systems-biology

name	systems-biology
description	Modeling biological systems
license	MIT
compatibility	opencode
metadata	{"audience":"systems biologists, computational biologists, researchers","category":"biology"}

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