DD-DeCaF

Bioinformatics Services for Data-Driven Design of Cell Factories and Communities



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What is DD-DeCaF?

The European Commission has awarded 6.3 million Euros to a four-year collaborative project on data-driven design of cells and microbial communities for applications ranging from human health to sustainable production of chemicals. With advances in synthetic biology genomes can now be edited at unprecedented speed allowing making multiple changes in the same genome at the same time. This increases the need for computational tools to design cells and communities of cells analogous to the tools used in Computer Aided Design of cars, buildings and other man-made objects. In biotechnology these design tools need to be able to use existing large-scale databases to discover new parts and place them in the functioning context of the cell. The tools need to be easily accessible and provide an intuitive visual map of the cell to the biotechnologists working in the lab on building better cell factories and communities.

The project, called DD-DeCaF (Bioinformatics Services for Data-Driven Design of Cell Factories and Communities) brings together leading academic partners from five European universities with five innovative European companies to address the challenge of building a comprehensive design tool. The academic partners will develop cutting edge methods for using large scale data to design cell factories and communities for biotechnological applications. Three innovative Small/Medium Enterprise partners will convert these advanced methods to software tools that can be used by non-experts and to build intuitive visualizations of biological networks. These tools will be tested and applied to real world cell factory development projects by end-user partners.

News and Social

  • Using big “bio-data” to design better cell factories [Press release] (April 01, 2016)

    The EU has granted 6.3 million Euros to the project DD-DeCaF, coordinated by the Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark. The objective is to develop a computer tool that will allow biotech companies to design and engineer cell factories faster than is currently possible today. The tool will accelerate the production of sustainable bio-chemicals and lay the groundwork for design of healthier foodstuff.


  • DD-DeCaF Kickoff Meeting in Brussels (March 10, 2016)

    The official kick-off meeting of the DD-DeCaF project was held in Brussels on 7-8 March 2016 at creoDK and hosted by the project coordinator the Novo Nordisk Foundation Center for Biosustainability.


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Consortium

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Scientific partners

Industrial partners

Software Tools

tiltshift code

OptFlux is an open-source and modular software to support in silico metabolic engineering tasks aimed at being the reference computational application in the field.

Cameo is a high-level python library developed to aid the strain design process in metabolic engineering projects.

iPath2: interactive Pathways Explorer is a web-based tool for the visualization, analysis and customization of various pathways maps.

The Transport Reactions Annotation and Generation (Triage) tool identifies the metabolites transported by each transmembrane protein and its transporter family.

@Note is a Biomedical Text Mining platform that copes with major Information Retrieval and Information Extraction tasks and promotes multi-disciplinary research.

The Mass-Action Stoichiometric Simulation (MASS) toolbox is a modeling software package that focuses on the construction and analysis of kinetic and constraint-based models of biochemical reactions systems.

iTOL: interactive Tree Of Life is an online tool for the display, annotation and management of phylogenetic trees. Trees can be annotated with 14 different dataset types, and exported into various graphical formats.

The Metabolic Models Reconstruction Using Genome-Scale Information (merlin) tool is an user-friendly Java application that performs the reconstruction of genome-scale metabolic models for any organism that has its genome sequenced.