NeuralBlitz What I do
- Model and simulate biological systems
- Analyze omics data integration
- Study gene regulatory networks
- Investigate signaling pathways
- Apply network analysis
- Develop computational models
When to use me
- When modeling cellular processes
- When analyzing high-throughput data
- When studying network dynamics
- When integrating multi-omics data
- When simulating biological systems
- When predicting system behavior
Key Concepts
Modeling Frameworks
Mathematical Models
- Ordinary differential equations (ODEs)
- Boolean networks
- Petri nets
- Agent-based models
- Constraint-based models
Metabolic Modeling
# Example: Flux Balance Analysis (FBA)
def fba(stoichiometry, objective, constraints):
"""
Predict metabolic fluxes.
S: Stoichiometric matrix
v: Flux vector
maximize: c^T v
subject to: S·v = 0, lb ≤ v ≤ ub
"""
# Linear programming problem
# Maximize objective function
# Subject to mass balance and capacity constraints
return optimize.linprog(objective, bounds=constraints)
# Key components
fba_components = {
'stoichiometric_matrix': 'S[m×n] - m metabolites, n reactions',
'objective_function': 'c^T v - typically biomass production',
'constraints': 'lb ≤ v ≤ ub - reaction bounds'
}
Network Analysis
- Topology: Degree distribution, betweenness
- Motifs: Feed-forward loops, feedback loops
- Robustness: Redundancy, modularity
- Dynamics: Stability, oscillations
Data Integration
- Genomics: Gene content
- Transcriptomics: Gene expression
- Proteomics: Protein abundance
- Metabolomics: Metabolic state
- Fluxomics: Reaction rates
Tools
- COBRA: Constraint-based reconstruction
- COPASI: Biochemical simulation
- PySB: Rule-based modeling
- CellDesigner: Pathway diagrams
- Cytoscape: Network visualization