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Related Concept Videos

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Bioreactor Design and Operational System01:29

Bioreactor Design and Operational System

Bioreactors are engineered vessels designed to cultivate microorganisms under controlled conditions for industrial bioprocessing. They maintain sterility and allow precise regulation of pH, temperature, oxygen, and nutrient levels to optimize microbial growth and metabolite production. Bioreactors range from small laboratory units of 1 liter to industrial systems holding up to 500,000 liters, though only about 75% of their volume is actively used for fermentation. The remaining headspace...
Designing Growth Media for Bioreactors01:30

Designing Growth Media for Bioreactors

Growth media provide essential nutrients that support cell growth and metabolism, thereby enhancing the yield of valuable products such as enzymes, antibiotics, and biomass. Designing an effective growth medium involves balancing all components to prevent nutrient limitations or toxic excesses, both of which can impair growth and reduce product yields.Composition of a Typical Growth MediumA typical growth medium contains carbon and nitrogen sources, salts, vitamins, trace elements, and...

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Updated: Jun 23, 2026

BioMEMS: Forging New Collaborations Between Biologists and Engineers
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teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.

Søren D Petersen1, Lucas Levassor1,2, Christine M Pedersen1

  • 1Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.

Plos Computational Biology
|March 8, 2024
PubMed
Summary

We developed teemi, an open-source platform for synthetic biology data management and analysis. It facilitates the design-build-test-learn cycle for engineering biological systems, including metabolic pathway design for medicinal compound production.

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Area of Science:

  • Synthetic biology
  • Computational biology
  • Bioengineering

Background:

  • Synthetic biology relies on data-driven engineering of biological systems.
  • Handling complex biological data under FAIR principles is crucial for efficient engineering.
  • Existing tools often lack user-friendly interfaces for simulation and data integration.

Purpose of the Study:

  • To develop an open-source, FAIR-compliant platform for computer-aided design and analysis in synthetic biology.
  • To enable user-friendly simulation, organization, and guidance for engineering biosystems.
  • To apply the platform for designing, simulating, and optimizing metabolic pathways.

Main Methods:

  • Development of teemi, a Python-based, literate programming platform hosted on GitHub.
  • Application of teemi for designing and simulating bioengineering workflows.
  • Integration and analysis of multivariate datasets using teemi.
  • Utilizing machine learning for predictive engineering of metabolic pathways.

Main Results:

  • Demonstrated the application of teemi in designing and simulating bioengineering tasks.
  • Successfully integrated and analyzed complex, multivariate datasets.
  • Applied machine learning for predictive engineering of metabolic pathways for alkaloid precursor production in yeast.
  • The teemi platform is publicly available and FAIR-compliant.

Conclusions:

  • The teemi platform provides a user-friendly, FAIR-compliant solution for synthetic biology data management and analysis.
  • It supports the iterative design-build-test-learn cycle, enhancing the engineering of biological systems.
  • teemi facilitates machine learning applications for predictive metabolic engineering, aiding in the production of valuable compounds.