Public deliverables will be made available below as soon as they are complete:

WP1: Project Management - led by SilicoLife

•  D1.4: Final project report to the European Commission - due MONTH 48
  

WP2: Retrosynthesis & Enumeration - led by SilicoLife

•  D2.4: Pathway database with all ranking and analysis metrics - due MONTH 36
  

WP3: Chemical Synthesis of New Products - led by GalChimia

•  No public deliverables
  

WP4: Gene Discovery & Protein Engineering - led by Universidade NOVA de Lisboa

•  No public deliverable

WP5: In Silico Metabolic Engineering - led by EMBL

• D.5.5: Public Final version of the updated models, due MONTH 42
  

WP6: Pathway Screening - led by University of Manchester

•  No public deliverables
  

WP7: Cell Factory Construction - led by DTU

•  No public deliverables

WP8: Product Validation & Sustainability Appraisal - led by DSM

•  D8.5: Sustainability appraisal and risk assessment of 10 target molecules, due MONTH 48
  

WP9: Exploitation and Dissemination - led by NNFCC

• D9.2: Project visuals and messaging - COMPLETED MONTH 6

• D9.3: Communication Channels (Website, Twitter, LinkedIn) - COMPLETED MONTH 6

Below is a collection of resources used to promote and disseminate information associated with the ShikiFactory100 project. 

Project Leaflet - contains general information about the project and describes its three major vectors (discovery, design & implementation, and validation).

Roll-up Banner - for project promotion at events such as conferences and trade fairs. 

Project Articles

The Shikimate Pathway - this article aims to provide background information about the research carried out by the project. It describes key metabolic pathways for living organisms, including the shikimate pathway, central to the Shikifactory100 project, and their applications at an industrial level.

Synthetic Biology for the Production of Biobased Molecules - this article aims to provide a background to the field of synthetic biology.  

• Robinson, C. J., et al., (2020). Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers. Metabolic Engineering. 60, 168-182.

• Milne, N., et al., (2020). Metabolic engineering of Saccharomyces cerevisiae for the de novo production of psilocybin and related tryptamine derivatives. Metabolic Engineering. 60, 25-36.

• Vieira, V., Rocha, M., (2019). CoBAMP: a Python framework for metabolic pathway analysis in constraint-based models. Bioinformatics. 35(24), 5361–5362. 

• Grozinger, L., et al., (2019). Pathways to cellular supremacy in biocomputing. Nature Communications. 10(5250).

• Borja, G. M., et al., (2019). Metabolic engineering and transcriptomic analysis of Saccharomyces cerevisiae producing p-coumaric acid from xylose. Microbial Cell Factories. 18(191).

• Del Carratore, F., et al., (2019). Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships. Analytical Chemistry. 91(20), 12799–12807. 

• Currin, A., et al., (2019). Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries. Synthetic Biology. 4(1). 

• Cruz F., et al., (2020) Towards the Reconstruction of Integrated Genome-Scale Models of Metabolism and Gene Expression. In: Fdez-Riverola F., Rocha M., Mohamad M., Zaki N., Castellanos-Garzón J. (eds) Practical Applications of Computational Biology and Bioinformatics, 13th International Conference. PACBB 2019. Advances in Intelligent Systems and Computing, vol 1005. Springer, Cham. 

• Correia, J., et al., (2019). Artificial Intelligence in Biological Activity Prediction.  In: Fdez-Riverola F., Rocha M., Mohamad M., Zaki N., Castellanos-Garzón J. (eds) Practical Applications of Computational Biology and Bioinformatics, 13th International Conference. PACBB 2019. Advances in Intelligent Systems and Computing, vol 1005. Springer, Cham. 

• Vieira, V., et al., (2019). Comparison of pathway analysis and constraint-based methods for cell factory design. BMC Bioinformatics. 20(350). 

• Carbonell, P., et al., (2019). Efficient learning in metabolic pathway designs through optimal assembling. IFAC PapersOnLine. 52(26), 7-12.

Scientific results from the ShikiFactory100 project will also be made available via CORDIS (Community Research and Development Information Service, the European Commission's public repository for the dissemination of information from all EU-funded research projects.

Results are to be stored and shared between partners via the ICE and GitLab platforms.