Easy strain design using a high-level interface

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Users primarily interested in using cameo as a tool for enumerating metabolic engineering strategies have access to cameo’s advanced programming interface via cameo.api that provides access to potential products (cameo.api.products), host organisms (cameo.api.hosts) and a configurable design function (cameo.api.design). Running cameo.api.design requires only minimal input and will run the following workflow.

Import the advanced interface.

from cameo import api

Searching for products

Search by trivial name.

api.products.search('caffeine')

	InChI	SMILES	charge	formula	mass	name	source	search_rank
MNXM680	InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)...	CN1C=NC2=C1C(=O)N(C)C(=O)N2C	0	C8H10N4O2	194.1906	caffeine	chebi:27732	0

Search by ChEBI ID.

api.products.search('chebi:27732')

	InChI	SMILES	charge	formula	mass	name	source	search_rank
MNXM680	InChI=1S/C8H10N4O2/c1-10-4-9-6-5(10)7(13)12(3)...	CN1C=NC2=C1C(=O)N(C)C(=O)N2C	0	C8H10N4O2	194.1906	caffeine	chebi:27732	0

Host organisms

Currently the following host organisms and respective models are available in cameo. More hosts and models will be added in the future (please get in touch with us if you’d like to get a particular host organism included).

for host in api.hosts:
    for model in host.models:
        print(host.name, model.id)

Escherichia coli iJO1366
Saccharomyces cerevisiae iMM904

Computing strain engineering strategies

For demonstration purposes, we’ll set a few options to limit the computational time. Also we’ll create a multiprocessing view to take advantage of multicore CPUs (strain design algorithms will be run in parallel for individually predicted heterologous pathways).

from cameo.parallel import MultiprocessingView
mp_view = MultiprocessingView()

Limit the number of predicted heterlogous pathways to 4.

api.design.options.max_pathway_predictions = 4

Set a time limit of 30 minutes on individual heuristic optimizations.

api.design.options.heuristic_optimization_timeout = 30

report = api.design(product='vanillin', view=mp_view)

Starting searching for compound vanillinFound 1 compounds that match query 'vanillin'

Id	Name	Formula
MNXM754	vanillin	C8H8O3

Choosing best match (vanillin) ... please interrupt if this is not the desired compound.

Predicting pathways for product vanillin in Escherichia coli (using model iJO1366).Pathway 1

	equation	lower_bound	upper_bound
MNXR5340	H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih...	-1000	1000
MNXR5336	2.0 H(+) + NADH + vanillate <=> H2O + vanillin...	-1000	1000
MNXR2795	S-adenosyl-L-methionine + glycine <=> H(+) + S...	-1000	1000
MNXR68718	H2O + 3,4-dihydroxybenzoate <=> 3-dehydroshiki...	-1000	1000

Max flux: 4.29196

Pathway 2

	equation	lower_bound	upper_bound
MNXR5340	H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih...	-1000	1000
MNXR5336	2.0 H(+) + NADH + vanillate <=> H2O + vanillin...	-1000	1000
MNXR84169	(6R)-5,10-methylenetetrahydrofolate + glycine ...	-1000	1000
MNXR68718	H2O + 3,4-dihydroxybenzoate <=> 3-dehydroshiki...	-1000	1000

Max flux: 7.28363

Pathway 3

	equation	lower_bound	upper_bound
MNXR5340	H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih...	-1000	1000
MNXR5336	2.0 H(+) + NADH + vanillate <=> H2O + vanillin...	-1000	1000
MNXR68718	H2O + 3,4-dihydroxybenzoate <=> 3-dehydroshiki...	-1000	1000
MNXR651	2.0 H(+) + NADH + formate <=> H2O + formaldehy...	-1000	1000

Max flux: 7.58479

Pathway 4

	equation	lower_bound	upper_bound
MNXR5340	H(+) + NADH + O2 + vanillate <=> H2O + 3,4-dih...	-1000	1000
MNXR5336	2.0 H(+) + NADH + vanillate <=> H2O + vanillin...	-1000	1000
MNXR230	H(+) + 4-hydroxybenzoate + O2 + NADPH <=> H2O ...	-1000	1000
MNXR640	methanol + NAD(+) <=> H(+) + NADH + formaldehyde	-1000	1000

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