EMMA-CAD
EMMA-CAD enables automated design and optimization of customized mammalian expression vectors for modular synthetic biology applications.
Key Features:
- Automated Connector Selection: Uses advanced computer optimization algorithms to select connectors and precisely integrate DNA components during vector assembly.
- Customizable Part Libraries: Allows tailoring of part libraries to match specific experimental requirements for expression vector construction.
- Personalized Design Spaces: Supports creation of personalized design spaces to configure unique vector architectures.
- Protocol Generation: Translates vector assembly designs into human- and machine-readable protocols suitable for automated workflows.
- Integration with EMMA: Integrates with the Extensible Modular Mammalian Assembly (EMMA) automated pipeline for end-to-end vector construction.
- Modular Expression Circuit Support: Supports modular design and assembly of DNA expression circuits for mammalian systems.
Scientific Applications:
- Modular DNA expression circuit design: Enables construction of modular expression circuits in mammalian systems using defined DNA parts and connectors.
- High-throughput vector construction: Accelerates rapid and precise design of mammalian expression vectors for high-throughput synthetic biology investigations.
- Automated assembly workflows: Facilitates translation of designs into automated production by providing machine-readable protocols and EMMA pipeline integration.
Methodology:
Automated connector selection using computer optimization algorithms; generation of human- and machine-readable assembly protocols; integration into the Extensible Modular Mammalian Assembly (EMMA) automated pipeline.
Topics
Details
- Cost:
- Free of charge (with restrictions)
- Tool Type:
- web application, workflow
- Operating Systems:
- Mac, Linux, Windows
- Added:
- 6/13/2022
- Last Updated:
- 6/13/2022
Operations
Publications
Luo Y, James JS, Jones S, Martella A, Cai Y. EMMA-CAD: Design Automation for Synthetic Mammalian Constructs. ACS Synthetic Biology. 2022;11(2):579-586. doi:10.1021/acssynbio.1c00433. PMID:35050610.
PMID: 35050610
Funding: - Biotechnology and Biological Sciences Research Council: BB/P02114X/1
- Volkswagen Foundation: R123021
- Shenzhen Key Laboratory of Synthetic Genomics: ZDSYS201802061806209
- Guangdong Provincial Key Laboratory of Synthetic Genomics: 2019B030301006
- Shenzhen Science and Technology Program: KQTD20180413181837372