DigiSal

DigiSal

Digital Salmon – from a reactive to a pre-emptive research strategy in aquaculture

Project overview

Project lead: Jon Olav Vik
Institution: NMBU
Partners: NTNU, UiT, Stirling, Wageningen, Institute of Marine Research, AquaGen, EWOS
Funding: NOK 38.8 mill.
Duration: 2016–2019

Publications

 

  • Øyås, Ove; Kobel, Carl Mathias; Vik, Jon Olav & Pope, Phillip B. (2024). Predicting microbial genome-scale metabolic networks directly from 16S rRNA gene sequences. bioRxiv. ISSN 2692-8205. doi: 10.1101/2024.01.26.576649.
  • Hassani, Sahar & Vik, Jon Olav (2024). FAIR Data Management: What's in it for researchers? zenodo. doi: 10.5281/zenodo.10577829.
  • Monsen, Øystein; Grønvold, Lars; Datsomor, Alex Kojo; Harvey, Thomas Nelson; Kijas, James & Suh, Alexander Sang-Jae [Show all 8 contributors for this article] (2024). The role of transposon activity in shaping cis-regulatory element evolution after whole genome duplication. bioRxiv. ISSN 2692-8205. doi: 10.1101/2024.01.02.573861.
  • Wedmark, Ylva Katarina; Vik, Jon Olav & Øyås, Ove (2023). A hierarchy of metabolite exchanges in metabolic models of microbial species and communities. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.09.05.556413.
  • Harvey, Thomas Nelson; Gillard, Gareth Benjamin; Røsæg, Line Lieblein; Grammes, Fabian; Monsen, Øystein & Vik, Jon Olav [Show all 8 contributors for this article] (2023). The genome regulatory landscape of Atlantic salmon liver through smoltification. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.08.16.553484.
  • Jin, Yang; Kozan, Darby; Anderson, Jennifer; Hensley, Monica; Shen, Meng-Chieh & Wen, Jia [Show all 10 contributors for this article] (2023). A high-cholesterol zebrafish diet promotes hypercholesterolemia and fasting-associated liver triglycerides accumulation. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.11.01.565134.
  • Jin, Yang; Li, Keshuai; Vik, Jon Olav; Hillestad, Marie & Olsen, Rolf-Erik (2023). Effect of dietary cholesterol, phytosterol, and docosahexaenoic acid on astaxanthin absorption and retention in rainbow trout. Research Square. ISSN 2693-5015. doi: 10.21203/rs.3.rs-2905894/v1.
  • Sahlström, Hanna Magdalena; Datsomor, Alex Kojo; Monsen, Øystein; Hvidsten, Torgeir Rhodén & Sandve, Simen Rød (2023). Functional validation of transposable element–derived cis-regulatory elements in Atlantic salmon. G3: Genes, Genomes, Genetics. ISSN 2160-1836. 13(4). doi: 10.1093/g3journal/jkad034.
  • Molversmyr, Håvard; Øyås, Ove; Rotnes, Filip & Vik, Jon Olav (2023). Extracting functionally accurate context-specific models of Atlantic salmon metabolism. npj Systems Biology and Applications. ISSN 2056-7189. 9(1). doi: 10.1038/s41540-023-00280-x.
  • Hofossæter, Mette Eline; Sørby, Randi; Göksu, Aleksandra Bodura; Mydland, Liv Torunn; Øverland, Margareth & Press, Charles McLean (2023). Cyberlindnera jadinii yeast as a functional protein source for Atlantic salmon (Salmo salar L.): Early response of intestinal mucosal compartments in the distal intestine. Fish and Shellfish Immunology. ISSN 1050-4648. 137. doi: 10.1016/j.fsi.2023.108758.
  • Zakhartsev, Maksim; Rotnes, Filip; Gulla, Marie; Øyås, Ove; van Dam, Jesse C.J. & Suarez-Diez, Maria [Show all 17 contributors for this article] (2022). SALARECON connects the Atlantic salmon genome to growth and feed efficiency. PLoS Computational Biology. ISSN 1553-734X. 18(6). doi: 10.1371/journal.pcbi.1010194. Full text in Research Archive
  • Datsomor, Alex Kojo; Gillard, Gareth Benjamin; Jin, Yang; Olsen, Rolf Erik & Sandve, Simen Rød (2022). Molecular Regulation of Biosynthesis of Long Chain Polyunsaturated Fatty Acids in Atlantic Salmon. Marine Biotechnology. ISSN 1436-2228. 24(4), p. 661–670. doi: 10.1007/s10126-022-10144-w. Full text in Research Archive
  • Bartosova, Zdenka; Villa Gonzalez, Susana; Voigt, Andre & Bruheim, Per (2021). High throughput semi-quantitative UHPSFC-MS/MS lipid profiling and lipid class determination. Journal of Chromatographic Science. ISSN 0021-9665. 59(7), p. 670–680. doi: 10.1093/chromsci/bmaa121.
  • Jin, Yang; Harvey, Thomas Nelson; Bartosova, Zdenka; Hassani, Sahar; Bruheim, Per & Sandve, Simen Rød [Show all 7 contributors for this article] (2021). Diet and Life Stage-Associated Lipidome Remodeling in Atlantic Salmon. Journal of Agricultural and Food Chemistry. ISSN 0021-8561. 69(12), p. 3787–3796. doi: 10.1021/acs.jafc.0c07281. Full text in Research Archive
  • Hiseni, Pranvera; Rudi, Knut; Wilson, Robert Charles; Hegge, Finn Terje & Snipen, Lars-Gustav (2021). HumGut: a comprehensive human gut prokaryotic genomes collection filtered by metagenome data. Microbiome. ISSN 2049-2618. doi: 10.1186/s40168-021-01114-w.
  • Dvergedal, Hanne; Sandve, Simen Rød; Angell, I.L.; Klemetsdal, Gunnar & Rudi, Knut (2020). Association of gut microbiota with metabolism in juvenile Atlantic salmon. Microbiome. ISSN 2049-2618. 8(1). doi: 10.1186/s40168-020-00938-2. Full text in Research Archive
  • Jin, Yang; Datsomor, Alex Kojo; Olsen, Rolf Erik; Vik, Jon Olav; Torgersen, Jacob Seilø & Edvardsen, Rolf Brudvik [Show all 9 contributors for this article] (2020). Targeted mutagenesis of Δ5 and Δ6 fatty acyl desaturases induce dysregulation of lipid metabolism in Atlantic salmon (Salmo salar). BMC Genomics. ISSN 1471-2164. 21. doi: 10.1186/s12864-020-07218-1. Full text in Research Archive
  • Lieven, Christian; Beber, Moritz E.; Olivier, Brett G.; Bergmann, Frank T.; Ataman, Meric & Babaei, Parizad [Show all 69 contributors for this article] (2020). MEMOTE for standardized genome-scale metabolic model testing. Nature Biotechnology. ISSN 1087-0156. 38, p. 272–276. doi: 10.1038/s41587-020-0446-y. Full text in Research Archive
  • Jin, Yang; Olsen, Rolf Erik; Harvey, Thomas Nelson; Østensen, Mari-Ann; Li, Keshuai & Santi, Nina [Show all 11 contributors for this article] (2020). Comparative transcriptomics reveals domestication‐associated features of Atlantic salmon lipid metabolism. Molecular Ecology. ISSN 0962-1083. 29(10), p. 1860–1872. doi: 10.1111/mec.15446. Full text in Research Archive
  • Harvey, Thomas Nelson; Sandve, Simen Rød; Jin, Yang; Vik, Jon Olav & Torgersen, Jacob Seilø (2019). Liver slice culture as a model for lipid metabolism in fish. PeerJ. ISSN 2167-8359. 7. doi: 10.7717/peerj.7732. Full text in Research Archive
  • Datsomor, Alex; Zic, Nikola; Li, Keshuai; Olsen, Rolf Erik; Jin, Yang & Vik, Jon Olav [Show all 10 contributors for this article] (2019). CRISPR/Cas9-mediated ablation of elovl2 in Atlantic salmon (Salmo salar L.) inhibits elongation of polyunsaturated fatty acids and induces Srebp-1 and target genes. Scientific Reports. ISSN 2045-2322. 9, p. 1–13. doi: 10.1038/s41598-019-43862-8. Full text in Research Archive
  • van Dam, Jesse; Koehorst, Jasper J; Vik, Jon Olav; dos Santos, Vitor A.P. Martins; Schaap, Peter J. & Suarez-Diez, Maria (2019). The Empusa code generator and its application to GBOL, an extendable ontology for genome annotation. Scientific Data. ISSN 2052-4463. 6(1). doi: 10.1038/s41597-019-0263-7. Full text in Research Archive
  • Datsomor, Alex Kojo; Olsen, Rolf Erik; Zic, Nikola; Madaro, Angelico; Bones, Atle M. & Edvardsen, Rolf Brudvik [Show all 8 contributors for this article] (2019). CRISPR/Cas9-mediated editing of Δ5 and Δ6 desaturases impairs Δ8-desaturation and docosahexaenoic acid synthesis in Atlantic salmon (Salmo salar L.). Scientific Reports. ISSN 2045-2322. 9, p. 1–13. doi: 10.1038/s41598-019-53316-w. Full text in Research Archive
  • Jin, Yang; Angell, Inga Leena; Sandve, Simen Rød; Snipen, Lars-Gustav; Olsen, Yngvar & Rudi, Knut (2019). Atlantic salmon raised with diets low in long-chain polyunsaturated n-3 fatty acids in freshwater have a Mycoplasma-dominated gut microbiota at sea. Aquaculture Environment Interactions. ISSN 1869-215X. 11, p. 31–39. doi: 10.3354/aei00297. Full text in Research Archive
  • Pham, Nhung; van Heck, Ruben; van Dam, Jesse C. J.; Schaap, Peter J.; Saccenti, Edoardo & Suarez-Diez, Maria (2019). Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling. Metabolites. ISSN 2218-1989. doi: 10.3390/metabo9020028.
  • Zakhartsev, Maksim (2019). Using a Multi-compartmental Metabolic Model to Predict Carbon Allocation in Arabidopsis thaliana. Methods in molecular biology. ISSN 1064-3745. doi: 10.1007/978-1-4939-9562-2_27.
  • Mulugeta, Teshome Dagne; Nome, Torfinn; To, Thu-Hien; Gundappa, Manu Kumar; Macqueen, Daniel J. & Våge, Dag Inge [Show all 8 contributors for this article] (2019). SalMotifDB: a tool for analyzing putative transcription factor binding sites in salmonid genomes. BMC Genomics. ISSN 1471-2164. 20(1). doi: 10.1186/s12864-019-6051-0. Full text in Research Archive
  • Rudi, Knut; Angell, Inga Leena; Pope, Phillip; Vik, Jon Olav; Sandve, Simen Rød & Snipen, Lars-Gustav (2018). A stable core gut microbiota across fresh- to saltwater transition for farmed Atlantic salmon. Applied and Environmental Microbiology. ISSN 0099-2240. 84(2). doi: 10.1128/AEM.01974-17. Full text in Research Archive
  • Gillard, Gareth Benjamin; Harvey, Thomas Nelson; Gjuvsland, Arne Bjørke; Jin, Yang; Thomassen, Magny Sissel S. & Lien, Sigbjørn [Show all 11 contributors for this article] (2018). Life-stage associated remodeling of lipid metabolism regulation in Atlantic salmon. Molecular Ecology. ISSN 0962-1083. 27(5), p. 1200–1213. doi: 10.1111/mec.14533. Full text in Research Archive
  • Jin, Yang; Olsen, Rolf Erik; Gillard, Gareth B.; Østensen, Mari-Ann; Korsvoll, Sven A. & Santi, Nina [Show all 9 contributors for this article] (2018). A systemic study of lipid metabolism regulation in salmon fingerlings and early juveniles fed plant oil. British Journal of Nutrition. ISSN 0007-1145. 120(6), p. 653–664. doi: 10.1017/S0007114518001885. Full text in Research Archive
  • Jin, Yang; Olsen, Rolf Erik; Østensen, Mari-Ann; Gillard, Gareth Benjamin; Li, Keshuai & Harvey, Thomas Nelson [Show all 11 contributors for this article] (2018). Transcriptional regulation of lipid metabolism when salmon fry switches from endogenous to exogenous feeding. Aquaculture. ISSN 0044-8486. 503, p. 422–429. doi: 10.1016/j.aquaculture.2018.12.089. Full text in Research Archive
  • Macqueen, Daniel J.; Primmer, Craig R.; Houston, Ross D.; Nowak, Barbara F.; Bernatchez, Louis & Bergseth, Steinar [Show all 34 contributors for this article] (2017). Functional Annotation of All Salmonid Genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture. BMC Genomics. ISSN 1471-2164. 18(484). doi: 10.1186/s12864-017-3862-8. Full text in Research Archive
  • Koehorst, Jasper J.; van Dam, Jesse C. J.; Saccenti, Edoardo; Martins dos Santos, Vitor A. P.; Suarez-Diez, Maria & Schaap, Peter J. (2017). SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles. Bioinformatics. ISSN 1367-4803. doi: 10.1093/bioinformatics/btx767.

View all works in Cristin

  • Rotnes, Filip; Øyås, Ove & Vik, Jon Olav (2021). An Energy-Centric Metabolic Model Connects the Atlantic Salmon Genome to Growth and Feed Utilization.
  • Øyås, Ove & Wahl, Andreas (2021). Kaffe med en forsker - Ove Øyås, NMBU.
  • Bartosova, Zdenka; Gonzalez, Susana Villa & Bruheim, Per (2019). High-throughput method for lipidomic analysis and semi-quantification of lipid classes.
  • Bartosova, Zdenka; Stafsnes, Marit Hallvardsdotter; Voigt, Andre & Bruheim, Per (2019). Supercritical fluid chromatography with mass spectrometry: A versatile tool for lipid profiling and lipid class quantitation.
  • Bartosova, Zdenka; Stafsnes, Marit Hallvardsdotter; Gjuvsland, Arne Bjørke; Harvey, Thomas Nelson; Nanton, Dominic & Sandve, Simen Rød [Show all 9 contributors for this article] (2018). Mass spectrometry based profiling of the salmon lipidome.
  • Vik, Jon Olav (2018). The Digital Salmon.
  • Vik, Jon Olav (2018). Digital production biology: From genes to food.
  • Vik, Jon Olav (2018). The Digital Salmon: From a reactive to a pre-emptive research strategy in aquaculture.
  • Vik, Jon Olav & Eckhoff, Rikke (2018). Møt en forsker: Systembiolog Jon Olav Vik. [Radio]. NRK Ekko.
  • Vik, Jon Olav & Risbråthe, Mette (2018). Sjå korleis laksen kan bli vegetarianar. [Internet]. forskning.no.
  • Vik, Jon Olav (2018). The Digital Salmon (animated film).
  • Rotnes, Filip (2018). Med kart og kompass blant gener og aminosyrer.
  • Vik, Jon Olav & Furuset, Anders (2018). Digital revolusjon kan gjøre dyre fôrforsøk billigere. [Newspaper]. Fiskeribladet.
  • Vik, Jon Olav & Furuset, Anders (2018). De tester ut en digital laks. [Internet]. Tekfisk podcast.
  • Rotnes, Filip (2018). A model of amino acid synthesis in Atlantic salmon.
  • Vik, Jon Olav (2018). Animating the Digital Salmon: How to explain systems biology research in simple words and pictures.
  • Grammes, Fabian; Koehorst, Jasper J; Hafthorson, Robert Anton; van Dam, Jesse; Gjuvsland, Arne Bjørke & Vik, Jon Olav (2018). SAPP-salar: Functional annotation interface for Salmo salar.
  • Vik, Jon Olav (2017). På vei mot Den Digitale Laksen.
  • Vik, Jon Olav (2017). Ein digital laks.
  • Vik, Jon Olav (2017). Kan vi regne oss fram til hva oppdrettslaksen bør spise?
  • Vik, Jon Olav (2017). Use Case: The Digital Salmon. Data and model management needs for a knowledge base of salmon physiology.
  • Hageskal, Audun & Vik, Jon Olav (2017). Disse laksene kan bli gull verdt for oppdrettsnæringen. [Newspaper]. Sysla.
  • Utskarpen, Audrun & Vik, Jon Olav (2017). Den digitale laksen. [Journal]. GENialt (Bioteknologirådets tidsskrift).
  • Vik, Jon Olav & Buer, Liz (2017). Den digitale laksen. [Newspaper]. Dagens Næringsliv.
  • Knudsen, Andreas Kjensli; Bratlie, Sigrid; Castro, Pablo; Lindström, Hannah Kim & Vik, Jon Olav (2017). Pilotepisode Anvendt Science Fiction - Bioteknologi 2.0.
  • Vik, Jon Olav (2017). Towards the Digital Salmon.
  • Vik, Jon Olav (2017). A purposeful simplification of reality.
  • Vik, Jon Olav (2017). Can we calculate what to feed farmed salmon?
  • Vik, Jon Olav (2017). The Digital Salmon hits the big screen.
  • Vik, Jon Olav (2016). How can mathematical modelling help feed the world?
  • Vik, Jon Olav (2016). Use Case: The Digital Salmon. Data and model management needs for a knowledge base of salmon physiology.
  • Vik, Jon Olav & Criscione, Valeria (2016). The fishy biotech future. [Business/trade/industry journal]. Norway Exports Seafood, Fishing & Aquaculture.
  • Vik, Jon Olav (2016). Slik landet vi den digitale laksen.
  • Vik, Jon Olav & Wessel, Solveig (2015). NMBU-forsker: – Nå er vi på landslaget i bioteknologi. [Newspaper]. Østlandets Blad.
  • Vik, Jon Olav; Olsen, Claude R. & Totland, Karin (2015). Nasjonalt senter for digitalt liv viser vei mot fremtiden. [Business/trade/industry journal]. NBS-nytt 2015/4, Tidsskrift for Norsk Biokjemisk Selskap.
  • Vik, Jon Olav (2015). First tailbeats of the Digital Salmon. [Internet]. iScience blog.
  • Jeyakumar, Harini & Hvidsten, Torgeir Rhoden (2023). Evaluation of machine learning methods to decode transcriptional regulation. Norwegian University of Life Sciences.
  • Førrisdal, Julie Wollebæk & Hvidsten, Torgeir Rhoden (2023). Deciphering transcriptional regulation using deep neural networks. Norwegian University of Life Sciences.
  • Wedmark, Ylva Katarina & Øyås, Ove (2022). Unbiased analysis of metabolite exchanges in metabolic models. Norwegian University of Life Sciences.
  • Molversmyr, Håvard & Vik, Jon Olav (2021). Model-based integration of omics data for context-specific analysis of Atlantic salmon metabolism. Norwegian University of Life Sciences.
  • Ruud, Ingunn Marie Verne & Vik, Jon Olav (2020). Developing and validating tests for a metabolic model of Atlantic salmon (Salmo salar). Norwegian University of Life Sciences.
  • Rotnes, Filip; Vik, Jon Olav; Zakhartsev, Maksim & Gjuvsland, Arne Bjørke (2018). Reconstruction and modelling of amino acid synthesis in Salmo salar. Norges miljø- og biovitenskapelige universitet.

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Research group

Discovering connections between salmon genes and fish feed

Norwegian researchers are developing a mathematical model of the salmon that will make it easier to find the optimal feed for aquaculture production of salmon.

The salmon is a predator which in the wild eats small fish and crustaceans in the ocean, and in the first decenniums of salmon breeding the feeding where based on fish oil and fish meal. But the growth in the business has demanded other feed sources, and today most of the feed are produced on land by farming. 75 percent of the fat and protein in the feed are derived from different plants and crops. This is not a sustainable feed source, and also the prices varies a lot.

The business is therefore investigating alternative feed variants, but to test out which of the many alternatives that are the best choice for salmon is time consuming, with a potential cost of several millions. An additional challenge is that the best feed mixture may vary considerably between different salmon strains.

The researcher know a lot about the salmon genes as the whole salmon genome was fully sequenced and mapped in 2016. They also know a lot about salmon physiology from studying the intestine, liver and muscles. Currently, the aim is to develop a mathematical model for all the processes going on in the fish's different organs, and then fuse it into one coherent model of salmon physiology - the digital salmon. This will then be used to simulate and test different variations of salmon feed, thereby allowing faster and more cost efficient development of better and more sustainable feeds.

Researchers within mathematics, data analysis, informatics, sensor technology, genomics and experimental biologists work together in the project.

The project is headed by the Norwegian University of Life Sciences (NMBU) with partners at NTNU, UiB, UiT,University of Stirling, UK, University of Wagening (The Netherlands), and the Institute of Marine Research (Norway).

Industrial partners are AquaGen (salmon breeding) and EWOS (feed producer).

More about the project

Towards the Digital Salmon

In the project Towards the Digital Salmon: From a reactive to a pre-emptive research strategy in aquaculture (DigiSal) the researchers will establish a mathematical model of the salmon physiology, to aid the development of tomorrow's salmon feed. The long term vision is to construct the Digital Salmon - a system physiology model where all the salmon's body functions are simulated in a coherent mathematical model. This will be the core in an open access knowledge platform of salmon.

Todays feed composition used for salmon farming is not sustainable. New ingredients are tried out, such as yeast and bacterial extracts, and micro algae. It is, however, a time consuming and expensive process to test out all possible combinations of alternative ingredients in feeding experiments. Also, different salmon strains will most likely respond differently due genetic differences. By replacing initial experiments by simulations, a more cost effective and improved feed development can be established.

Utilizing the salmon genome

The researcher will therefore translate knowledge about salmon physiology into mathematical expressions. The underlying biochemical pathways are tightly linked to the genetic expression of the enzymes and regulatory proteins involved. A major achievement was made in 2016 when the complete salmon genome was released after sequencing three billions of DNA bases making up the salmon's 29 chromosomes. Mapping the genome also revealed how salmon strains and the salmonidae family may have evolved.

This new insight paved the way for efficient measurement of the level of expression of all genes in a tissue or blood sample from salmon. In addition, measurements of thousands of biomolecules and metabolites in the same samples can be fed into the model. Based on the data, a dynamic model that respond to variations in feeding, genetic background and other parameters will be constructed.

The knowledgebase in terms of the data and the model, will enable new analysis of already known aspects of the salmon's biology, discover knowledge gaps, further acquiring and incorporating new data, and there by develop and improve the model. The goal is that eventually this will be a tool used by the aquaculture industry to test out variants of salmon strains, feed and environment in a simulation, thereby foreseeing eventual challenges and problems before they occur in production. Hence, the title "from reactive to pre-emptive".

 

Mathematical models make it easier

The researcher view the salmon as a biological system built up of a set of components that rely on each other. To understand variation in a given phenomenon, such as body growth, the potential reactions and processes in the different organs contributing to the phenomenon are combined in a mathematical model that fits with the experimental measurements that can be sampled from salmon.

The benefit of describing physiological processes by mathematical models and run simulations is partly that the researcher can reveal new knowledge, and partly that wrong hypothesis' can be abandon before being tested in experiments. The researcher can therefore quickly select the feed mixtures that most likely will succeed, and then test these in actual feeding experiments.

In DigiSal the researcher especially study metabolism and how this is coupled to genes. They can measure what the salmon eats, how the feed is ingested and converted down to molecular level.

The mathematical model will be encoded with standardized nomenclature of the salmon's biochemical reactions, enzymes, reaction parameters, and molecules involved. The standardization allows automatic coupling of already established biochemical databases and datasets form other experiments.

Transdisciplinary

The project gathers researchers that do mathematics, data analysis, informatics, sensor technology, genomics and experimental biology.

As so many disciplines and partners are involved, the project management emphasizes that the different experts should develop understanding of each others research areas and encourages the team to see possibilities and limitations within the project.

Most of the researchers are located at NMBU, Ås, but several of the project partners are located in other parts of Norway or abroad. The project partners will gather physically from time to time, but usually meetings and contact will be by Skype.

Responsible Research and Innovation

The project will emphasis the dialogue with lay people, researchers within and outside the project, industrial partners and representatives for the authorities. During the project seminars aimed at lay people discussing the use of systems biology for more sustainable food production, possibilities for industry development, and potential impacts of new technology on the consumer, will be arranged.

The project will also make data and models available and usable by a web-platform, integrated with a resource base of the salmon genome.

Innovation

When the mathematical models are in place, the fish farmers will get recommendations about the feed mixture of their specific salmon strain. In the future, the fish farmers might get custom advice on dosing of the feed, incorporating weather and temperature data and forecasts, for optimal feeding.

For companies that develop new feed variants, the mathematical models will be a tool to select the best ingredients, resulting in shorter time from research to market with less failing animal experiments and lower costs.

For companies that breed new salmon strains, the model will enable them to speed up the process of selecting suitable fishes for further development and breeding, as well as quality control, surveillance and maintenance of their product,

Following the project and an increased need for monitoring and data acquisition, one could envisage development of devices and sensors for a future marked in aquaculture.

Watch presentation of the project

At the Digital Life 2020 conference postdoctoral fellow Ove Øyås talked about the project.
Watch recording of his talk.

Latest news from the project