# Cell Biology

> --

- Skill: `neuralblitz/cell-biology-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/cell-biology-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/cell-biology-2/raw
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- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/neuralblitz/cell-biology-2

---

--

## Cell Structure Overview

### Prokaryotic vs. Eukaryotic Cells

| Feature | Prokaryotic | Eukaryotic |
|---------|--------------|------------|
| **Nucleus** | No | Yes |
| **Size** | 0.1-5 μm | 10-100 μm |
| **DNA** | Circular chromosome | Linear chromosomes |
| **Membrane** | Yes | Yes |
| **Organelles** | Few | Many |
| **Cell wall** | Yes (most) | Only plants/fungi |
| **Ribosomes** | 70S | 80S (cytoplasm) |

### Cell Theory

```python
# Cell theory principles
cell_theory = {
    'principle_1': 'All living organisms are composed of one or more cells',
    'principle_2': 'The cell is the basic unit of structure and organization',
    'principle_3': 'Cells arise from pre-existing cells',
    'principle_4': 'Energy flows within cells (biochemistry)',
    'principle_5': 'Genetic information is passed from cell to cell'
}
```

### Cell Size Limitations

```python
# Factors limiting cell size
size_limitations = {
    'surface_area_to_volume_ratio': {
        'description': 'As cell grows, SA/V decreases',
        'impact': 'Limits nutrient/waste exchange',
        'formula': 'SA/V = 3/r for sphere'
    },
    'nuclear_size': {
        'description': 'Nucleus can only control limited cytoplasmic volume',
        'impact': 'Limits gene expression capacity'
    },
    'diffusion_time': {
        'description': 'Molecules take time to diffuse',
        'impact': 'Limits intracellular signaling speed'
    },
    'organelle_density': {
        'description': 'Organelles occupy finite space',
        'impact': 'Limits metabolic capacity'
    }
}
```

-----

## Major Organelles

### Nucleus

```python
# Nuclear structure and function
nucleus = {
    'structure': {
        'nuclear_envelope': 'Double membrane with nuclear pores',
        'nucleolus': 'rRNA synthesis and ribosome assembly',
        'chromatin': 'DNA + histone proteins'
    },
    'functions': [
        'DNA replication',
        'RNA transcription',
        'Ribosome biogenesis',
        'Chromosome organization',
        'Nuclear transport'
    ],
    'diameter': '5-10 μm',
    'pores': '4000-7000 per nucleus in mammalian cells'
}

# Nuclear transport
nuclear_transport = {
    'nuclear_localization_signal': 'PKKKRKV (basic, NLS)',
    'nuclear_export_signal': 'LEU-rich (NES)',
    'transport': 'Importin/Exportin mediated',
    'energy': 'Ran-GTP gradient'
}
```

### Mitochondria

```python
# Mitochondrial structure and function
mitochondria = {
    'structure': {
        'outer_membrane': 'Porous, contains porins',
        'intermembrane_space': 'Similar to cytosol',
        'inner_membrane': 'Cristae, site of ETC',
        'matrix': 'Krebs cycle, DNA, ribosomes'
    },
    'functions': [
        'ATP production (oxidative phosphorylation)',
        'Citric acid cycle',
        'Fatty acid oxidation',
        'Apoptosis initiation',
        'Heat production (thermogenesis)'
    ],
    'genome': 'Circular DNA, 16.5 kb in humans',
    'origin': 'Endosymbiotic (derived from α-proteobacteria)'
}

# Electron transport chain
etc_complexes = {
    'Complex_I': 'NADH dehydrogenase',
    'Complex_II': 'Succinate dehydrogenase',
    'Complex_III': 'Cytochrome bc1 complex',
    'Complex_IV': 'Cytochrome c oxidase',
    'Complex_V': 'ATP synthase'
}

def calculate_atp_production(nadh, fadh2):
    """Calculate theoretical ATP yield"""
    atp_from_nadh = nadh * 2.5  # P/O ratio
    atp_from_fadh2 = fadh2 * 1.5
    return atp_from_nadh + atp_from_fadh2
```

### Endomembrane System

```python
# Endomembrane system components
endomembrane = {
    'endoplasmic_reticulum': {
        'rough_ER': 'Protein synthesis, ribosome-bound',
        'smooth_ER': 'Lipid synthesis, detoxification',
        'functions': [
            'Protein folding and modification',
            'Lipid biosynthesis',
            'Calcium storage'
        ]
    },
    'golgi_apparatus': {
        'cis': 'Receiving (cis-face)',
        'medial': 'Processing',
        'trans': 'Sorting (trans-face)',
        'functions': [
            'Protein modification (glycosylation)',
            'Protein sorting',
            'Lipid modification'
        ]
    },
    'lysosomes': {
        'function': 'Intracellular digestion',
        'enzymes': 'Acid hydrolases (pH 4.5-5)',
        'substrates': 'Proteins, nucleic acids, lipids, carbohydrates'
    },
    'endosomes': {
        'early': 'Sorting endosomes',
        'late': 'Maturation to lysosomes'
    }
}
```

### Cytoskeleton

```python
# Cytoskeletal components
cytoskeleton = {
    'microtubules': {
        'diameter': '25 nm',
        'subunit': 'α/β-tubulin dimer',
        'GTP': 'Required for polymerization',
        'motor_proteins': 'Kinesin (anterograde), Dynein (retrograde)',
        'organization': 'Centrosome (animal cells)'
    },
    'actin_filaments': {
        'diameter': '7 nm',
        'subunit': 'G-actin monomer',
        'ATP': 'Required for polymerization',
        'motor_proteins': 'Myosin',
        'organization': 'Cortical, stress fibers'
    },
    'intermediate_filaments': {
        'diameter': '10 nm',
        'types': 'Vimentin, keratin, lamin',
        'function': 'Structural support'
    }
}
```

-----

## Cell Signaling

### Types of Cell Signaling

```python
# Signaling types
signaling_types = {
    'autocrine': {
        'description': 'Cell signals to itself',
        'example': 'Cytokine signaling in immune cells'
    },
    'paracrine': {
        'description': 'Local signaling to nearby cells',
        'example': 'Synaptic signaling'
    },
    'endocrine': {
        'description': 'Long-distance via bloodstream',
        'example': 'Hormone signaling'
    },
    'juxtacrine': {
        'description': 'Direct cell-cell contact',
        'example': 'Notch signaling'
    }
}

# Signal transduction types
signal_types = {
    'lipid_second_messengers': ['DAG', 'IP3', 'cAMP', 'cGMP'],
    'calcium_signaling': ['Ca²⁺ release from ER', 'store-operated calcium entry'],
    'kinase_cascades': ['MAPK pathway', 'PI3K/Akt pathway'],
    'ion_channels': ['Ligand-gated', 'Voltage-gated']
}
```

### Major Signaling Pathways

```python
# Receptor tyrosine kinase (RTK) signaling
rtk_signaling = {
    'receptors': ['EGFR', 'InsR', 'FGFR', 'PDGFR'],
    'ligands': ['EGF', 'Insulin', 'FGF', 'PDGF'],
    'pathway': [
        '1. Ligand binding → receptor dimerization',
        '2. Autophosphorylation of tyrosine residues',
        '3. Adapter proteins bind phosphotyrosines',
        '4. RAS/MAPK, PI3K/Akt pathways activated'
    ],
    'downstream': ['MAPK/ERK', 'PI3K/Akt', 'PLCγ']
}

# G protein-coupled receptor (GPCR) signaling
gpcr_signaling = {
    'structure': '7 transmembrane domains',
    'G_proteins': ['Gs (stimulatory)', 'Gi (inhibitory)', 'Gq (phospholipase C)'],
    'pathways': [
        'Gs → Adenylyl cyclase → cAMP → PKA',
        'Gq → PLC → IP3/DAG → PKC',
        'Gi → Adenylyl cyclase inhibition'
    ]
}

# Nuclear receptor signaling
nuclear_receptor_signaling = {
    'receptors': ['ER', 'GR', 'TR', 'RAR', 'VDR'],
    'ligands': ['Steroid hormones', 'Thyroid hormone', 'Retinoic acid', 'Vitamin D'],
    'mechanism': 'Lipid-soluble → cytoplasm → nucleus → gene transcription'
}
```

### Second Messengers

```python
# Second messenger systems
second_messengers = {
    'cAMP': {
        'synthesized_by': 'Adenylyl cyclase',
        'degraded_by': 'Phosphodiesterase',
        'effectors': ['PKA', 'EPAC', 'CNG channels'],
        'pathway': 'G_s → AC → cAMP → PKA'
    },
    'IP3_DAG': {
        'synthesized_by': 'Phospholipase C',
        'targets': ['IP3 → ER Ca²⁺ release', 'DAG → PKC activation'],
        'pathway': 'G_q → PLC → IP3/DAG'
    },
    'calcium': {
        'sources': ['ER (via IP3R)', 'Extracellular', 'Mitochondria'],
        'buffers': 'Calmodulin, parvalbumin',
        'effectors': 'Calmodulin, PKC, CaMK'
    },
    'cGMP': {
        'synthesized_by': 'Guanylyl cyclase',
        'degraded_by': 'Phosphodiesterase',
        'effectors': ['PKG', 'CNG channels', 'PDEs']
    }
}
```

-----

## Cell Cycle Regulation

### Cell Cycle Phases

```python
# Cell cycle phases
cell_cycle = {
    'G1': {
        'duration': 'Variable (hours to days)',
        'events': [
            'Cell growth',
            'Protein synthesis',
            'Organelle replication',
            'G1 checkpoint (restriction point)'
        ],
        'cyclins': 'Cyclin D binds CDK4/6'
    },
    'S': {
        'duration': '8-10 hours (human)',
        'events': [
            'DNA replication',
            'Histone synthesis',
            'Centrosome duplication'
        ],
        'cyclins': 'Cyclin E (early), Cyclin A (later)'
    },
    'G2': {
        'duration': '4-6 hours',
        'events': [
            'Cell growth',
            'Protein synthesis',
            'G2 checkpoint'
        ],
        'cyclins': 'Cyclin A binds CDK1'
    },
    'M': {
        'duration': '1-2 hours',
        'events': [
            'Prophase: Chromatin condensation',
            'Metaphase: Chromosome alignment',
            'Anaphase: Sister chromatid separation',
            'Telophase/Cytokinesis: Cell division'
        ],
        'cyclins': 'Cyclin B binds CDK1 (M-phase promoting factor)'
    }
}

# Cell cycle checkpoints
checkpoints = {
    'G1_S_checkpoint': {
        'restrictions': 'DNA damage', 'nutrient status', 'cell size',
        'key_proteins': 'p53, p21, Rb'
    },
    'G2_M_checkpoint': {
        'restrictions': 'DNA replication complete', 'DNA damage',
        'key_proteins': 'Chk1, Chk2, Cdc25'
    },
    'M_checkpoint': {
        'restrictions': 'Chromosome attachment to spindle',
        'key_proteins': 'Mad2, BubR1, APC/C'
    }
}
```

### Cyclins and CDKs

```python
# Cyclin-CDK complexes
cyclin_cdk = {
    'G1_CDK': {
        'cyclins': ['Cyclin D'],
        'cdks': ['CDK4', 'CDK6'],
        'substrates': ['Rb protein'],
        'function': 'G1 progression'
    },
    'G1_S_transition': {
        'cyclins': ['Cyclin E'],
        'cdks': ['CDK2'],
        'substrates': ['Rb protein'],
        'function': 'S phase entry'
    },
    'S_phase': {
        'cyclins': ['Cyclin A'],
        'cdks': ['CDK2'],
        'function': 'DNA replication'
    },
    'G2_M_transition': {
        'cyclins': ['Cyclin A', 'Cyclin B'],
        'cdks': ['CDK1'],
        'function': 'M phase entry'
    }
}

# Cell cycle regulators
regulators = {
    'positive': ['Cyclins', 'CDK1/2/4/6', 'CDC25'],
    'negative': ['p21', 'p27', 'p16', 'p53']
}
```

-----

## Cell Death Pathways

### Apoptosis

```python
# Apoptosis pathways
apoptosis = {
    'intrinsic_mitochondrial': {
        'triggers': ['DNA damage', 'Oxidative stress', 'Growth factor withdrawal'],
        'key_events': [
            'Mitochondrial outer membrane permeabilization (MOMP)',
            'Cytochrome c release',
            'Caspase-9 activation',
            'Apoptosome formation'
        ],
        'regulators': ['Bcl-2 family', 'p53', 'IAPs']
    },
    'extrinsic_death_receptor': {
        'receptors': ['Fas/CD95', 'TNF-R1', 'TRAIL-R'],
        'pathways': ['Fas → FADD → Caspase-8', 'TNF → TRADD → Caspase-8'],
        'key_events': [
            'Death receptor activation',
            'DISC formation',
            'Caspase-8 activation'
        ]
    }
}

# Caspase cascade
caspases = {
    'initiator_caspases': ['Caspase-8', 'Caspase-9', 'Caspase-10'],
    'executioner_caspases': ['Caspase-3', 'Caspase-6', 'Caspase-7'],
    'substrates': ['PARP', 'Lamin', 'Actin', 'DNA repair proteins']
}
```

### Other Cell Death Types

```python
# Necrosis
necrosis = {
    'characteristics': [
        'Cell swelling',
        'Membrane rupture',
        'Release of cellular contents',
        'Inflammation'
    ],
    'triggers': ['Physical injury', 'Toxins', 'Ischemia']
}

# Autophagy
autophagy = {
    'types': ['Macroautophagy', 'Microautophagy', 'Chaperone-mediated'],
    'process': [
        'Initiation: ULK1 complex',
        'Nucleation: PI3K complex',
        'Elongation: LC3 conjugation',
        'Fusion: Autophagosome with lysosome'
    ],
    'functions': 'Recycling, stress survival, quality control'
}

# Ferroptosis
ferroptosis = {
    'characteristics': ['Iron-dependent', 'Lipid peroxidation', 'No caspase activation'],
    'triggers': ['GPX4 inhibition', 'Iron overload', 'Lipid ROS accumulation'],
    'inhibitors': ['Ferrostatin-1', 'Liproxstatin-1']
}
```

-----

## Cell Culture Techniques

### Basic Cell Culture

```python
# Cell culture fundamentals
culture_conditions = {
    'temperature': '37°C (mammalian)',
    'co2': '5% CO₂',
    'ph': '7.2-7.4',
    'osmolarity': '280-310 mOsm',
    'serum': '10% FBS (typically)'
}

# Common cell lines
cell_lines = {
    'HeLa': {
        'origin': 'Human cervical cancer',
        'type': 'Epithelial',
        'applications': 'General transfection, protein expression'
    },
    'HEK293': {
        'origin': 'Human embryonic kidney',
        'type': 'Epithelial',
        'applications': 'Transfection, protein production'
    },
    'COS-7': {
        'origin': 'African green monkey kidney',
        'type': 'Fibroblast',
        'applications': 'Transient expression'
    },
    'NIH-3T3': {
        'origin': 'Mouse embryo',
        'type': 'Fibroblast',
        'applications': 'Transfection, transformation'
    }
}

# Cell counting
def calculate_cell_concentration(count, dilution_factor, hemocytometer_squares):
    """
    Calculate cell concentration.
    """
    cells_per_ml = (count / hemocytometer_squares) * dilution_factor * 10**4
    return cells_per_ml
```

### Transfection Methods

```python
# Transfection methods comparison
transfection_methods = {
    'lipofection': {
        'efficiency': 'High',
        'toxicity': 'Moderate',
        'applications': 'Transient transfection'
    },
    'electroporation': {
        'efficiency': 'High (cell type dependent)',
        'toxicity': 'High',
        'applications': 'Stable transfection, primary cells'
    },
    'calcium_phosphate': {
        'efficiency': 'Moderate',
        'toxicity': 'Low',
        'applications': 'Stable transfection, HEK293'
    },
    'viral': {
        'efficiency': 'Very high',
        'toxicity': 'Variable',
        'applications': 'Gene delivery, difficult cells'
    }
}
```

-----

## Common Errors to Avoid

- **Contamination**: Aseptic technique is essential
- **Mycoplasma**: Regular testing recommended
- **Cell line authentication**: Verify identity
- **P-assage number**: Limit passages for primary cells
- **Media/serum variability**: Test lots
- **Freezing damage**: Use DMSO, controlled rates
- **Over confluency**: Subculture before contact inhibition
- **Temperature shifts**: Keep cells at 37°C
- **pH drift**: CO₂ incubator essential
- **Ignoring morphology**: Changes indicate problems