Paper List
-
Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
-
Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
-
Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
-
MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
-
The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
-
A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
-
ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
-
DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
School of Mathematics and Statistics, Rochester Institute of Technology | School of Physics, Rochester Institute of Technology | School of Physics and Astronomy & School of Mathematics and Statistics, Rochester Institute of Technology
The 30-Second View
IN SHORT: This work addresses the challenge of systematically characterizing how the high-dimensional parameter space of Markov models for different sodium channel isoforms influences the robustness and excitability of neuronal firing.
Innovation (TL;DR)
- Methodology Integrates a six-state Markov model for nine human NaV isoforms with a simplified KV3.1 model, enabling a unified framework for isoform-specific stability analysis.
- Methodology Applies bifurcation theory and local stability analysis to map 'excitable landscapes' across the (g_Na, g_K) parameter space, visualizing regions supporting stable oscillatory behavior.
- Biology Quantitatively ranks NaV isoforms by their supported excitable regimes, identifying NaV1.3, 1.4, and 1.6 as broadly supportive and NaV1.7 and 1.9 as minimally oscillatory.
Key conclusions
- Isoforms NaV1.3, NaV1.4, and NaV1.6 support the broadest parameter regions for stable limit cycles (oscillatory firing), indicating their robustness in sustaining action potential trains.
- Isoforms NaV1.7 and NaV1.9 exhibit minimal oscillatory behavior across the tested conductance parameter space, correlating with their specialized roles in peripheral nociception.
- The hybrid Markov-HH modeling and stability analysis framework successfully narrows the vast parameter search space for designing synthetic excitable systems, moving from trial-and-error to principled design.
Abstract: We investigate a conductance‑based neuron model to explore how voltage‑gated ion channel isoforms influence action‑potential generation. The model combines a six‑state Markov representation of NaV channels with a first‑order KV3.1 model, allowing us to vary maximal sodium and potassium conductances and compare nine NaV isoforms. Using bifurcation theory and local stability analysis, we map regions of stable limit cycles and visualize excitability landscapes via heatmap‑based diagrams. These analyses show that isoforms NaV1.3, NaV1.4 and NaV1.6 support broad excitable regimes, while isoforms NaV1.7 and NaV1.9 exhibit minimal oscillatory behavior. Our findings provide insights into the role of channel heterogeneity in neuronal dynamics and may help to guide the design of synthetic excitable systems by narrowing the parameter space needed for robust action‑potential trains.