teemi 0.1.1

A Python package for constructing microbial strains

teemi: Literate programming can streamline bioengineering workflows

What is teemi?

teemi, named after the Greek goddess of fairness, is a python package designed to make microbial strain construction reproducible and FAIR (Findable, Accessible, Interoperable, and Reusable). With teemi, you can simulate the steps of a strain construction cycle, from generating genetic parts to designing a combinatorial library and keeping track of samples through a commercial Benchling API and a low-level CSV file database. This tool can be used in literate programming to increase efficiency and speed in metabolic engineering tasks. To try teemi, visit our Google Colab notebooks.

teemi not only simplifies the strain construction process but also offers the flexibility to adapt to different experimental workflows through its open-source Python platform. This allows for efficient automation of repetitive tasks and a faster pace in metabolic engineering.

Our demonstration of teemi in a complex machine learning-guided metabolic engineering task showcases its efficiency and speed by debottlenecking a crucial step in the strictosidine pathway. This highlights the versatility and usefulness of this tool in various biological applications. To see teemi in action, check out our Google Colab notebooks.

Curious about how you can build strains easier and faster? Head over to our Google Colab notebooks and give it a try.

Please cite our paper (link tba) if you’ve used teemi in a scientific publication.

Features

• Combinatorial library generation

• HT cloning and transformation workflows

• Flowbot One instructions

• CSV-based LIMS system as well as integration to Benchling

• Genotyping of microbial strains

• Advanced Machine Learning of biological datasets with the AutoML H2O

• Workflows for selecting enzyme homologs

• Promoter selection workflows from RNA-seq datasets

• Data analysis of large LC-MS datasets along with workflows for analysis

Getting started

To get started with making microbial strains in an HT manner please follow the steps below:

1. Install teemi. You will find the necessary information below for installation.

2. Check out our notebooks for inspiration to make HT strain construction with teemi.

3. You can start making your own workflows by importing teemi into either Google colab or Jupyter lab/notebooks.

Colab notebooks

As a proof of concept we show how teemi and literate programming can be used to streamline bioengineering workflows. These workflows should serve as a guide or a help to build your own workflows and thereby harnessing the power of literate programming with teemi.

Specifically, in this study we present how teemi and literate programming to build simulation-guided, iterative, and evolution-guided laboratory workflows for optimizing strictosidine production in yeast.

Below you can find all the notebooks developed in this work. Just click the Google colab badge to start the workflows.

First DBTL cycle

DESIGN:

0. Describes how we can automatically fetch homologs from NCBI from a query in a standardizable and repeatable way .

1. Describes how promoters can be selected from RNAseq data and fetched from online database with various quality measurements implemented .

2. Describes how a combinatorial library can be generated with the DesignAssembly class along with robot executable intructions .

BUILD:

3. Describes the assembly of a CRISPR plasmid with USER cloning .

4. Describes the construction of the background strain by K/O of G8H and CPR .

5. Shows how the first combinatorial library was generated for 1280 possible combinations .

TEST:

6. Describes data processing of LC-MS data and genotyping of the generated strains .

LEARN:

7. Describes how we use AutoML to predict the best combinations for a targeted second round of library construction .

Second DBTL cycle

DESIGN:

8. Shows how results from the ML can be translated into making a target library of strains .

BUILD:

9. Shows the construction of a targeted library of strains .

TEST:

10. Describes the data processing of LC-MS data like in notebook 7 .

LEARN:

11. Second ML cycle of ML showing how the model increased performance and saturation of best performing strains .

Installation

Use pip to install teemi from PyPI.

$ pip install teemi

If you want to develop or if you cloned the repository from our GitHub you can install teemi in the following way.

$ pip install -e <path-to-teemi-repo>

You might need to run these commands with administrative privileges if you’re not using a virtual environment (using sudo for example). Please check the documentation for further details.

Documentation and Examples

Documentation is available on through numerous Google Colab notebooks with examples on how to use teemi and how we use these notebooks for strain construnction. The Colab notebooks can be found here teemi.notebooks.

• Documentation: https://teemi.readthedocs.io.

Contributions

Contributions are very welcome! Check our guidelines for instructions how to contribute.

License

• Free software: MIT license

Credits

• This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

• teemis logo was made by Jonas Krogh Fischer. Check out his website.

History

0.1.0 (2023-01-02)

• First release on PyPI.