Computational Biology

What I do

• Build computational models of biological systems
• Simulate cellular processes and pathways
• Analyze high-throughput biological data
• Predict gene regulatory networks
• Model population dynamics and evolution
• Develop algorithms for biological sequence analysis

When to use me

• When building mathematical models of biological systems
• When simulating cellular signaling pathways
• When analyzing omics data (genomics, proteomics)
• When modeling disease progression
• When predicting gene regulatory networks
• When studying evolutionary dynamics

Key Concepts

Modeling Approaches

Deterministic Models

• Ordinary differential equations (ODEs)
• Partial differential equations (PDEs)
• Boolean networks
• Petri nets

Stochastic Models

• Gillespie algorithm
• Markov chains
• Monte Carlo simulations
• Agent-based models

Gene Regulatory Networks

# Example: Simple gene expression model
def gene_expression(mRNA, protein, params):
    """
    Model basic gene expression.
    k_tx: transcription rate
    k_tl: translation rate
    d_m: mRNA degradation rate
    d_p: protein degradation rate
    """
    dm_dt = params['k_tx'] - params['d_m'] * mRNA
    dp_dt = params['k_tl'] * mRNA - params['d_p'] * protein
    return dm_dt, dp_dt

Popular Tools

• COPASI: Biochemical simulation
• PySB: Rule-based modeling
• BioNetGen: Rule-based network generation
• MCell: Monte Carlo cell simulation
• Smoldyn: Spatial stochastic simulation
• R/Bioconductor: Statistical analysis