luokai0 Systems Biology Expert
Before Starting
- Which biological scale? (molecular, cellular, tissue, organism)
- Data-driven or mechanistic modeling?
- Steady-state or dynamic analysis?
Core Expertise Areas
Biological Networks
Gene regulatory networks: transcription factor to target gene interactions. Signaling networks: receptor to effector cascades with feedback and crosstalk. Metabolic networks: enzyme-catalyzed reaction networks, stoichiometric models. PPI networks: protein interaction maps from yeast two-hybrid, co-IP, mass spec. Network motifs: recurring subgraphs with functional significance.
Dynamic Modeling
ODE models: deterministic, kinetic rate laws, mass action and Michaelis-Menten. Stochastic models: Gillespie algorithm, intrinsic noise from low molecule numbers. Boolean models: discrete states on and off, fast qualitative analysis. Agent-based models: individual cell behaviors produce tissue-level patterns. Bifurcation analysis: how steady states change with parameter variation.
Multi-Omics Integration
Transcriptomics: mRNA levels by RNA-seq, condition-specific expression. Proteomics: protein abundance by mass spectrometry, post-translational modifications. Metabolomics: small molecule profiling by NMR or mass spectrometry. Integration methods: correlation, regression, network-based, and ML approaches. Single-cell multi-omics: ATAC-seq and RNA-seq in same cell, regulatory inference.
Emergent Properties
Robustness: system maintains function under perturbations, feedback-mediated. Adaptation: return to basal state after step stimulus, integral feedback required. Bistability: two stable states, hysteresis, switch-like behavior. Oscillations: sustained periodic behavior from negative feedback with delay. Noise propagation: how fluctuations in one component affect others.
Best Practices
- Validate models with independent experimental data not used for fitting
- Perform sensitivity analysis to identify key parameters
- Consider measurement noise when fitting models to experimental data
- Use identifiability analysis before parameter estimation
Common Pitfalls
| Pitfall | Fix |
|---|---|
| Overfitting complex models | Use parsimony, prefer simpler models with good fit |
| Ignoring noise in gene expression | Stochastic models needed for low copy number genes |
| Missing feedback loops | Systematically map all regulatory interactions |
| Confusing correlation with causation | Perturbation experiments needed to establish causation |
Related Skills
- computational-biology-expert
- molecular-biology-expert
- mathematics/differential-equations-expert