Salmon farmed on modern feeds contains less of the healthy, long-chain fatty acids (EPA and DHA) than before. Up until the turn of the millennium, farmed salmon were fed fish oil as a replacement for their omega-3 rich natural prey. However, fish oil is now a scarce resource, and more than half of the fat in modern feeds comes from plant oils that are inexpensive, but devoid of long-chain omega-3 fatty acids. How can we increase the omega-3 content of salmon on sustainable feeds?
One option is to breed salmon that are well adapted to the feeds of the future. There is heritable variation in salmon’s ability to build EPA and DHA from shorter omega-3 fatty acids. The DNA sequence of salmon is now well known, allowing rapid characterization of heritable differences in nutrient utilization. A salmon family that appears promising on one feed, may not be the best on another. Therefore, we need to understand the salmon’s body as a system: a functional whole made up of parts that mutually affect, but also depend on, each other. A systems understanding of the interplay between feed and genetic factors will allow a tailoring of fish to feed and vice versa, which is robust to fluctuations in feedstuff availability and pricing.
As a first step towards such a systems understanding, the GenoSysFat project involves two biological experiments. 1) A traditional feeding experiment using high- vs low-omega-3 diets and salmon families that differ in feed utilization. 2) A novel study with pieces of liver kept alive and “fed” in laboratory dishes, studying for each fish how different feeds affect metabolism and gene activity. This allows faster and more detailed exploration of the interplay between genetics and feeds. Results will be interpreted with the help of mathematical models for the biochemical reaction networks, which are well established for other species and will be adapted to salmon based on the newly sequenced salmon genome.
- People (33)
- Institutions (7)
- Investigations (0+4)
- Studies (0+9)
- Assays (0+32)
- Data files (2+69)
- Models (0+2)
- SOPs (1+8)
- Publications (7)
- Presentations (0+6)
- Events (0+2)
- Samples (0+1)
Institutions: University of Rostockorcid.org/0000-0002-5886-5563
Reproducibility of results is fundamental to all sciences. In computational biology, standard formats like Systems Biology Markup Language (SBML), CellML, or NeuroML enable the exchange of simulation models, and foster interoperability between software tools importing and exporting these formats.
My main research interest is in developing methods and tools that (1) improve the reuse of computational models in biology, (2) ensure reproducibility of modeling results and (3) that lead to easier
Software Engineer and Architect working within the FAIRDOM team.
Leading the development of SEEK and RightField.
Institutions: Wageningen University & Researchorcid.org/0000-0001-8172-8981
Combined taxonomy table of abundance of OTUs (Operational Taxonomy Units) in both freshwater and saltwater samples from 16S V3-V4 Illumina sequencing of gut microbiota. Primers used for sequencing are given in https://fairdomhub.org/sops/270
The OTUs are presented in number of counts per sample (n=349). Each row represent one sample. Raw data are available in the Sequence Read Archive database under accession number SRP119730 (https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP119730).
Investigations: 1 hidden item
Studies: 1 hidden item
Assays: 1 hidden item
We have adapted the definitions of terms in [ISA best practice] and [programmes and projects]:
Programme = Overarching research theme (The Digital Salmon)
Project = Research grant (DigiSal, GenoSysFat)
Investigation = a particular biological process, phenomenon or thing
(typically corresponds to [plans for] one or more closely related papers)
Study = experiment whose design reflects a specific biological research question
Assay = standardized measurement or diagnostic experiment using a
Contributor: Jon Olav Vik
Investigations: No Investigations
Studies: No Studies
Assays: No Assays
Date Published: No date defined
Journal: Mol Ecol
PubMed ID: 29431879
Citation: Mol Ecol. 2018 Feb 12. doi: 10.1111/mec.14533.
Date Published: 15th Jan 2018
Journal: Appl Environ Microbiol
Citation: Appl Environ Microbiol 84(2) : e01974-17
Author: Edgar R. C.
Date Published: 18th Aug 2013
Journal: Nat Methods
PubMed ID: 23955772
Citation: Nat Methods. 2013 Oct;10(10):996-8. doi: 10.1038/nmeth.2604. Epub 2013 Aug 18.
Author: Edgar R. C.
Date Published: 12th Aug 2010
PubMed ID: 20709691
Citation: Bioinformatics. 2010 Oct 1;26(19):2460-1. doi: 10.1093/bioinformatics/btq461. Epub 2010 Aug 12.
Author: Brazma A.,Hingamp P.,Quackenbush J.,Sherlock G.,Spellman P.,Stoeckert C.,Aach J.,Ansorge W.,Ball C. A.,Causton H. C.,Gaasterland T.,Glenisson P.,Holstege F. C.,Kim I. F.,Markowitz V.,Matese J. C.,Parkinson H.,Robinson A.,Sarkans U.,Schulze-Kremer S.,Stewart J.,Taylor R.,Vilo J.,Vingron M.
Date Published: 1st Dec 2001
Journal: Nat Genet
PubMed ID: 11726920
Citation: Nat Genet. 2001 Dec;29(4):365-71.
Author: Caporaso J. G.,Kuczynski J.,Stombaugh J.,Bittinger K.,Bushman F. D.,Costello E. K.,Fierer N.,Pena A. G.,Goodrich J. K.,Gordon J. I.,Huttley G. A.,Kelley S. T.,Knights D.,Koenig J. E.,Ley R. E.,Lozupone C. A.,McDonald D.,Muegge B. D.,Pirrung M.,Reeder J.,Sevinsky J. R.,Turnbaugh P. J.,Walters W. A.,Widmann J.,Yatsunenko T.,Zaneveld J.,Knight R.
Date Published: 11th Apr 2010
Journal: Nat Methods
PubMed ID: 20383131
Citation: Nat Methods. 2010 May;7(5):335-6. doi: 10.1038/nmeth.f.303. Epub 2010 Apr 11.
Author: Bekaert M.
Date Published: 14th Nov 2012
Journal: PLoS One
PubMed ID: 23166792
Citation: PLoS One. 2012;7(11):e49903. doi: 10.1371/journal.pone.0049903. Epub 2012 Nov 14.