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 ```python # 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