High-level interface for users

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.

If you’re running this notebook on try.cameo.bio, things might run very slow due to our inability to provide access to the CPLEX solver on a public webserver. Furthermore, Jupyter kernels might crash and restart due to memory limitations on the server.

from cameo import api
report = api.design(product='vanillin')
Starting searching for compound vanillinFound 2 compounds that match query 'vanillin'
Id Name Formula
MNXM754vanillinC8H8O3
MNXM741anilineC6H7N
Choosing best match (vanillin) ... please interrupt if this is not the desired compound.

CH 3 O HO O - OBDepict

Predicting pathways for product vanillin in Escherichia coli (using model iJO1366).
<IPython.core.display.Javascript object>
Predicting pathways for product vanillin in Saccharomyces cerevisiae (using model iMM904).
<IPython.core.display.Javascript object>
Optimizing 8 pathways
Starting optimization at Tue, 24 May 2016 12:00:41
Finished after 00:02:56
Starting optimization at Tue, 24 May 2016 12:04:07
Finished after 00:03:40
Starting optimization at Tue, 24 May 2016 12:08:26
Finished after 00:04:09
Starting optimization at Tue, 24 May 2016 12:12:53
Finished after 00:01:57
Starting optimization at Tue, 24 May 2016 12:15:11
Finished after 00:01:27
Starting optimization at Tue, 24 May 2016 12:16:49
Finished after 00:01:22
Starting optimization at Tue, 24 May 2016 12:18:22
Finished after 00:01:09
Starting optimization at Tue, 24 May 2016 12:19:40
Finished after 00:02:31
report
[(<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122f12400>,
  +MNXR5336+MNXR5340+MNXR7229+MNXR68718),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122e54c50>,
  +MNXR230+MNXR640+MNXR5336+MNXR5340),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122f79358>,
  +MNXR640+MNXR5336+MNXR5340+MNXR68718),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122ee6278>,
  +MNXR5336+MNXR5340+MNXR7734+MNXR68718),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x123025da0>,
  +MNXR5336+MNXR5340+MNXR68718),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122fe90b8>,
  +MNXR5336+MNXR5340+MNXR14769),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122de4cf8>,
  +MNXR230+MNXR5336+MNXR5340),
 (<cameo.strain_design.heuristic.evolutionary_based.OptGeneResult at 0x122cb6518>,
  +MNXR5336+MNXR5340+MNXR5836+MNXR7067)]

IPython notebook

Click here to download this page as an IPython notebook.