Multiomics tutorial

Guiding metabolic engineering via multiomics data and machine learning

We will show how the combination of tools presented here (ICE + EDD + ART + Jupyter notebooks) provide a standardized manner to store data so it can be leveraged to produce actionable recommendations and guide synthetic biology.

ICE is an open source repository platform for managing information about DNA parts and plasmids, proteins, microbial host strains, and plant seeds. EDD is an open source online repository of experimental data and metadata. ART is a library that leverages machine learning for synthetic biology purposes, providing predictive models and recommendations for the next set of experiments.

When combined, this set of tools can effectively store, visualize, and leverage synthetic biology data to enable predictive bioengineering and effective actionable items for the next DBTL cycle.

You can find more information in this paper.

We start with a base strain (wild type, or WT) that is bioengineered to produce isoprenol (3-methyl-3-buten-1-ol ). This strain is bioengineered according to several designs (i.e.: knockout malate dehydrogenase, overexpress citrate synthase) suggested by ART. The results are 95 bioengineered strains (BE1,BE2… etc) for which experimental data (isoprenol production levels) are simulated through the Omics Mock Generator library (OMG) and stored in EDD and ICE. These data are then leveraged by ART to recommend, using machine learning, new designs that are expected to improve isoprenol production (REC1, REC2, …). These recommendations and production predictions are compared with the ground truth provided by OMG.

Each of these steps (in orange) is demonstrated through screencasts and Jupyter notebooks, as shown in the table below.

Step by step tutorial

This table provides a guide for all steps in the simulated engineering process described in the figure above.

The process is fully showcased through a combination of Jupyter notebooks and screencasts which can be found below under each step number

Running the full tutorial requires you to download the following libraries from github:

  1. ART

  2. OMG

  3. Multiomics

You can download these libraries in a JupyterHub server provided by JBEI, ABF or your own computer.

Step 1

Importing the WT strain information into ICE (the public ABF repository).


No files needed for this tutorial.



Screencast1_ICEsingleImport.mp4

Step 2

Creating the omics data for the WT strain by leveraring the OMG library.


This tutorial requires:

  1. A Jupyter notebook.

  2. Installing the OMG library from github.

  3. Downloading the multiomics files from github.


Screencast2_EDDimport.mp4

Step 4

Visualizing data through EDD



No files needed for this tutorial.

Screencast3_EDDvisualization.mp4

Step 5

Generating initial design suggestions through ART. These are the modifications of the original strain from step 1.


This tutorial requires:

  1. A jupyter notebook

  2. Installing the ART library from github (academic and commercial licenses are available upon request).


Step 6

Bulk ICE import of engineered strains.


You will need these files:

  1. Strain information file


Screencast4 _ICEmultipleImport.mp4

Step 7

ICE export of bioengineered strains.



No files needed for this tutorial.


Screencast5_ICEexport.mp4

Step 8

Generating isoprenol production data for the bioengineered strains.



This tutorial requires:

  1. A Jupyter notebook.

  2. Installing the OMG library from github.

  3. Downloading the multiomics files from github.


Step 9

Importing into EDD of bioengineered strains production data (including quick visualization).


You will need these files:

  1. Experiment Description file

  2. Isoprenol production file

Screencast6_BEupload.mp4

Step 10

Export of isoprenol production data for bioengineered strains from EDD.



This tutorial requires:

  1. A Jupyter notebook.



Screencast7_EDDexport.mp4

Step 11

Training ART and producing recommendations.


This tutorial requires:

  1. A Jupyter notebook.

  2. Installing the ART library from github (academic and commercial licenses are available upon request).


Screencast8_ARTrecommendations.mp4

Step 12

Using the ART frontend.


This step requires:

  1. Limonene ART input file.

  2. If you don't have an @lbl.gov email, sign up and create an account at art.agilebiofoundry.org

Screencast9_ARTfrontend.mp4

Step 13

Comparing ART predictions with ground truth.



This tutorial requires:

  1. A Jupyter notebook.

  2. Downloading the multiomics files from github.


For more information, please email ese-robotics@lbl.gov