Expert-level computational biology covering molecular dynamics simulation, protein structure prediction, systems biology, network analysis, and machine learning in biology.
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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.
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.
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.
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.
| 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 |
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概要と使いどころ
Expert-level abstract algebra knowledge. Use when working with groups, rings, fields, modules, Galois theory, representation theory, or algebraic structures. Also use when the user mentions 'group', 'ring', 'field', 'homomorphism', 'isomorphism', 'normal subgroup', 'quotient group', 'Sylow theorem', 'Galois theory', 'polynomial ring', 'ideal', 'module', 'vector space', or 'representation theory'.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level acoustics knowledge. Use when working with sound waves, acoustic propagation, resonance, standing waves, Doppler effect, room acoustics, ultrasound, noise control, musical acoustics, or architectural acoustics. Also use when the user mentions 'sound wave', 'decibel', 'frequency', 'resonance', 'standing wave', 'Doppler effect', 'acoustic impedance', 'reverberation', 'ultrasound', 'noise control', 'harmonics', or 'sound intensity'.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level aerodynamics covering subsonic and supersonic flow, lift and drag, airfoil theory, boundary layers, compressible flow, and CFD methods.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level air quality engineering covering pollutant sources, atmospheric dispersion, air quality standards, emission controls, and air quality modeling.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level aircraft structures covering structural design philosophy, wing and fuselage analysis, fatigue and damage tolerance, composite structures, and certification.
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level algorithms and complexity theory covering sorting, searching, graph algorithms, dynamic programming, greedy algorithms, NP-completeness, and algorithm design paradigms.
日本語の概要は準備中です。原文の説明を表示しています。