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  • 1.
    Andorf, Sandra
    et al.
    Bioinformatics and Biomathematics Group, Genetics and Biometry Unit, Research Institute for the Biology of Farm Animals (FBN), Dummersdorf, Germany.
    Gärtner, Tanja
    Institute for Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Steinfath, Matthias
    Institute for Biochemistry and Biology, University of Potsdam, Potsdam-Golm, Germany.
    Witucka-Wall, Hanna
    Institute for Genetics, University of Potsdam, Potsdam-Golm, Germany.
    Altmann, Thomas
    Institute for Genetics, University of Potsdam, Potsdam-Golm, Germany.
    Repsilber, Dirk
    Bioinformatics and Biomathematics Group, Genetics and Biometry Unit, Research Institute for the Biology of Farm Animals (FBN), Dummersdorf, Germany.
    Towards systems biology of heterosis: a hypothesis about molecular network structure applied for the Arabidopsis metabolome2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 147157Article in journal (Refereed)
    Abstract [en]

    We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function. Interpreting heterosis as increased adaptability, our model predicts that the biological networks involved show increasing connectivity of regulatory interactions. A detailed analysis of metabolite profile data reveals that the increasing-connectivity prediction is true for graphical Gaussian models in our data from early development. This mirrors properties of observed heterotic Arabidopsis phenotypes. Furthermore, the model predicts a limit for increasing hybrid vigor with increasing heterozygosity--a known phenomenon in the literature.

  • 2.
    Repsilber, Dirk
    et al.
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Martinetz, Thomas
    Institute for Neuro- and Bioinformatics, University of Lübeck, Lüubeck, Germany.
    Björklund, Mats
    Department of Animal Ecology, Evolutionary Biology Centre, University of Uppsala, Uppsala.
    Adaptive dynamics of regulatory networks: size matters2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 618502Article in journal (Refereed)
    Abstract [en]

    To accomplish adaptability, all living organisms are constructed of regulatory networks on different levels which are capable to differentially respond to a variety of environmental inputs. Structure of regulatory networks determines their phenotypical plasticity, that is, the degree of detail and appropriateness of regulatory replies to environmental or developmental challenges. This regulatory network structure is encoded within the genotype. Our conceptual simulation study investigates how network structure constrains the evolution of networks and their adaptive abilities. The focus is on the structural parameter network size. We show that small regulatory networks adapt fast, but not as good as larger networks in the longer perspective. Selection leads to an optimal network size dependent on heterogeneity of the environment and time pressure of adaptation. Optimal mutation rates are higher for smaller networks. We put special emphasis on discussing our simulation results on the background of functional observations from experimental and evolutionary biology.

  • 3.
    Selbig, Joachim
    et al.
    University of Potsdam, Department of Biochemistry and Biology, Bioinformatics Chair, Potsdam, Germany.
    Steinfath, Matthias
    Genetics and Biometry unit, Research Institute for the Biology of Farm Animals (FBN), Dummerstorf, Germany.
    Repsilber, Dirk
    2Genetics and Biometry unit, Research Institute for the Biology of Farm Animals(FBN), Dummerstorf, Germany.
    Network structure and biological function: reconstruction, modeling, and statistical approaches2009In: EURASIP Journal on Bioinformatics and Systems Biology, ISSN 1687-4145, E-ISSN 1687-4153, article id 714985Article in journal (Refereed)
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