Package: teemi
teemi: a python package designed to make high-throughput strain construction reproducible and FAIRWhat 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 all 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 ourGoogle 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.Curious about how you can build strains easier and faster with teemi?
Head over to ourGoogle Colab notebooksand give it a try.For a quick introduction, check our quick guides:A Quick Guide to Creating a Combinatorial LibraryA Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)Our pre-print“Literate programming for iterative design-build-test-learn cycles in bioengineering”is out now.
Please cite it if you’ve used teemi in a scientific publication.OverviewFeaturesGetting startedA Quick Guide to Creating a Combinatorial LibraryA Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)Colab notebooksStrictosidine case : First DBTL cycleStrictosidine case : Second DBTL cycleInstallationDocumentation and ExamplesContributionsLicenseCreditsFeaturesCombinatorial library generationHT cloning and transformation workflowsFlowbot One instructionsCSV-based LIMS system as well as integration to BenchlingGenotyping of microbial strainsAdvanced Machine Learning of biological datasets with the AutoMLH2OWorkflows for selecting enzyme homologsPromoter selection workflows from RNA-seq datasetsData analysis of large LC-MS datasets along with workflows for analysisGetting startedTo get started with making microbial strains in an HT manner please follow the steps below:Install teemi. You will find the necessary information below for installation.Check out ournotebooksfor inspiration to make HT strain construction with teemi.You can start making your own workflows by importing teemi into either Google colab or Jupyter lab/notebooks.A Quick Guide to Creating a Combinatorial LibraryThis guide provides a simple example of the power and ease of use of the teemi tool.
Let’s take the example of creating a basic combinatorial library with the following design considerations:Four promotersTen enzyme homologsA Kozak sequence integrated into the primersOur goal is to assemble a library of promoters and enzymes into a genome via in vivo assembly.
We already have a CRISPR plasmid; all we need to do is amplify the promoters and enzymes for the transformation.
This requires generating primers and making PCRs. We’ll use teemi for this process.To begin, we load the genetic parts using Teemi’s easy-to-use functionread_genbank_files(), specifying the path to the genetic parts.fromteemi.design.fetch_sequencesimportread_genbank_filespath='../data/genetic_parts/G8H_CYP_CPR_PARTS/'pCPR_sites=read_genbank_files(path+'CPR_promoters.gb')CPR_sites=read_genbank_files(path+'CPR_tCYC1.gb')We have four promoters and ten CPR homologs (all with integrated terminators).
We want to convert them intopydna.Dseqrecordobjects from their current form asBio.Seqrecord. We can do it this way:frompydna.dseqrecordimportDseqrecordpCPR_sites=[Dseqrecord(seq)forseqinpCPR_sites]CPR_sites=[Dseqrecord(seq)forseqinCPR_sites]Next, we add these genetic parts to a list in the configuration we desire, with the promoters upstream of the enzyme homologs.list_of_seqs=[pCPR_sites,CPR_sites]If we want to integrate a sgRNA site into the primers, we can do that. In this case, we want to integrate a Kozak sequence.
We can initialize it as shown below.kozak=[Dseqrecord('TCGGTC')]Now we’re ready to create a combinatorial library of our 4x10 combinations. We can import the Teemi class for this.fromteemi.design.combinatorial_designimportDesignAssemblyWe initialize with the sequences, the pad (where we want the pad - in this case, between the promoters and CPRs), then select the overlap and the desired temperature for the primers.
Note that you can use your own primer calculator. Teemi has a function that can calculate primer Tm using NEB, for example, but for simplicity, we’ll use the default calculator here.CPR_combinatorial_library=DesignAssembly(list_of_seqs,pad=kozak,position_of_pads=[1],overlap=35,target_tm=55)Now, we can retrieve the library.CPR_combinatorial_library.primer_list_to_dataframe()idanneals tosequenceannealing temperaturelengthprice(DKK)descriptionfootprintlen_footprintP001pMLS1…56.112036.0Anneals to pMLS1…20P002pMLS1…56.184988.2Anneals to pMLS1, overlaps to 2349bp_PCR_prod…28………………………The result of this operation is a pandas DataFrame which will look similar to the given example (note that the actual DataFrame have more rows).To obtain a DataFrame detailing the steps required for each PCR, we can use the following:CPR_combinatorial_library.pcr_list_to_dataframe()pcr_numbertemplateforward_primerreverse_primerf_tmr_tmPCR1pMLS1P001P00256.1156.18PCR2AhuCPR_tCYC1P003P00453.0453.50PCR3pMLS1P001P00556.1156.18………………The output is a pandas DataFrame. This is a simplified version and the actual DataFrame can have more rows.Teemi has many more functionalities. For instance, we can easily view the different combinations in our library.CPR_combinatorial_library.show_variants_lib_df()01Systematic_nameVariantpMLS1AhuCPR_tCYC1(1, 1)0pMLS1AanCPR_tCYC1(1, 2)1pMLS1CloCPR_tCYC1(1, 3)2…………This command results in a pandas DataFrame, showing the combinations in the library. This is a simplified version and the actual DataFrame would have 40 rows for this example.The next step is to head to the lab and build some strains. Luckily, we have many examples demonstrating how to do this for a large number of strains and a bigger library (1280 combinations).
Please refer to ourColab notebooksbelow where we look at optimizing strictosidine production in yeast with Teemi.A Quick Guide to making a CRISPR plasmid with USER cloning (for the beginner)Here is a quick guide on how we simulate the assembly of a CRISPR plasmid with USER cloning.
Big thanks toBjörn Johanssonfor the initial work with pydna that makes much of this possible.
Please check outpydnahere.Let’s begin with the simple workflow:frompydna.primerimportPrimerfrompydna.dseqrecordimportDseqrecordStep 1: Getting the fragments we want to integrate into our CRISPR plasmid.
Specifically, we aim to integrate sgRNAs to knock out two targets.# 1.1: Define the primersU_pSNR52_Fw_1=Primer('CGTGCGAUTCTTTGAAAAGATAATGTATGA')TJOS_66_P2R=Primer('ACCTGCACUTAACTAATTACATGACTCGA')U_pSNR52_Fw_2=Primer('AGTGCAGGUTCTTTGAAAAGATAATGTATGA')TJOS_65_P1R=Primer('CACGCGAUTAACTAATTACATGACTCGA')Primers are short, single-stranded DNA sequences that are necessary for targeting the specific DNA region we want to amplify using PCR.1.2: Get the gRNA template. We retrieve the gRNA template from plate we have in the lab with the following teemi function.
The gRNA template is the DNA sequence that encodes the guide RNA. This RNA molecule guides the Cas9 protein to the target DNA sequence, where it induces a cut.fromteemi.lims.csv_databaseimportget_dna_from_box_namegRNA1_template=get_dna_from_plate_name('gRNA1_template (1).fasta','plasmid_plates',database_path="G8H_CPR_library/data/06-lims/csv_database/")1.3: Perform a PCR to amplify the gRNA.
PCR (Polymerase Chain Reaction) is a technique used to amplify a specific DNA sequence. Here, we’re amplifying our gRNA templates.frompydna.amplifyimportpcrgRNA1_pcr_prod=pcr(U_pSNR52_Fw_1,TJOS_66_P2R,gRNA1_template)gRNA2_pcr_prod=pcr(U_pSNR52_Fw_2,TJOS_65_P1R,gRNA2_template)1.4: Use the USER enzyme to process the PCR products.
The USER enzyme is used to create single-stranded overhangs on the PCR products, which will facilitate their insertion into the plasmid.fromteemi.design.cloningimportUSER_enzymegRNA1_pcr_USER=USER_enzyme(gRNA1_pcr_prod)gRNA2_pcr_USER=USER_enzyme(gRNA2_pcr_prod)print(gRNA1_pcr_USER)print(gRNA2_pcr_USER)Output:Dseq(-425)
        TCTT..GTTAAGTGCAGGT
GCACGCTAAGAA..CAAT

Dseq(-425)
         TCTT..GTTAATCGCGTG
TCACGTCCAAGAA..CAATStep 2: Digesting the plasmid. The plasmid is a small, circular DNA molecule. We’re importing a specific template that we’ll use to integrate our gRNAs.# 2.1: Import the plasmidvector=Dseqrecord(get_dna_from_plate_name('Backbone_template - p0056_(pESC-LEU-ccdB-USER) (1).fasta','plasmid_plates',database_path="G8H_CPR_library/data/06-lims/csv_database/"),circular=True)2.2: Digest the plasmid with AsiSI enzyme.
Digestion with the AsiSI enzyme creates specific cuts in the plasmid, allowing us to insert our gRNAs at these locations.fromBio.RestrictionimportAsiSIvector_asiSI,cCCDB=sorted(vector.cut(AsiSI),reverse=True)print(vector_asiSI.seq)Output:Dseq(-6972)
  CGCG..TGCGAT
TAGCGC..ACGC2.3: Nick the digested plasmid using a nicking enzymefromteemi.design.cloningimportnicking_enzymevector_asiSI_nick=Dseqrecord(nicking_enzyme(vector_asiSI))vector_asiSI_nick.seqNicking enzymes create single-stranded breaks in the DNA. This step prepares the plasmid for the insertion of the gRNAs.Output:Dseq(-6972)
        CATT..AATGCGTGCGAT
TAGCGCACGTAA..TTACStep 3: Assembling sgRNAs and vector# 3.1: Combine the nicked vector with the USER processed gRNAs and loop the resulting sequencerec_vec=(vector_asiSI_nick+gRNA1_pcr_USER+gRNA2_pcr_USER).looped()rec_vec.seqIn this final step, we’re assembling the plasmid by combining the nicked vector with the processed gRNAs. The resulting molecule is a circular DNA plasmid containing our gRNAs.Output:Dseq(o7797)
CATT..CGTG
GTAA..GCACFor more real-life examples on how to use this in complex metabolic worklfows in a high-throughput manner pleas check ourColab notebooks.Colab notebooksAs 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 first study we present how teemi and literate programming to build simulation-guided, iterative,
and evolution-guided laboratory workflows for optimizing strictosidine production in yeast.
If you wanna read the study you can find the pre-printhere.Below you can find all the notebooks developed in this work.
Just click the Google colab badge to start the workflows.Strictosidine case : First DBTL cycleThe strictosidine pathway and short intro:Strictosidine is a crucial precursor for 3,000+ bioactive alkaloids found
in plants, used in medical treatments like cancer and malaria.
Chemically synthesizing or extracting them is challenging.
We’re exploring biotechnological methods to produce them in yeast cell factories.
But complex P450-mediated hydroxylations limit production.
We’re optimizing these reactions using combinatorial optimization, starting with geraniol hydroxylation(G8H) as a test case.
Feal free to check out the notebooks for more information on how we did it.DESIGN:Automatically fetch homologs from NCBI from a query in a standardizable and repeatable wayPromoters can be selected from RNAseq data and fetched from online database with various quality measurements implementedCombinatorial libraries can be generated with the DesignAssembly class along with robot executable intructionsBUILD:Assembly of a CRISPR plasmid with USER cloningConstruction of the background strain by K/O of G8H and CPRFirst combinatorial library was generated for 1280 possible combinationsTEST:Data processing of LC-MS data and genotyping of the generated strainsLEARN:Use AutoML to predict the best combinations for a targeted second round of library constructionStrictosidine case : Second DBTL cycleDESIGN:Results from the ML can be translated into making a targeted library of strainsBUILD:Shows the construction of a targeted library of strainsTEST:Data processing of LC-MS data like in notebook 6LEARN:Second ML cycle of ML showing how the model increased performance and saturation of best performing strainsInstallationUse pip to install teemi fromPyPI.$ pip install teemiIf you want to develop or if you cloned the repository from ourGitHubyou can install teemi in the following way.$ pip install -e <path-to-teemi-repo>Or if you are in the teemi repository:$ pip install -e .For those who want to contribute or develop further, you can install the development version with:$ pip install -e .[dev]Or directly from PyPI:$ pip install teemi[dev]You might need to run these commands with administrative
privileges if you’re not using a virtual environment (usingsudofor example).
Please check thedocumentationfor further details.Documentation and ExamplesDocumentation 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 hereteemi.notebooks.Documentation:https://teemi.readthedocs.ioContributionsContributions are very welcome! Check ourguidelinesfor instructions how to contribute.LicenseFree software: MIT licenseCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.teemis logo was made by Jonas Krogh Fischer. Check out hiswebsite.History0.3.3 (2023-11-08)Updated readme file with extra examplesFixed setup.py file for installation of development packages like: pip install teemi[dev]0.3.2 (2023-01-08)This release features a re-factored DesignAssembly class with:Simplified methods i.e. redundant methods have been removed.The ability to add more than one pad, which can be used to make constructs with overlapping ends for for plasmid cloning.0.3.1 (2023-31-07)
- This release failed due to a bug in the readme file.0.3.0 (2023-22-06)New submodules: gibson_cloningThis module is used to perform simple Gibson cloning workflows.
While the addition of the “gibson_cloning” submodule is an exciting development, this module is still a work in progress.
Next, a golden gate module. Keep posted on the progress.0.2.0 (2023-31-05)New submodules: CRISPRsequencecutter, sequence_finder.CRISPRSequenceCutter is a dataclass that is used to cut DNA through CRISPR-cas9 double-stranded break.
SequenceFinder is a dataclass that finds upstream and downstream sequences from a sequence input, annotates them and saves them.0.1.0 (2023-01-02)First release on PyPI.