BU RISE Modeling Project

A comprehensive computational neuroscience project focused on modeling BDNF (Brain-Derived Neurotrophic Factor) networks and their role in neurodegenerative diseases. This project combines advanced neuronal modeling with drug intervention studies to understand tipping points in brain networks and potential therapeutic strategies. Accepted for presentation at the annual Society For Neuroscience Conference.

🧬 Project Overview

This project models the complex dynamics of BDNF networks in neuronal systems, particularly focusing on:

• BDNF/proBDNF Tipping Points: Understanding critical ratios that lead to network failure
• Drug Intervention Studies: Simulating therapeutic BDNF injections at different disease stages
• Network Topology Analysis: Examining small-world properties and connectivity patterns
• Biologically Accurate Modeling: Implementing realistic neuronal and synaptic dynamics

🔬 Key Features

1. Neuronal Network Modeling

• ODE-based Neuron Models: Custom NEURON mechanisms implementing BDNF biochemistry
• Probabilistic Synapses: Realistic synaptic transmission with weight adaptation
• Multi-layer Architecture: Cortical layer differentiation with biological accuracy
• Activity-dependent Plasticity: Synaptic strength changes based on network activity

2. BDNF Biochemistry

• proBDNF/mBDNF Dynamics: Modeling synthesis, cleavage, and degradation
• Receptor Interactions: p75 and TrkB receptor binding kinetics
• Neurotrophic Effects: Growth and apoptosis signaling pathways
• tPA Regulation: Tissue plasminogen activator involvement in BDNF processing

3. Disease Modeling

• Tipping Point Analysis: Systematic exploration of proBDNF:mBDNF ratios (1:3 to 1:20)
• Network Degradation: Progressive loss of connections and clustering
• Catastrophic Failure: Identification of critical thresholds for network collapse
• Recovery Patterns: Analysis of network resilience and repair mechanisms

4. Drug Intervention Studies

• Early Intervention Trials: BDNF injection at different disease stages
• Dose Response Analysis: Various BDNF concentrations (0.1-0.5 μM)
• Temporal Recovery: Short-term and long-term therapeutic effects
• Layer-specific Analysis: Differential responses across cortical layers

📁 Project Structure

BU_RISE_Modelling_Project/
├── 🧠 Core Models
│   ├── neuronal_network_class.py      # Main network implementation
│   ├── ode_neuron_class.py           # Individual neuron model
│   └── test.py                       # Testing and validation
│
├── 🔧 NEURON Mechanisms
│   ├── ode_neuron.mod                # BDNF biochemistry model
│   └── probabilistic_syn.mod         # Synaptic dynamics
│
├── 🧪 Drug Trials
│   └── drug_trials/                  # Intervention study results
│       ├── early_intervention_mild_*/ # Multiple trial runs
│       └── my_trial_*/               # Custom trial configurations
│
├── 📊 Analysis Results
│   ├── bdnf_network_analysis/        # BDNF-specific analysis
│   ├── biological_network_analysis/  # General network properties
│   └── bdnf_tests/                   # Activity effect studies
│
├── 🎯 Reference Implementations
│   └── good_ones/                    # Validated model versions
│       ├── biologically_accurate.py  # Biologically realistic network
│       └── graphs/                   # Network visualization tools
│
└── 📈 Utilities
    ├── view_ata.py                   # Data analysis tools
    └── tipping_point_summary.txt     # Critical ratio analysis

🚀 Getting Started

Prerequisites

• Python 3.7+
• NEURON simulator
• Required Python packages:

  pip install neuron neuronpp numpy matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/BU_RISE_Modelling_Project.git
   cd BU_RISE_Modelling_Project

2. Compile NEURON mechanisms:

   nrnivmodl

3. Run basic test:

   python test.py

Quick Start Example

from neuronal_network_class import CorrectedNeuronalNetwork

# Create a small network
initial_concentrations = [0.2, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.1]
neuron_params = [0.005, 0.0025, 1.0, 0.9, 0.0005, 0.2, 0.1, 1.0, 0.9, 0.005, 0.3, 0.1, 0.0001]

network = CorrectedNeuronalNetwork(
    rows=5, 
    cols=5,
    initial_neuron_concentrations=initial_concentrations,
    neuron_params=neuron_params
)

# Run simulation
network.run_simulation(duration=20000)

📊 Key Results

BDNF Tipping Points

The project has identified critical proBDNF:mBDNF ratios that determine network survival:

Ratio	Survival %	Network State
1:3	7.5%	Catastrophic failure
1:3.6	57.5%	50% loss threshold
1:4.2	95.9%	Stable network
1:7.4	47.2%	Recovery possible

Drug Intervention Efficacy

• Early intervention (mild disease): 61.7% immediate health recovery
• Optimal dose: 0.3 μM BDNF for sustained effects
• Recovery time: 1-5 hours for significant improvement
• Network resilience: Improved clustering and small-world properties

Network Properties

• Clustering Coefficient: 0.075 - 0.476
• Average Path Length: 2.095 - 3.858
• Small-World Coefficient: 0.024 - 0.175

🔬 Scientific Applications

Research Areas

1. Neurodegenerative Disease Modeling

   • Alzheimer's disease progression
   • Parkinson's disease network effects
   • Stroke recovery mechanisms

2. Drug Development

   • BDNF-based therapeutics
   • Optimal dosing strategies
   • Timing of interventions

3. Network Neuroscience

   • Criticality in brain networks
   • Small-world topology analysis
   • Resilience and recovery patterns

Publications & Presentations

This work contributes to understanding:

• BDNF signaling in neurodegenerative diseases
• Network-level effects of neurotrophic factors
• Computational approaches to drug intervention

🎉 Conference Presentation:

• Society for Neuroscience (SfN) Annual Meeting 2025 - Accepted for presentation
• Abstract Title: "A Multiscale Systems Biology Framework to Model mBDNF–proBDNF-Mediated Bifurcation Dynamics in CNS Neurotrophin Signaling"
• Presentation Type: Late-Breaking Poster presentation
• Conference Dates: November 19, 2025, San Diego, CA

🛠️ Development

Running Tests

# Test individual components
python test.py

# Validate network dynamics
python good_ones/biologically_accurate.py

# Analyze existing data
python view_data.py

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Submit a pull request

Code Structure

• Modular Design: Separate classes for neurons, networks, and analysis
• NEURON Integration: Custom mechanisms for biological accuracy
• Data Persistence: Pickle-based storage for analysis results
• Visualization: Comprehensive plotting and analysis tools

📈 Future Directions

1. Enhanced Disease Models

   • Multiple neurodegenerative conditions
   • Age-related network changes
   • Genetic risk factors

2. Advanced Drug Modeling

   • Combination therapies
   • Sustained release formulations
   • Targeted delivery systems

3. Machine Learning Integration

   • Predictive modeling of network states
   • Optimization of intervention strategies
   • Automated parameter tuning

📞 Contact & Support

For questions, collaborations, or support:

• Principal Investigator: Ethan Liu
• Institution: Boston University RISE Program
• Email: eel170984@gmail.com

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Boston University RISE Program
• Society for Neuroscience (SfN) for accepting this work for presentation at SfN 2025
• NEURON development team
• Computational neuroscience community
• Biological data contributors

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This project represents cutting-edge research in computational neuroscience, combining detailed biochemical modeling with network-level analysis to advance our understanding of brain diseases and therapeutic interventions.