Systems biology focus on system level analysis of molecular biology by modeling biological properties from gene function to organism phe- notypes as emergent properties arising from interacting genes, proteins and metabolites. We apply systems biology approach to reverse-en- gineer the regulatory networks of plants from transcriptomics, proteomics and metabolomics (omics) data. We then use these networks as the basis for investigating the complexity and evolution of gene regulation across diverse plant species and for predicting the effects of artificial perturbation in transgenic plants. Our network models can be used to computationally generate testable hypothesis and have been applied, for example, to select candidate genes for perturbation experiments.

Hvidsten Torgeir 1150The recent revolution in omics technology has enabled re- searchers to move beyond dissecting biological systems one gene at a time, and instead modeling interactions of multiple genes, proteins and metabolites required to understand complex biological properties.The focus on interactions is the hallmark of systems biology, and requires integrating massive amount of heterogeneous data sources to curb the combinatorial explosion resulting from studying more than one gene at a time.To infer network models from data, we use a technique from computer science called machine learning.Machine learning infers general models form characterized observations (examples) and can be used both to explain underlying patterns in data and to provide predictions for new, uncharacterized observations.

The focus on gene networks rather than on individual genes has, for example, allowed us to study the complexity of gene regulation in aspen leaves and wood.We found that a number of relevant regulators in these systems could only be identified when considering interactions of regulators such as AND logics. We have also compared networks across plant species and shown, for example, that gene centrality in regulatory networks tends to be conserved. Moreover, by studying the conservation of gene neighborhoods across species we can more confidently identify the most likely functional orthologs among several predicted candidates. Finally, we have demonstrated the power of using network for predicting the molecular effects of perturbation experiments and for explaining the observed phenotypes in transgenic trees.
Bild Hvidsten 880The network neighborhood of AT3G52480 in A. thaliana and the corresponding network of orthologs in Populus. Red links are conserved. The most sequence similar predicted ortholog of AT3G52480 (POPTR_0006s22080) has diverged in regulation, while the less sequence similar ortholog (POPTR_0016s07240) has co-expression partners that are orthologs of the co-expression partners of AT3G52480 (i.e. conserved regulation). AT3G52480 is uncharacterized, but the network neighborhood in A. thaliana is enriched for genes involved in response to fructose stimulus (FDR corrected p-value of 5.5e-06).

We are developing a number of online tools for facilitating systems biology analysis in plants. ComPlEx is a portal for Com- parative analysis of Plant Expression networks, and PopGenIE/ ConGenIE (Populus/Conifer Genome Integrative Explorer) now include network tools for performing co-expression anal- ysis. All tools are available from the PlantGenIE web resource (

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Publications list

  1. Functional and evolutionary genomic inferences in Populus through genome and population sequencing of American and European aspen
    Proc Natl Acad Sci U S A. 2018, 115(46):E10970-E10978
  2. The grayling genome reveals selection on gene expression regulation after whole genome duplication
    Genome Biol Evol. 2018 Sep 15 [Epub ahead of print]
  3. Subfunctionalization versus neofunctionalization after whole-genome duplication
    Nat Genet. 2018 Jul;50(7):908-909
  4. Ethylene signaling induces gelatinous layers with typical features of tension wood in hybrid aspen
    New Phytol. 2018, 218 (3):999-1014
  5. Lineage-specific rediploidization is a mechanism to explain time-lags between genome duplication and evolutionary diversification
    Genome Biol. 2017; 18(1):111
  6. Photoperiodic control of seasonal growth is mediated by ABA acting on cell-cell communication
    Science 2018, 360(6385):212-214
  7. A multi-omics approach reveals function of Secretory Carrier-Associated Membrane Proteins in wood formation of​ ​​Populus​​ ​trees
    BMC Genomics. 2018, 19(1)
  8. AspWood: High-spatial-resolution transcriptome profiles reveal uncharacterized modularity of wood formation in Populus tremula
    Plant Cell. 2017, 29 (7):1585-1604
  9. Lineage-specific rediploidization is a mechanism to explain time-lags between genome duplication and evolutionary diversification
    Genome Biol. 2017; 18(1):111
  10. Gene co-expression network connectivity is an important determinant of selective constraint
    PLoS Genet. 2017, 13(4):e1006402
  11. NorWood: a gene expression resource for evo-devo studies of conifer wood development
    New Phytol. 2017, 216(2):482-494
  12. Extracting functional trends from whole genome duplication events using comparative genomics.
    Biol Proced Online. 2016  eCollection 2016. Review
  13. The Atlantic salmon genome provides insights into rediploidization
    Nature. 2016, 33(7602):200-
  14. Quantitative proteomics reveals protein profiles underlying major transitions in aspen wood development
    BMC Genomics 2016, 17(1):119
  15. Serendipitous Meta-Transcriptomics: The Fungal Community of Norway Spruce (Picea abies)
    PLoS One. 2015 Sep 28;10(9):e0139080
  16. The Plant Genome Integrative Explorer Resource:
    New Phytol. 2015, 208 (4):1149-1156
  17. Synergy: A Web Resource for Exploring Gene Regulation in Synechocystis sp. PCC6803
    PLoS One. 2014 Nov 24;9(11):e113496
  18. Populus tremula (European aspen) shows no evidence of sexual dimorphism
    BMC Plant Biol. 2014; 14(1):276
  19. ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa
    BMC Genomics. 2014; 15(1):106
  20. OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI-superoxide dismutase Populus plants
    BMC Genomics. 2013; 14:893
  21. Characterization of cytokinin signaling and homeostasis gene families in two hardwood tree species: Populus trichocarpa and Prunus persica
    BMC Genomics. 2013; 14:885
  22. The Norway spruce genome sequence and conifer genome evolution
    Nature 2013; 497(7451):579-584
  23. Miranda H, Cheregi O, Netotea S, Hvidsten TR, Moritz T, Funk C
    Co-expression analysis, proteomic and metabolomic study on the impact of a Deg/HtrA protease triple mutant in Synechocystis sp. PCC 6803 exposed to temperature and high light stress
    J. of Proteomics 2013, 78:94-311
  24. Onskog J, Freyhult E, Landfors M, Ryden P, Hvidsten TR
    Classification of microarrays; synergistic effects between normalization, gene selection and machine learning
    BMC Bioinformatics. 2011 Oct 7;12(1):390. [Epub ahead of print]
  25. Baba K, Karlberg A, Schmidt J, Schrader J, Hvidsten TR, Bako L, Bhalerao RP
    Activity-dormancy transition in the cambial meristem involves stage-specific modulation of auxin response in hybrid aspen
    Proceedings of the National Academy of Sciences of the United States of America: 2011 108:3418-3423
  26. Street NR, Jansson S, Hvidsten TR
    A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation
    BMC Plant Biology: 2011 11:13
  27. Wilczynski B, Hvidsten TR
    A computer scientist's guide to the regulatory genome
    Fundamenta Informaticae: 2010 103:323-332
  28. Freyhult E, Landfors M, Önskog J, Hvidsten TR, Rydén P
    Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
    BMC Bioinformatics: 2010 11:503, 14 pp
  29. Hvidsten TR, Lægreid A, Kryshtafovych A, Andersson G, Fidelis K, Komorowski J
    A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity
    PLoS One: 2009 4:e6266
  30. Björkholm P, Daniluk P, Kryshtafovych A, Fidelis K, Andersson R, Hvidsten TR
    Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts
    Bioinformatics: 2009 25(10):1264-1270
  31. Hvidsten TR, Kryshtafovych A, Fidelis K
    Local descriptors of protein structure: A systematic analysis of the sequence-structure relationship in proteins using short- and long-range interactions
    Proteins: Structure, Function, and Bioinformatics: 2009 75:870-884
  32. Wabnik K, Hvidsten TR, Kedzienska A, Van Leene J, De Jaeger G, Beemster GTS, Komorowski J, Kuiper MTR
    Gene expression trends and protein features effectively complement each other in gene function prediction
    Bioinformatics: 2009 25(3):322-330
  33. Fahlén J, Landfors M, Freyhult E, Bylesjö M, Trygg J, Hvidsten TR, Rydén P
    Bioinformatic strategies for cDNA-microarray data processing
    In: Batch Effects and Noise in Microarray Experiments: Sources and Solutions, Edited by A. Scherer, John Wiley & Sons 2009, 61-74
  34. Strömbergsson H, Daniluk P, Kryshtatovyck A, Fidelis K, Wikberg JES, Kleywegt GJ, Hvidsten TR
    Interaction model based on local protein substructures generalizes to the entire structural enzyme-ligand space
    Journal of Chemical Information and Modeling: 2008 48:2278-2288