Computational Biology Expert

Before Starting

1. Molecular, cellular, or systems level?
2. Simulation, prediction, or data analysis?
3. Which software ecosystem? (Python, R, GROMACS, NAMD)

Core Expertise Areas

Molecular Dynamics

Force fields: AMBER, CHARMM, GROMOS define atom interactions. Integration: Verlet or leapfrog algorithm integrates equations of motion. Periodic boundary conditions: avoid surface effects, simulate bulk behavior. Thermostat and barostat: maintain temperature and pressure in NPT ensemble. Free energy: umbrella sampling, metadynamics, alchemical perturbation methods.

Protein Structure Prediction

AlphaFold2: deep learning, near-experimental accuracy for single domains. ESMFold: language model based, faster but slightly less accurate than AlphaFold2. Homology modeling: template-based for sequences with known homologs. Intrinsically disordered: not well-handled by structure prediction, need ensemble. Structure validation: DOPE score, MolProbity, Ramachandran plot analysis.

Systems Biology

ODE models: deterministic kinetic models of reaction networks. Stochastic simulation: Gillespie algorithm for small molecule numbers. Boolean networks: coarse-grained on/off gene regulation models. Metabolic flux analysis: FBA predicts steady-state fluxes in metabolic networks. Parameter estimation: MCMC and optimization for fitting models to data.

Network Biology

PPI networks: protein-protein interaction networks, hub proteins. Gene regulatory networks: transcription factor target relationships. Network topology: degree distribution, clustering coefficient, shortest paths. Community detection: modules in biological networks correspond to functional units.

Best Practices

• Always validate simulation against experimental observables
• Use sufficient simulation time to ensure convergence
• Apply appropriate statistical tests to simulation data
• Document all parameters and software versions for reproducibility

Common Pitfalls

Pitfall	Fix
Insufficient simulation length	Check convergence with multiple metrics
Wrong protonation states	Calculate pKa and set protonation at simulation pH
Overinterpreting AlphaFold structures	High confidence regions are reliable, low confidence are not
Ignoring model identifiability	Many parameters may give same fit to data

Related Skills

• molecular-biology-expert
• bioinformatics-expert
• machine-learning-expert