Programming Microbes

Zymergen Technology Team
Jun 25 · 8 min read
Figure 1. Zymergen leverages biology and experimental results to improve microbial chemical production.
Figure 2. Small scale experiments are used to predict large scale performance. Microbes that look promising at the microliter scale are tested in bench-top tanks. Particularly promising strains are tested at industrial scale.

Genome Optimization

Figure 3. Exploration of the space of possible genetic edits is intractable, so Zymergen uses biology and algorithms to navigate it more efficiently.

Our Workflow

Figure 4. Engineering microbes has many analogies to software engineering.

Our Software

Figure 5. Zymergen’s Data Science team uses Python for cleaning data, modeling, and designing new strains.

Biological Feature Extraction

Figure 6. Illustration of the most efficient route for converting food to the chemical of interest.
Figure 7. Flux Balance Analysis uses linear programming to determine reaction rates in a metabolic network.
>>> model = cobra.io.read_sbml_model('e_coli.sbml')>>> msg_reaction = model.reactions.get_by_id('msg_production')>>> model.objective = msg_production>>> model.optimize()>>> def summarize_fluxes(model):        ...        return pd.DataFrame(results)
Figure 9. We can use numerous objectives to obtain a variety of flux-derived features for machine learning.

Using Genome Optimization to Make Useful Molecules

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