Data-driven modeling of the mouse macrophage Toll-like receptor signaling pathway

Authors

• NP Manes
• SA Hassan
• AA Armstrong
• F Zhang
• RA Gottschalk
• MJ Marino
• ID Fraser
• RN Germain
• M Meier-Schellersheim
• A Nita-Lazar

Abstract

The Toll-like receptor (TLR) signaling pathway is important for the initiation of innate immune responses. Variations in ligand structure, concentration (e.g., of lipopolysaccharide (LPS)) and receptor stimulation history are known to affect TLR signaling and downstream pathway dynamics. To avoid oversimplified modeling and model overfitting, we used targeted mass spectrometry (LC-MS) to measure TLR4 pathway protein abundances and constrained interaction parameters through molecular structural simulations, thereby creating a strongly data-driven TLR4 pathway model. The protein abundances ranged from 1,332 (Elk1) to 227,000,000 (actin) copies per cell. They moderately correlated with RNA-seq transcript abundance values (r = 0.699), and this regression was used to estimate proteome-wide abundance values. 151 protein complexes were modeled using AlphaFold-Multimer, and their association rates were predicted using TransComp. Simmune is being used to perform rule-based model simulations, explore pathway parameters, and examine the connections among the parameters and molecular complexes. This work was supported (in part) by the Division of Intramural Research, NIAID, NIH.

Scientific Focus Area: Systems Biology

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