bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.02.12.705506

A mechanistic model of protein kinase A dynamics under pro- and anti-nociceptive inputs

Abstract

Protein kinase A (PKA) is a central integrator of nociceptive signaling, yet a quantitative account of how pro- and anti-nociceptive inputs shape its dynamics remains incomplete. Here, we develop a mechanistic model of PKA activity in nociceptive neurons that explicitly links receptor activation to downstream kinase regulation. Using time-course and dose-response measurements, we infer unknown process parameters and quantify parameter and prediction uncertainties to ensure robust conclusions. The model captures the activation of PKA by serotonin and forskolin and its suppression by opioids. We show how the model can be used for the assessment of alternative circuit topologies, and demonstrate that receptor context and stimulation history reconfigure PKA responsiveness, providing testable predictions for opioid modulation under clinically relevant dosing. This framework offers a principled basis for integrating PKA with broader pain-signaling networks, supports rational exploration of combination therapies, and establishes a general strategy for disentangling neuromodulatory control of kinase activity. Author summaryPain perception is modulated by a complex network of signaling pathways activated by different receptors with opposing effects. A key player in this process is protein kinase A (PKA), whose regulation by both serotonin and opioid receptors is not yet fully understood. In this study, we developed a mathematical model to investigate how these opposing signals affect PKA activity in sensory neurons. After estimating the unknown model parameters from a comprehensive dataset, we were able to quantitatively analyze the dynamic behavior of the system and use it for comparison of alternative circuit topologies. Our model provides a valuable tool for integrating diverse molecular interactions involved in pain processing and could help guide future efforts to develop better treatments for chronic pain and reduce opioid tolerance.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lakrisenko, P., Isensee, J., Hucho, T., Weindl, D., Hasenauer, J.. 2026-02-14. A mechanistic model of protein kinase A dynamics under pro- and anti-nociceptive inputs. https://doi.org/10.64898/2026.02.12.705506

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Concentration limits and localization of hydrogen peroxide in the extracellular space of solid tissues

H2O2 released to the extracellular space (ECS) regulates diverse physiological processes, yet its concentrations and spatial distribution in tissues remain poorly defined. This uncertainty hampers mechanistic understanding of redox signaling. Here, we used reaction-diffusion modeling to estimate extracellular H2O2 concentrations and transport ranges in various scenarios. Idealized analytical models were combined with numerical models incorporating localized NADPH oxidase (NOX) clusters, ECS microstructure, membrane permeability, and the thioredoxin- and GSH-dependent clearance systems. Using maximal neutrophil and NOX superoxide/H2O2 release rates, we obtained upper bounds for extracellular H2O2. Adjacent to isolated average-sized, fully active NOX2 clusters H2O2 peaked at ~540 nM at adhesion cell-cell separations, and decreased radially over ~50-100 nm. At the receptor cell surface, peak concentration decreased inversely with intercellular separation, to <5 nM at 1 m separation. Radial decrease here, for this wide separation, was over ~2.5 m. Even the former maximal extracellular concentrations induce just a minimal, highly localized oxidation of the intracellular Prdx, Trx and GSH pools. In turn, maximally activated neutrophils carry ~2000 such NOX2 clusters, inducing 10s of M peak ECS H2O2 concentrations. These cause extensive Prdx and Trx oxidation near the exposed membranes. However, the GSH-dependent system still sustains a strong transmembrane gradient if the permeation barrier remains intact, and ECS H2O2 concentrations decay to sub-M within a few m of the source cell. Extracellular H2O2 concentrations scaled linearly with source flux in all the examined conditions. These results establish stringent constraints on autocrine, juxtacrine and next-cell paracrine H2O2 signaling.

systems biology↗

LSD-pipeline: Causal Inference of miRNA Network Effects in Alzheimer's Disease

MicroRNAs (miRNAs) are implicated in Alzheimer's disease (AD), but research has focused on individual miRNAs and direct targets. Existing approaches to miRNA regulation in AD identify associations rather than causal effects, and few methods estimate multi-stage chains from miRNAs through target genes to target transcription factor (TF) cascades. We developed the LSD pipeline (LASSO-SEM-DoWhy), integrating LASSO feature selection, multi-stage structural equation modeling, and DoWhy causal inference to identify and validate miRNA causal pathways in AD. Applying LSD to six blood miRNA and brain mRNA datasets, we identified four LSD-validated miRNAs (miR-30d-5p, miR-92a-3p, miR-296-5p, miR-193a-5p) as AD biomarkers, achieving >86% ROC accuracy in an independent validation cohort. Several miRNAs with no significant direct association with AD showed significant effects when estimated through their target networks, while others significant in direct analysis were not supported at the network level, underscoring the value of network-level analysis. Extending to the TF layer revealed complete miRNA [->] targets [->] TF cascades [->] AD causal chains, with HMGA1, NKX2-3, and PRRX2 as key intermediaries. Confirmed classic pathways converge primarily on tau pathology and synaptic dysfunction. miRNA effects were largely age-independent, suggesting miRNAs act as early initiators of AD pathogenesis. Beyond AD, the LSD pipeline provides a generalizable framework for uncovering causal regulatory mechanisms in other diseases.

systems biology↗

PyKappa: Rule-based modeling in Python

Rule-based languages have proven effective for modeling systems of interacting structured entities as typically encountered in chemistry and molecular biology. We present PyKappa, a rule-based modeling package written in Python whose interpreted nature enables interactive simulation and analysis, including by agentic AI. The package seeks to broaden the base of developers by utilizing a widely known programming language and serves as an easy-to-deploy teaching tool. Using PyKappa, we conduct a case study of phase separation.

systems biology↗