# Biotechnology

> --

- Skill: `neuralblitz/biotechnology` (Agent Skill)
- Install (CLI): `npx skillmds@latest add neuralblitz/biotechnology`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neuralblitz/biotechnology/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: NeuralBlitz (https://skillmd.com/u/neuralblitz)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/neuralblitz/biotechnology

---

--

## Genetic Engineering

### Molecular Cloning

```python
# Basic cloning workflow
cloning_workflow = {
    'step1': 'Isolate vector and insert DNA',
    'step2': 'Digest with restriction enzymes',
    'step3': 'Ligate insert into vector',
    'step4': 'Transform into host cells',
    'step5': 'Screen for recombinants',
    'step6': 'Verify with sequencing'
}

# Restriction enzyme types
restriction_enzyme_types = {
    'Type_II': {
        'description': 'Cleave at specific sequence',
        'examples': ['EcoRI', 'BamHI', 'HindIII'],
        'recognition': 'Palindromic, 4-8 bp'
    },
    'Type_III': {
        'description': 'Cleave away from site',
        'examples': ['EcoPI', 'HinfI']
    },
    'Type_IV': {
        'description': 'Modified DNA',
        'examples': ['McrBC']
    }
}

# Ligation
def calculate_ligation_efficiency(insert_ng, vector_ng, insert_size, vector_size, molar_ratio=3):
    """
    Calculate optimal ligation conditions.
    """
    # Moles calculation
    vector_moles = vector_ng / (vector_size * 650)  # 650 Da per bp
    insert_moles = insert_ng / (insert_size * 650) * molar_ratio
    
    return vector_moles, insert_moles
```

### PCR Applications

```python
# PCR types
pcr_types = {
    'standard': 'Basic amplification',
    'hot_start': 'Reduced non-specific products',
    'multiplex': 'Multiple targets',
    'quantitative': 'qPCR for quantification',
    'digital': 'dPCR for absolute quantification',
    'reverse_transcription': 'RT-PCR for RNA'
}

# Primer design rules
primer_design = {
    'length': '18-25 nucleotides',
    'tm': '55-65°C (within 5°C of each other)',
    'gc_content': '40-60%',
    'avoid': ['Hairpins', 'Dimers', 'Repeats'],
    'gc_clamp': '1-2 bp at 3' end',
    'product_size': '100-3000 bp typical'
}

# qPCR analysis
qpcr_analysis = {
    'reference_genes': ['GAPDH', 'ACTB', '18S rRNA'],
    'normalization': 'ΔCt method',
    'comparison': 'ΔΔCt method',
    'efficiency': 'E = 10^(-1/slope) - 1'
}
```

### CRISPR-Cas Systems

```python
# CRISPR system types
crispr_types = {
    'Cas9': {
        'type': 'Class 2, Type II',
        'function': 'Double-strand break',
        'pam': 'NGG',
        'applications': 'Knockout, knockin'
    },
    'Cas12a': {
        'type': 'Class 2, Type V',
        'function': 'Staggered cut',
        'pam': 'TTTV',
        'applications': 'Cleavage, diagnostics'
    },
    'Cas13': {
        'type': 'Class 2, Type VI',
        'function': 'RNA cleavage',
        'pam': 'PFS',
        'applications': 'RNA editing, detection'
    },
    'prime_editing': {
        'type': 'Cas9-nickase + RT',
        'function': 'All 12 types of mutation',
        'no_double_strand_break': True
    }
}

# sgRNA design
def design_sgrna(target_sequence, pam='NGG', gc_range=(0.3, 0.7)):
    """
    Design single guide RNA targets.
    """
    targets = []
    
    for i in range(len(target_sequence) - 22):
        if target_sequence[i+21:i+23] == pam[:2]:
            protospacer = target_sequence[i:i+20]
            
            # Check GC content
            gc = (protospacer.count('G') + protospacer.count('C')) / 20
            if gc_range[0] <= gc <= gc_range[1]:
                targets.append({
                    'position': i,
                    'protospacer': protospacer,
                    'pam': target_sequence[i+20:i+23]
                })
    
    return targets
```

-----

## Bioprocess Engineering

### Fermentation Systems

```python
# Fermentation types
fermentation_types = {
    'batch': {
        'description': 'Closed system, no addition during run',
        'advantages': ['Simple', 'Low contamination risk'],
        'disadvantages': ['Low productivity', 'Catabolite repression']
    },
    'fed_batch': {
        'description': 'Feed substrate during run',
        'advantages': ['High cell density', 'Controlled feeding'],
        'applications': ['Recombinant protein', 'High yield']
    },
    'continuous': {
        'description': 'Continuous inlet/outlet',
        'advantages': ['Steady state', 'High productivity'],
        'disadvantages': ['Stability issues', 'Contamination risk']
    },
    'perfusion': {
        'description': 'Cell retention, product removal',
        'advantages': ['High cell density', 'Continuous harvest'],
        'applications': ['Cell therapy', 'Biopharmaceuticals']
    }
}

# Growth kinetics
class MicrobialKinetics:
    @staticmethod
    def monod_equation(mu, mu_max, ks, s):
        """
        Monod growth model.
        mu: Specific growth rate
        mu_max: Maximum growth rate
        ks: Half-saturation constant
        s: Substrate concentration
        """
        return mu_max * s / (ks + s)
    
    @staticmethod
    def yield_coefficient(s0, sf, x0, xf):
        """
        Calculate biomass yield.
        """
        Yx_s = (xf - x0) / (s0 - sf)
        return Yx_s
    
    @staticmethod
    def product_formation(x, yp_x, s):
        """
        Product formation kinetics.
        """
        # Luedeking-Piret model
        alpha = 0.1  # Growth-associated
        beta = 0.01  # Non-growth-associated
        return alpha * x + beta * x
```

### Bioreactor Design

```python
# Bioreactor types
bioreactor_types = {
    'stirred_tank': {
        'agitation': 'Impeller',
        'scale_up': 'Power/volume constant',
        'applications': 'Most common'
    },
    'airlift': {
        'description': 'Internal/external circulation',
        'applications': 'Shear-sensitive cultures'
    },
    'wave_bioreactor': {
        'description': 'Wave-induced mixing',
        'applications': 'Cell therapy, vaccines'
    },
    'hollow_fiber': {
        'description': 'Semi-permeable membranes',
        'applications': 'High-value products'
    }
}

# Scale-up criteria
scaleup_criteria = {
    'power_per_volume': 'Constant P/V',
    'tip_speed': 'Constant impeller tip speed',
    'oxygen_transfer': 'Constant kLa',
    'mixing_time': 'Constant mixing time',
    'shear': 'Constant average shear rate'
}
```

-----

## Recombinant Protein Production

### Expression Systems

```python
# Expression system comparison
expression_systems = {
    'E_coli': {
        'pros': ['Fast', 'Cheap', 'Well-characterized'],
        'cons': ['No post-translation', 'Inclusion bodies possible'],
        'best_for': 'Simple proteins, enzymes'
    },
    'S_cerevisiae': {
        'pros': ['Eukaryotic', 'Secretion possible'],
        'cons': ['Lower yield', 'Hyperglycosylation'],
        'best_for': 'Secreted proteins'
    },
    'Pichia_pastoris': {
        'pros': ['High yield', 'Secretion', 'Glycosylation'],
        'cons': ['Methanol requirement'],
        'best_for': 'Secreted proteins, scale-up'
    },
    'Insect_cells': {
        'pros': ['Complex proteins', 'Post-translation'],
        'cons': ['Lower yield', 'More expensive'],
        'best_for': 'Complex eukaryotic proteins'
    },
    'Mammalian_cells': {
        'pros': 'Human-like glycosylation',
        'cons': ['Slow', 'Expensive'],
        'best_for': 'Therapeutic antibodies'
    }
}

# Optimization strategies
protein_optimization = {
    'codon_optimization': 'Match host tRNA usage',
    'promoter_choice': 'Inducible vs constitutive',
    'signal_peptide': 'Secretion signal',
    'fusion_tags': 'His, GST, MBP, SUMO',
    'folding': ' chaperone co-expression',
    'solubility': 'Solubility enhancers'
}
```

### Protein Purification

```python
# Purification strategies
purification_methods = {
    'affinity': {
        'tags': ['His-tag (Ni-NTA)', 'GST', 'MBP', 'Strep'],
        'principle': 'Specific binding',
        'advantage': 'High purity in one step'
    },
    'ion_exchange': {
        'anion': 'Bind at pH > pI',
        'cation': 'Bind at pH < pI',
        'principle': 'Charge interaction'
    },
    'hydrophobic_interaction': {
        'principle': 'Hydrophobic patches',
        'high_salt': 'Promotes binding'
    },
    'size_exclusion': {
        'principle': 'Molecular size',
        'desalting': 'Buffer exchange'
    }
}

# Chromatography parameters
chromatography_params = {
    'binding_capacity': 'mg protein/mL resin',
    'flow_rate': 'cm/hr or mL/min',
    'resolution': 'Separation of peaks',
    'yield': 'Recovery percentage',
    'purity': 'Target purity level'
}
```

-----

## Industrial Enzymes

### Enzyme Classes

```python
# Industrial enzyme types
industrial_enzymes = {
    'hydrolases': {
        'examples': ['Amylase', 'Protease', 'Lipase', 'Cellulase'],
        'applications': ['Starch processing', 'Detergents', 'Baking']
    },
    'oxidoreductases': {
        'examples': ['Glucose oxidase', 'Laccase', 'Peroxidase'],
        'applications': ['Food industry', 'Textile', 'Biosensors']
    },
    'transferases': {
        'examples': ['Transglutaminase', 'Glycosyltransferase'],
        'applications': ['Crosslinking', 'Glycosylation']
    },
    'lyases': {
        'examples': ['Pectin lyase', 'Alginate lyase'],
        'applications': ['Fruit processing', 'Algae processing']
    },
    'isomerases': {
        'examples': ['Glucose isomerase', 'Racemase'],
        'applications': 'HFCS production'
    }
}

# Enzyme kinetics
class EnzymeKinetics:
    @staticmethod
    def michaelis_menten(v, vmax, km, s):
        """
        Michaelis-Menten equation.
        v = (Vmax * [S]) / (Km + [S])
        """
        return vmax * s / (km + s)
    
    @staticmethod
    def lineweaver_burk(v, vmax, km, s):
        """
        Double reciprocal plot.
        1/v = (Km/Vmax)(1/[S]) + 1/Vmax
        """
        return 1/v if v != 0 else float('inf')
    
    @staticmethod
    def inhibition_types(km_app, vmax_app, i_type):
        """
        Enzyme inhibition.
        """
        if i_type == 'competitive':
            return {'km_increased': True, 'vmax_same': True}
        elif i_type == 'noncompetitive':
            return {'km_same': True, 'vmax_decreased': True}
        elif i_type == 'uncompetitive':
            return {'km_decreased': True, 'vmax_decreased': True}
```

### Enzyme Engineering

```python
# Directed evolution
directed_evolution = {
    'error_prone_pcr': 'Introduce random mutations',
    'dna_shuffling': 'Recombine related sequences',
    'saturation_mutagenesis': 'Target specific positions',
    'computational_design': 'AI/ML-guided design'
}

# Rational design
rational_design = {
    'structure_based': 'Use 3D structure',
    'sequence_based': 'Conserved regions',
    'machine_learning': 'Predict function'
}
```

-----

## Biosensors

### Biosensor Components

```python
# Biosensor structure
biosensor_components = {
    'biorecognition_element': {
        'types': [
            'Enzyme',
            'Antibody',
            'Nucleic acid',
            'Cell receptor',
            'Whole cell',
            'Tissue'
        ]
    },
    'transducer': {
        'electrochemical': ['Amperometric', 'Potentiometric', 'Conductometric'],
        'optical': ['Fluorescence', 'SPR', 'Colorimetric'],
        'mass_sensitive': ['QCM', 'SAW', 'Piezoelectric'],
        'thermal': ['Calorimetric']
    },
    'detector': 'Signal processing and display'
}

# Common biosensors
common_biosensors = {
    'glucose_monitor': {
        'element': 'Glucose oxidase',
        'transducer': 'Electrochemical',
        'market': 'Largest biosensor market'
    },
    'pregnancy_test': {
        'element': 'Anti-hCG antibody',
        'transducer': 'Colorimetric (Lateral flow)'
    },
    'PCR_detector': {
        'element': 'DNA probe',
        'transducer': 'Fluorescent'
    }
}
```

### Diagnostic Applications

```python
# Point-of-care diagnostics
poc_diagnostics = {
    'lateral_flow': {
        'examples': 'Pregnancy, COVID-19, HIV',
        'advantages': 'Simple, fast, no equipment'
    },
    'electrochemical': {
        'examples': 'Glucose, lactate',
        'advantages': 'Sensitive, miniaturizable'
    },
    'surface_plasmon_resonance': {
        'examples': 'SPR biosensors',
        'advantages': 'Label-free, real-time'
    }
}
```

-----

## Biopharmaceuticals

### Therapeutic Proteins

```python
# Biopharmaceutical types
biopharmaceuticals = {
    'antibodies': {
        'types': ['Full mAb', 'Fragment', 'Bispecific', 'ADC'],
        'examples': ['Humira', 'Remicade', 'Keytruda'],
        'expression': 'CHO cells'
    },
    'hormones': {
        'examples': ['Insulin', 'Growth hormone', 'EPO'],
        'expression': 'E. coli, CHO'
    },
    'enzymes': {
        'examples': ['tPA', 'Streptokinase', 'Asparaginase'],
        'applications': 'Thrombolysis, cancer'
    },
    'vaccines': {
        'subunit': 'Hepatitis B, VLP',
        'mRNA': 'COVID-19',
        'viral_vector': 'Ebola, COVID-19'
    }
}

# Manufacturing process
biopharma_process = {
    'upstream': ['Cell bank', 'Bioreactor', 'Harvest'],
    'downstream': [
        'Clarification',
        'Capture chromatography',
        'Viral inactivation',
        'Polishing',
        'Viral filtration',
        'Formulation'
    ],
    'fill_finish': ['Bulk fill', 'Filtration', 'Lyophilization', 'Packaging']
}
```

### Regulatory Considerations

```python
# FDA/EMA requirements
regulatory_requirements = {
    'IND': 'Investigational New Drug (FDA)',
    'BLA': 'Biologics License Application',
    'CMC': 'Chemistry, Manufacturing, Controls',
    'GMP': 'Good Manufacturing Practice',
    'validation': ['Process', 'Analytical', 'Cleaning']
}

# Biosimilarity
biosimilar_requirements = {
    'comparability': 'Structure and function',
    'nonclinical': 'Animal studies',
    'clinical': 'PK/PD, efficacy, safety',
    'immunogenicity': 'Antibody formation'
}
```

-----

## Applications and Industry

### Market Applications

```python
# Biotechnology applications
biotech_applications = {
    'healthcare': [
        'Therapeutic proteins',
        'Gene therapy',
        'Cell therapy',
        'Vaccines',
        'Diagnostics'
    ],
    'agriculture': [
        'GM crops',
        'Biofertilizers',
        'Biostimulants',
        'Animal health'
    ],
    'industrial': [
        'Enzymes',
        'Biofuels',
        'Biopolymers',
        'Bioremediation'
    ],
    'food': [
        'Fermented foods',
        'Food additives',
        'Nutraceuticals',
        'Preservatives'
    ]
}
```

### Sustainable Biotechnology

```python
# Bioeconomy
bioeconomy = {
    'biofuels': {
        'ethanol': 'Corn, sugarcane, cellulose',
        'biodiesel': 'Vegetable oils, algae',
        'biogas': 'Anaerobic digestion'
    },
    'bioplastics': {
        'PLA': 'Corn starch',
        'PHA': 'Bacterial fermentation'
    },
    'biorefinery': 'Complete utilization of biomass'
}
```

-----

## Common Errors to Avoid

- **Ignoring biosafety**: Follow biosafety level requirements
- **Not validating processes**: Process validation is critical
- **Assuming scalability**: Lab to production is challenging
- **Ignoring host cell proteins**: HCP removal important
- **Neglecting viral safety**: Viral clearance required
- **Not understanding glycosylation**: Affects function
- **Ignoring stability**: Formulation development essential
- **Underestimating purification**: Often bottleneck
- **Not considering regulatory**: FDA/EMA requirements
- **Forgetting raw materials**: Quality affects product