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Parameter Optimization of Biochemical Models for Precision Medicine: A Case Study in PI(4,5)P2 Synthesis
Gonzalo Hernandez-Hernandez1,2, Mindy Tieu2, Pei-Chi Yang1,2
1Center for Precision Medicine and Data Science, University of California, Davis, California.
Biorxiv : the Preprint Server for Biology
|July 17, 2025
Summary
We developed a new computational framework to model lipid signaling networks, specifically focusing on phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2). This approach aids in understanding disease mechanisms and advancing precision medicine.
Area of Science:
- Biochemistry
- Systems Biology
- Computational Biology
Background:
- Lipidomics is crucial for precision medicine, with phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) regulating membrane signaling and metabolism.
- PI(4,5)P2's role in diseases like cancer and neurodegeneration makes it a key biomarker and therapeutic target.
- Traditional modeling struggles with the complexity of lipid networks.
Purpose of the Study:
- To present a modular and extendable framework for constructing mechanistic models of lipid kinetics.
- To model the synthesis and degradation of PI(4,5)P2 as a case study.
- To enable efficient identification of rate constants for enzymatic dynamics.
Main Methods:
- Developed a modular, extendable framework for mechanistic lipid kinetic modeling.
- Applied the framework to model PI(4,5)P2 synthesis and degradation.
- Optimized five kinetic parameters using experimental time-course data for PI(4)P, PI(4,5)P2, and IP3.
Main Results:
- The model demonstrated a strong correlation with experimental trends and reproduced key cellular signaling dynamics.
- Successfully identified rate constants governing enzymatic dynamics.
- Simulated signaling perturbations related to PI4KA and PIP5K1C loss-of-function.
Conclusions:
- The developed framework provides a scalable foundation for predictive biochemical modeling.
- This approach facilitates a deeper understanding of lipid signaling in health and disease.
- Offers potential for individualized applications in precision medicine.
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