to optimize clinical outcomes.