Center for Integrative Genetics
Center for Integrative Genetics
Salte R.,Norwegian University of Life Sciences |
Salte R.,Center for Integrative Genetics |
Bentsen H.B.,Nofima Marine |
Moen T.,Norwegian University of Life Sciences |
And 9 more authors.
Canadian Journal of Fisheries and Aquatic Sciences | Year: 2010
We estimated additive genetic variation and heritability of survival after Gyrodactylus salaris infection from survival records in a pedigreed family material of wild Atlantic salmon (Salmo salar) in a controlled challenge test. We used a statistical model that distinguishes between survival time for the fish that died and the ability to survive the entire test as two separate traits. Eleven of the 49 full-sib families suffered 100% mortality, 15 families had between 10% and 25% survival, and the four least affected families had survival rates between 36% and 48%. Estimated heritability of survival on the liability scale was 0.32 ± 0.10. Time until death for fish that died during the test and the ability to survive the entire test were not expressions of the same genetic trait. Simply selecting survivors as parents for the next generation is expected to more than double the overall survival rate in only one generation, given similar exposure to the parasite. Improving the genetic capacity to survive the infection will probably not eradicate the parasite, but when used as a disease control measure, such improvement may contain the infection at a level where the parasite ceases to be a major problem.
Machina A.,Georgia Institute of Technology |
Machina A.,Center for Integrative Genetics |
Ponosov A.,Center for Integrative Genetics |
Voit E.O.,Georgia Institute of Technology
Journal of Biotechnology | Year: 2010
Recent trends suggest that future biotechnology will increasingly rely on mathematical models of the biological systems under investigation. In particular, metabolic engineering will make wider use of metabolic pathway models in stoichiometric or fully kinetic format. A significant obstacle to the use of pathway models is the identification of suitable process descriptions and their parameters. We recently showed that, at least under favorable conditions, Dynamic Flux Estimation (DFE) permits the numerical characterization of fluxes from sets of metabolic time series data. However, DFE does not prescribe how to convert these numerical results into functional representations. In some cases, Michaelis-Menten rate laws or canonical formats are well suited, in which case the estimation of parameter values is easy. However, in other cases, appropriate functional forms are not evident, and exhaustive searches among all possible candidate models are not feasible. We show here how piecewise power-law functions of one or more variables offer an effective default solution for the almost unbiased representation of uni- and multivariate time series data. The results of an automated algorithm for their determination are piecewise power-law fits, whose accuracy is only limited by the available data. The individual power-law pieces may lead to discontinuities at break points or boundaries between sub-domains. In many practical applications, these boundary gaps do not cause problems. Potential smoothing techniques, based on differential inclusions and Filippov's theory, are discussed in Appendix A. © 2010 Elsevier B.V.