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by claude@2026-07, 2026-07-03
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The study developed a sensitivity analysis-guided New Approach Methodology (NAM) to optimize and infer parameters in biochemical kinetic models for phosphoinositide (PIP, PIP2, IP3) synthesis, aiming to translate molecular time-series measurements into cell-specific mechanistic predictions. Using superior cervical ganglion neuron data, the authors constructed and optimized the model and validated it against independent dose-dependent responses without refitting, then used local and global sensitivity analyses to identify dominant parameter drivers. They inferred cell-specific models via sensitivity fingerprinting and a neural network trained on synthetic phosphoinositide time series, and both approaches reproduced experimental lipid dynamics across tsA201, human neuroblastoma, and hippocampal neuron cells while preserving the baseline structure; kinase-perturbation predictions distinguished hippocampal neurons with large basal PIP pools from small-pool cells, and simulations of PI4KA and PIP5K1C loss-of-function under repeated stimulation showed progressive signaling collapse only in small-pool cells. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Precision medicine requires models that can translate rich molecular measurements into individualized predictions of biological response. This challenge is particularly acute for phosphoinositide signaling disorders that often exhibit cell-type-specific responses to identical genetic or pharmacological perturbations. Here, we develop a New Approach Methodology (NAM) demonstrating that basal phosphoinositide pool composition, determined by the size of the PI(4)P reserve, determines the robustness of lipid signaling. The NAM comprises a core kinetic model of phosphatidylinositol (PI), phosphatidylinositol 4-phosphate (PI(4)P), phosphatidylinositol 4,5-bisphosphate (PI(4,5)P 2 ), and inositol 1,4,5-trisphosphate (IP 3 ) dynamics. The model also incorporates phospholipase C (PLC)-mediated hydrolysis and phosphatase-mediated turnover and explicitly accounts for IP 3 biosensor binding during parameter optimization. Parameters were optimized using experimental measurements from superior cervical ganglion (SCG) neurons and validated against independent dose-dependent PI(4,5)P 2 depletion data. Local and global sensitivity analyses were performed to identify the dominant parameter drivers of pathway behavior. These sensitivity relationships were then used to generate a population of model variants that captured phosphoinositide dynamics observed in tsA201 cells, human neuroblastoma cells, and hippocampal neurons. To infer cell-specific models, we developed two complementary inverse methods: sensitivity fingerprinting derived from mechanistic model sensitivities and a neural network trained on synthetic phosphoinositide time series. Both approaches reproduced experimental PI(4)P, PI(4,5)P 2 , and IP3 dynamics across cell types while preserving the baseline model structure. Importantly, the inferred models predicted experimentally observed differential vulnerability to kinase perturbation without additional fitting. Hippocampal neurons with large basal pools of PI(4)P maintained PI(4,5)P 2 and IP 3 signaling under phosphatidylinositol 4-kinase alpha (PI4KA) inhibition, whereas cells with small basal PI(4)P pools exhibited signaling failure. Simulations of PI4KA and phosphatidylinositol-4-phosphate 5-kinase type 1 gamma (PIP5K1C) loss-of-function mutations under repeated stimulation further revealed progressive signaling collapse in small-pool neurons but sustained function in large-pool neurons, demonstrating that basal lipid composition can determine genetic vulnerability. Together, this NAM provides a predictive, cell-specific framework for translating dynamic lipid measurements into mechanistic models that support precision medicine applications in phosphoinositide-related disorders.
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Abstract
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.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Competing Interest Statement: The authors declare no competing interests.
We have expanded and revised the text and figures in the revised submission.
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