Stoffskiftemodellering for havbruk

Farmed Atlantic salmon is Norway’s second lagest export, and by 2030 aquaculture will provide most of the world’s food fish. Salmon is a highly resource-efficient production animal, but scarce feed resources require action to adapt new, sustain able diets. Over the last few decades, feed formulations have shifted from being mostly based on fish to using plant sources, which has brought new challenges, and recipes have become increasingly complicated. However, in the volatile market for raw materials, the most cost-effective feed composition is a moving target. This applies both from an economic and sustainability perspective. Meat production is basically a kind of chemical processing, where the sophisticated chemical machinery in the animals’ metabolism break down raw materials and build up new products. These chemical processes are carried out by molecular machines called which are coded for in the genome. With knowledge of which enzymes an organism’s genes code for, the metabolism’s chemical repertoire can also be mapped. Methods for systematising such a network of chemical reactions have been developed and used both in medicine and bioprocess technology, but until now little has been used in animal production and other production biology. This thesis applies systems biology methods to study the salmon’s metabolism. In the first paper, we develop SALARECON, a constraint-based metabolic model for salmon, and the first genome-scale metabolic reconstruction for a production animal. We use this to study the relationship between growth rate and oxygen saturation in the water, as well as to predict growth-limiting amino acids for growth on different protein sources. The second paper focuses on functional interpretations of gene expression data and using gene expression data to remove low-expression reactions to generate sample-specific models with bounded solution spaces. Starting from SALARECON, six methods were used to create mathematically and stoichiometrically consistent submodels for each of 208 samples from a feed-switch experiment. To compare the models functionally, we used a test framework for chemical processes, where SALARECON was able to perform 121. The relevance of genes for certain processes was determined through simulations with SALARECON, and this was combined with the gene expression data to form a reference basis for each of the samples. Each sub-model was characterized functionally, and compared with the reference of the corresponding sample. Of the six methods, GIMME, iMAT and INIT were best able to produce models according to the criteria. The study shows that sample-specific sub-models can provide more precise functional insight than a general model, and that these methods developed for humans are transferable to other non-mammals. In the third paper, we extended SALARECON with flexible fat composition to the model SimSaLipiM and used this for simulated feeding experiments. The model was extended with synthesis and breakdown of fatty acids, as well as flexible fatty acid composition in the biomass to reflect the observed variation. In order to adapt the model to feed composition data, the model can be regenerated based on a list of fatty acids. We formulated feed intake in the model, based on chemical compositions of feed ingredients, and used CO2 footprint of ingredients as weights to identify sustainable feed recipes. Optimal feed compositions were highly context-dependent and location, availability of ingredients and how the criterion for optimality has a major impact on the result. In summary, this thesis points the way to applications of metabolic modeling in research on salmon. Mathematical models serve as lenses through which we interpret data and explore the consequences of underlying assumptions. They allow us to analyze complex systems, evaluate alternative strategies, and identify causal effects, bridging theory and empirical observations, as shown in the development of feed recipes and gene expression data, integrating modeling in future experiments on salmon will both contribute to enriching the results and to developing the model framework further.Laksenæringen er Norges nest største eksportnæring, og innen 2030 vil over halvparten av verdens matfiskforbruk komme fra oppdrett. Atlanterhavslaks er et svært ressurseffektivt produksjonsdyr, men begrensede fôrressurser krever handling for å tilpasse nye, bærekraftige dietter. Oppskriftene har blitt mer og mer kompliserte, og med et råvaremarked i stadig omveltning, varierer det hva som er den mest lønnsomme fôrsammensetningen, både økonomisk og i bærekraftperspektiv. Kjøttproduksjon er i bunn og grunn en slags kjemisk prosessering, hvor det sofistikerte maskineriet i dyrenes stoffskifte bryter ned råstoff og bygger opp nye produkter. Enzymer, de molekylære maskinene som utfører arbeidet, er kodet for i genene. Med kunnskap om hvilke enzymer en organismes gener koder for, kan også stoffskiftets kjemiske repertoar kartlegges. Metoder for å systematisere et slikt nettverk av kjemiske reaksjoner er utviklet og anvendt både i medisin og bioprosessteknologi, men inntil nå har det vært så godt som fraværende i dyreproduksjon og annen produksjonsbiologi.publishedVersio

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

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Reconstruction and modelling of amino acid synthesis in Salmo salar

This thesis documents the process behind the reconstruction of the Salmo Salar Amino Acid Synthesis model (SalSAA). The model is a mathematical description of amino acid biosynthesis and protein polymerisation in Atlantic salmon. Flux balance analysis was performed to study the effect of the amino acid composition of fish meal versus soyben meal on protein polymerisation. As indicated by earlier reseach, fish meal has a composition of amino acids promoting higher growth rate for protein than soybean meal. The model also correctly predicts the most beneficial amino acid additions to soybean meal to promote growth.Dette dokumentet dokumenterer prosessen bak rekonstruksjonen av den støkiometriske modellen for aminosyresyntese og proteinpolymerisering i atlanterhavslaks (SalSAA). Modellen er en matematisk beskrivelse av aminosyresyntese og proteinpolymerisering i Atlanterhavslaks. Det ble gjort simuleringer for ̊a studere effekten av protein fra fiskemel og soyamel p ̊a proteinsyntese. Tidligere forskning tilsier at fiskemel har en komposisjon av aminosyrer som fremmer vekst bedre enn soyamel, hvilket ble bekreftet av modellen. Simuleringene foruts ̊a ogs ̊a korrekt hvilke aminosyretilsetninger som forbedrer vekst p ̊a soyamel best.submittedVersionM-K

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds