Synthetic Biology

Contents

Synthetic Biology#

Tutorials for the omicverse.synbio module — a self-contained three-layer design stack that bridges metabolism, protein / enzyme engineering, and DNA.

A synthetic-biology project rarely lives in one layer. You reason about a pathway’s yield (metabolism), the enzymes that carry its flux (protein), and the genes you will actually build (DNA). ov.synbio puts all three in one place — and, crucially, wires them together. Its signature is the A↔B hinge: predict a turnover number from an enzyme’s sequence, push it into a genome-scale metabolic model as an enzyme-capacity constraint, and re-solve the attainable yield. Edit the enzyme → the metabolic network re-solves.

Layer

Functions

A — metabolic (COBRApy)

load_gem, fba, pfba, fva, single_gene_deletion, double_gene_deletion, strain_design, production_envelope, ec_model, apply_kcat

B — protein/enzyme (ESM / ProteinMPNN)

predict_structure, inverse_design, denovo_backbone, variant_effect, stability_ddg, enzyme_kcat, enzyme_function, protein_embed

C — DNA (DNAchisel / primer3)

codon_optimize, design_primers

The introductory notebook runs all three layers on real data — the e_coli_core genome-scale model, the 56-residue GB1 domain, and E. coli phosphofructokinase — and closes with the A↔B coupling.

Installation#

pip install 'omicverse[synbio]'

The metabolic and DNA layers are CPU-only (COBRApy with the free GLPK solver, DNAchisel, primer3). The protein layer uses a GPU automatically when available and reuses omicverse’s existing PyTorch dependency; ESM / ESMFold / ProteinMPNN weights download on first use to ~/.omicverse/synbio_weights (override with OMICOS_SYNBIO_WEIGHTS). Every backend is optional and gated behind an actionable error — import omicverse never requires any of them.