Parameter Optimization of Biochemical Models for Precision Medicine: A Case Study in PI(4,5)P2 Synthesis

Precision medicine requires models that can translate rich molecular measurements into individualized predictions of biological response. Phosphoinositide signaling disorders present an acute challenge, where nonlinear dynamics vary across cell types and are difficult to predict or interpret from measurements alone without mechanistic modeling. We developed a sensitivity analysis-guided New Approach Methodology (NAM) using a multi-step framework and applied it as a proof-of-concept to phosphoinositide signaling. We constructed and optimized a kinetic model of PIP, PIP2, and IP3 dynamics using superior cervical ganglion neuron data, then validated it against independent dose-dependent responses without refitting. We then performed local and global sensitivity analyses to identify dominant parameter drivers of pathway behaviors. Sensitivity-guided parameter variation generated a population of model variants that captured lipid dynamics observed in tsA201 cells, human neuroblastoma cells, and hippocampal neurons. To infer cell-specific models, we implemented two complementary inverse approaches: sensitivity fingerprinting guided by mechanistic sensitivities and a neural network trained on synthetic phosphoinositide time series. Both approaches reproduced experimental PIP, PIP2, and IP3 dynamics across cell types while preserving the baseline model structure. Importantly, the inferred models correctly predicted experimentally observed responses to kinase perturbation without additional fitting: hippocampal neurons with large basal PIP pools maintained signaling under PI4K inhibition, whereas cells with small pools exhibited failure, validating the framework. Simulations of PI4KA and PIP5K1C loss-of-function mutations under repeated stimulation revealed progressive signaling collapse in small-pool cells but sustained function in large-pool cells, demonstrating that basal lipid composition determines genetic vulnerability. The NAM provides a predictive, cell-specific framework for translating dynamic lipid measurements into mechanistic models that support precision medicine applications in phosphoinositide-related disorders.