Centre for Integrative Genetics (CIGENE)

Professor, Director

E-mail: stig.omholt@umb.no

Phone (work): +47-6496-5297; (cellular): +47-9094-0985

Degree: Dr. Philos. in theoretical biology at the University of Oslo in 1988 on self-organization aspects of the honeybee society.

Current positions: Professor at the Norwegian University of Life Sciences (UMB) and
Director of the national core facility “Centre for Integrative Genetics” (CIGENE).
Concurrently appointed Kristine Bonnevie Professor at the CoE Centre for Ecological and Evolutionary Synthesis,University of Oslo (from 1st September 2010)

Current organizational responsibilities: Chair of the RCN-FUGE Resource and Competence Platform on marine functional genomics “GenoFisk”; Chair of the RCN-FUGE nation-wide Genotyping and Sequencing Platform; Chair of the international In Vitro Meat Consortium; Board member of MedCoast Scandinavia; SAB member of Oxford Centre for Integrative Systems Biology, University of Oxford; SAB member of Centre for Integrated Systems Biology of Ageing and Nutrition, University of Newcastle; SAB member of EraSysBio. SAB member of Centre for Marine Evolutionary Biology at University of Gothenburg; Norway’s representative to European Strategy Forum on Research Infrastructures (ESFRI); Member of the EU NOE “Virtual Physiological Human” with main focus on integrating genetics theory and methodology with multiscale and multiphysics modeling under the umbrella of the IUPS Physiome Project; Member of the scientific team leading the sequencing of the Atlantic salmon genome (initiator); Member of the scientific team leading the sequencing of the Atlantic cod genome .

Current project portfolio: Involved in the Atlantic salmon and cod whole genome sequencing initiatives, in one FP6 NoE on ageing and development, in one FP7 NoE on the Virtual Physiological Human, three NordForsk projects, and several projects funded by the Research Council of Norway ranging from marine functional genomics to modelling of the microphysiology of the brain.

Professional background: Dairy farmer (1978-1989); Research leader in the aquaculture industry (1984-1986); Scientific consultant for various companies (1986-1988); Head of one node in the government-initiated Norwegian R&D computer network (FUNN) (1988-1989); Research leader at the Department of Animal Science (1992-1995); Assistant head of the Department of Animal Science (1996-1997); Head of the Department of Animal Science (1998-1999); Professor at the Department of Animal Science, UMB (1989-); Director of CIGENE since 2002.

Current research activity: Through interactions with several highly qualified individuals inside and outside CIGENE, I am currently involved in a rather broad range of research topics. However, most of the topics are connected to the overall aim of contributing to the development of a framework or theoretical foundation being capable of explaining and predict how observed genetic variation results in observed phenotypic variation in causal terms. Many will call this activity systems biology. However, one should keep in mind that genetics (from the Greek genno (γεννώ) = give birth) is defined as the science of genes, heredity, and the variation of organisms (http://en.wikipedia.org/wiki/Genetics). This shows that a substantial portion of what people consider to be systems biology research, i.e. the understanding of system characteristics (phenotypes) from molecular interactions, fits very well within the disciplinary goals of genetics. Such a theoretical foundation will have a substantial impact on a broad range of topics within evolutionary biology, production biology and biomedicine. Needless to say, it will also have a strong impact on synthetic biology in general.

Guided by the research vision stated above I am currently involved in understanding basic brain physiological phenomena, the tanning process in humans, the dynamics underlying generation of malign melanoma, the ultimate and proximal mechanisms responsible for the observed variation in filet colour within and between salmon species, the making of methodology and computational pipelines for handling the next generation of genotypic or sequence data in connection with detection of causative genetic variation in biomedicine and production biology, the additivity/non-additivity structures associated with the expression phenotypes in yeast, the development of a new high-throughput and high-dimensional phenotyping methodology for yeast based on FTIR spectroscopy, use of yeast as a model system for a detailed understanding of long-standing enigmatic phenomena within genetics, development of a new approach for how to make use of multivariate methods on complex models of biological differentiation in order to gain a much deeper understanding of model behaviour as well as the relationship between parameters/premises and model behaviour, use of the mammalian heart model to elucidate basic genetic phenomena, and the link between statistical descriptors genetic phenomena at the population level and system dynamics models operating at the individual level.

Previous research:  Read more

Photo: Stig Omholt (photo by Håkon Sparre, UMB)

Selected publications (2000-2011):

• Gjuvsland et al. (2011) Order-preserving principles underlying genotype-phenotype maps ensure high additive proportions of genetic variance. Journal of Evolutionary Biology (in press)
  
• Bradley et al. (2011) OpenCMISS: A multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. Progress in Biophysics and Molecular Biology (in press)
  
• Hunter et al. (2011) The Heart Physiome Project. Wires Systems Biology (in press)
  
• Warringer et al. (2011) Trait Variation in Yeast Is Defined by Population History. PLoS Genet 7(6): e1002111. 
• Tøndel et al. (2011) Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models. BMC Systems Biology 5:90
  
• Øyehaug et al. (2011) Dependence of spontaneous neuronal firing and depolarisation block on astroglial membrane transport mechanisms. Journal of Computational Neuroscience; DOI: 10.1007/s10827-011-0345-9Online First™
  
• Salvado et al. (2011), Methods for and results from the study of design principles in molecular systems, Math. Biosci, 16/j.mbs.2011.02.005
  
• Houle et al. (2010) Phenomics – the next challenge. Nature Review Genetics 11: 855-866;Hunter et al. (2010) A vision and strategy for the virtual physiological human in 2010 and beyond. Philos Transact A Math Phys Eng Sci. 368: 2595
• Gjuvsland AB, Plahte E, Ådnøy T, Omholt SW (2010). Allele Interaction - Single Locus Genetics Meets Regulatory Biology. PLoS ONE 5(2):e9379
• Østby et al. (2009) Astrocytic mechanisms explaining neural-activity-induced shrinkage of extraneuronal space. PLoS Computational Biology. 5(1): e1000272. doi:10.1371/journal.pcbi.1000272
• Lorenz S, Brenna-Hansen S, Moen T, Roseth A, Davidson WS, Omholt SW, Lien S. (2010) BAC-based upgrading and physical integration of a genetic SNP map in Atlantic salmon. Anim Genet. 2010 Feb;41(1):48-54. Epub 2009 Nov 16
  
• Wolkenhauer, Olaf; Lao, Angelyn; Omholt, Stig; Martens, Harald (2009) Systems approaches in molecular and cell biology: making sense out of data and providing meaning to models. Quantum Information and Computation VII. Edited by Donkor, Eric J.; Pirich, Andrew R.; Brandt, Howard E.. Proceedings of the SPIE, Volume 7342 (2009)., pp. 734318-734318-11
  
• Martens H, Veflingstad SR, Plahte E, Martens M, Bertrand D, Omholt SW (2009). The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors. BMC Systems Biology 3:87
  
• Thingnes J, Oyehaug L, Hovig E, Omholt SW. (2009) The mathematics of tanning. BMC Syst Biol. 2009 Jun 9;3:60.
  
• Kohler A, Böcker U, Warringer J, Blomberg A, Omholt SW, Stark E, Martens H. (2009) Reducing inter-replicate variation in fourier transform infrared spectroscopy by extended multiplicative signal correction. Appl Spectrosc. 2009 Mar; 63(3):296-305
• Moen et al. (2008) A linkage map of the Atlantic salmon (Salmo salar) based on EST-derived SNP markers. BMC Genomics 9:223
• Rajasingh, H. et al. (2008) When parameters in dynamic models become phenotypes - a case study on flesh pigmentation in the Chinook salmon. Genetics, 179: 1113-1118
• Gjuvsland A. et al. (2007). Threshold-dominated regulation hides genetic variation in gene expression networks. BMC Systems Biology, 1: 57
• Rajasingh H. et al. (2007). Why are salmonids pink? Canadian Journal of Fisheries and Aquatic Sciences. 64: 1614–1627
• Gjuvsland A. et al. (2007). Nonlinear regulation enhances the phenotypic expression of trans-acting genetic polymorphisms. BMC Systems Biology, 1: 32
• Gjuvsland, A. et al. (2007). Statistical epistasis is a generic feature of gene regulatory networks. Genetics 175: 411-420
• Hayes, B. et al. (2006). Power of QTL mapping experiments in commercial Atlantic salmon populations, exploiting linkage and linkage disequilibrium and effect of limited recombination in males. Heredity 97: 19-26
• Kristensen, VN. et al. (2006). Genetic variation in putative regulatory loci controlling gene expression in breast cancer. PNAS 103: 7735-7740
• Edvardsen, H. (2006). Experimental validation of data mined SNPs from several databases and consecutive dbSNP builds. Pharmacogenet. Genom. 16: 207-217
• Rajasingh H. et al. (2006) Carotenoid dynamics in Atlantic salmon. BMC Biology 4: 10
• Guidugli KR et al. (2005). Vitellogenin regulates hormonal dynamics in the worker caste of a eusocial insect, FEBS Letters, 579: 4961-4965
• Omholt SW and Amdam GV (2004) Epigenetic Regulation of Aging in Honeybee Workers. Sci. Aging Knowl. Environ. (26), pe28. [DOI:10.1126/sageke.2004.26.pe28]
• Amdam GV et al. (2004) Hormonal control of the yolk precursor vitellogenin regulates immune function and longevity in honeybees. Exp. Gerontol., 39: 767-773
• Amdam GV et al. (2003). Social exploitation of vitellogenin. Proc. Natl. Acad. Sci., 100: 1799-1802
• Beye M et al. (2003). The gene csd is the primary signal for sexual development in the honeybee and encodes an SR-type protein Cell, 114: 419-429
• Øyehaug L et al. (2003). Targeted reduction of complex models with time scale hierarchy - a case study. Math. Biosciences, 185: 123-152
• Amdam GV and Omholt, SW (2002). The regulatory anatomy of honeybee lifespan. J. theor. Biol., 216: 209-228
• Øyehaug L et al. (2002). The regulatory basis of melanogenic switching. J. theor. Biol., 216: 209-228
• Omholt SW (2002). Cell biology - Foundations of systems biology. Science, 295: 2220-2220
• Omholt SW et al. (2000). Gene regulatory networks generating the phenomena of additivity, dominance and epistasis. Genetics, 155: 969-980