systems-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 ```python # 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