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.

Psylocibin is a compound most famously found in “magic mushrooms”. It has potent psychotropic properties, which aside from its notorious recreational uses, is also thought to hold potential for the treatment of a range of psychological and neurological ailments. In their study, Milne et al demonstrate that psylocibin could be produced from an engineered strain of the well-known yeast Saccharomyces cerevisiae. Furthermore, the yeast could produce psilocybin derivatives that may have new useful pharmaceutical properties.

• 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).

Aromatic amino acids are valuable chemicals and are precursors for a range of industrial compounds. This particular study looks at the widely used p-coumaric acid and aims to improve its production in yeast. Borja et al observed a significant effect that the carbon source had on the production, where xylose was a better substrate for p-coumaric acid production than glucose.

• 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.
  
  
• Babaei M., et al., (2020). Metabolic engineering of Saccharomyces cerevisiae for rosmarinic acid production. ACS Synth Biol. 9(8), 1978-1988.

Rosmarinic acid is a compound found in several plants. It is widely used as a food and cosmetic ingredient and has various pharmaceutical applications. However the production of this compound remains limited as natural availability is low and chemical synthesis is too complex. This study, for the first time, shows recombinant production of rosmarinic acid in engineered yeast.

• Sáez-Sáez J., et al., (2020). Engineering the oleaginous yeast Yarrowia lipolytica for high-level resveratrol production. Metab Eng. 62, 51-61.

Resveratrol is a plant secondary metabolite with a range of medicinal properties. Its low availability from the direct plant source has led researchers to develop microbial production of the compound, however commercially viable production levels are still proving difficult. In this study, the authors are demonstrating that the use of the yeast Yarrowia lipolytica as host organism is a promising host for the production of resveratrol along with several other valuable products.

• Otero-Muras, I., Carbonell, P., (2020). Automated engineering of synthetic metabolic pathways for efficient biomanufacturing. Metab Eng. [Online ahead of print]

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals. Significant advances in strain engineering are leading metabolic engineering to become a truly manufacturing technology capable of producing goods on an industrial scale. This review article demonstrates that the success of metabolic engineering heavily relies on biodesign algorithms which identify promising production routes and regulation strategies.

• Carbonell, P., et al., (2020). In silico design and automated learning to boost next-generation smart biomanufacturing. Synthetic Biology. 5(1).  

The increasing demand for bio-based compounds is now allowing biofoundries to produce and deliver valuable goods on an industrial scale. Nowadays, entire portfolios of producer strains are being developed in record times thanks to the integration and automation of the design, build, test and learn (DBTL) steps of the production cycle. As new in silico design tools are being developed to improve the efficacy of DBTL pipelines, the ever-increasing data gathered by biofoundries can now be added to these in-silico tools, therefore rendering them even more powerful and reliable. This paper discusses the future of biomanufacturing in an environment where the process will not only be fully automated, but will also be able to learn and adapt quickly to produce optimal designs.

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.