Colab notebooks for enhancing biological workflows

Teemi is a software tool designed to simplify the process of creating and running scientific workflows. By integrating literate programming into the workflow, the code is written in a way that is easy to understand, with detailed documentation included within the code itself. This approach enhances the reproducibility and transparency of the workflow, making it easier for others to understand, use, and build upon.

In the specific case of the study, teemi was used with literate programming to create a simulation-guided, iterative, and evolution-guided laboratory workflow for optimizing strictosidine production in yeast. By using this approach, the researchers were able to streamline the workflow, making it more efficient and effective at achieving the desired outcome.

The notebooks provided in the study are an excellent resource for others interested in applying teemi and literate programming to their own bioengineering workflows. By examining these notebooks, users can gain a better understanding of how to implement these tools into their own workflow and how to optimize the workflow for their specific application.

In summary, the study demonstrates the benefits of using teemi and literate programming in bioengineering workflows, providing an excellent starting point for those interested in optimizing their own workflows.

Strictosidine case : First DBTL cycle

DESIGN:

  1. Describes how we can automatically fetch homologs from NCBI from a query in a standardizable and repeatable way Notebook 00.

  2. Describes how promoters can be selected from RNAseq data and fetched from online database with various quality measurements implemented Notebook 01.

  3. Describes how a combinatorial library can be generated with the DesignAssembly class along with robot executable intructions Notebook 02.

BUILD:

  1. Describes the assembly of a CRISPR plasmid with USER cloning Notebook 03.

  2. Describes the construction of the background strain by K/O of G8H and CPR Notebook 04.

  3. Shows how the first combinatorial library was generated for 1280 possible combinations Notebook 05.

TEST:

  1. Describes data processing of LC-MS data and genotyping of the generated strains Notebook 06.

LEARN:

  1. Describes how we use AutoML to predict the best combinations for a targeted second round of library construction Notebook 07.

Strictosidine case : Second DBTL cycle

DESIGN:

  1. Shows how results from the ML can be translated into making a target library of strains Notebook 08.

BUILD:

  1. Shows the construction of a targeted library of strains Notebook 09.

TEST:

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

LEARN:

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

For more information head over to our publication describing the use of teemi in a literate programming context.