🧬 CD40-Immunosome

Systems Modeling Framework for CD40–TRAF6 Signaling and CRISPR Synergy

Overview

**CD40-Immunosome** is a reproducible computational systems immunology framework for modeling feedback-regulated CD40–TRAF6 signaling dynamics and CRISPR-mediated perturbations.

The platform integrates:

• Ordinary Differential Equation (ODE) modeling of receptor-proximal signaling
• SOCS1-mediated negative feedback simulation
• Monte Carlo robustness analysis
• Quantitative synergy scoring using area-under-the-curve (AUC) metrics
• Interactive Streamlit-based visualization dashboard

This repository accompanies the preprint:

Nama, Y. (2026).
CD40-Immunosome: A Systems Modeling Framework for CD40–TRAF6 Signaling and CRISPR Synergy.

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Biological Motivation

CD40 activation plays a critical role in dendritic cell maturation and anti-tumor immunity. However, signaling amplitude and duration are tightly regulated by intracellular feedback loops, particularly SOCS1-mediated attenuation.

This framework addresses three key questions:

1. How does scaffold-mediated receptor clustering alter NF-κB dynamics?
2. What is the quantitative impact of SOCS1 deletion on signaling persistence?
3. Can multi-parameter modeling predict synergistic immunotherapeutic strategies?

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Model Architecture

1️⃣ Core ODE System

The signaling network models:

• TRAF6 recruitment
• NF-κB activation
• SOCS1 negative feedback

The system is numerically integrated using a fixed-step Runge–Kutta 4th order (RK4) solver over a 200-minute simulation window.

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2️⃣ Null-Model Comparison

Feedback inhibition can be disabled (k6 = 0, k7 = 0) to simulate SOCS1-deficient conditions and compare:

• Transient activation (wild-type)
• Sustained plateau dynamics (knockout)

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3️⃣ Monte Carlo Robustness Analysis

• ±20% stochastic perturbation of kinetic parameters
• n = 50 simulations
• Quantifies structural stability of transient peak dynamics

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4️⃣ CRISPR Synergy Quantification

Modified Bliss Independence metric:

Synergy = (AUC_agonist_KO - AUC_agonist) / AUC_agonist * 100

Allows systematic comparison of simulated knockouts targeting:

• SOCS1
• PD-L1
• CTLA-4
• IL-10

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Interactive Dashboard

The Streamlit interface enables:

• Real-time kinetic parameter manipulation
• Visualization of NF-κB temporal dynamics
• Null-model comparisons
• Monte Carlo sensitivity analysis
• Automated synergy score export

Live Web App:
https://cd40-immunosome-tool-yash.streamlit.app/

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📂 Repository Structure

CD40-Immunosome-Tool/
│
├── app.py
├── requirements.txt
├── README.md
├── LICENSE
├── CITATION.cff
├── dashboard.png
└── assets/

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🛠 Installation

1️⃣ Clone the repository

git clone https://github.com/YASH4-HD/CD40-Immunosome-Tool.git
cd CD40-Immunosome-Tool

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Launch the dashboard

streamlit run app.py

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🔁 Reproducibility

All simulations are reproducible using:

• Deterministic RK4 solver
• Fixed parameter configuration
• Defined Monte Carlo perturbation range
• Explicit synergy scoring formula
• No proprietary datasets are required.

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📜 Citation

If you use this suite in your research, please cite it as:

Nama, Y. (2026). CD40-Immunosome: A Systems Modeling Framework for CD40–TRAF6 Signaling and CRISPR Synergy (Version 1.0.1) Zenodo. https://doi.org/10.5281/zenodo.18850205. GitHub. https://github.com/YASH4-HD/CD40-Immunosome-Tool

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Author

Yashwant Nama
Independent Researcher | Systems Immunology & Computational Modeling

Focus: Systems Immunology, Mechanobiology, Computational Modeling and Reproducible Bioinformatics.

🔗 Connect & Verify:

• ORCID: 0009-0003-3443-4413
• LinkedIn: Yashwant Nama
• Project Website: Streamlit Dashboard