Browsing by Author "Moreno, Liana Amaya"

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    Vester's sensitivity model for genetic networks with time-discrete dynamics
    (Springer International Publishing, 2014) Moreno, Liana Amaya; Defterli, Özlem; Fuegenschuh, Armin; Weber, Gerhard Wilhelm
    We propose a new method to explore the characteristics of genetic networks whose dynamics are described by a linear discrete dynamical model x(t+1) = Ax(t). The gene expression data x(t) is given for various time points and the matrix A of interactions among the genes is unknown. First we formulate and solve a parameter estimation problem by linear programming in order to obtain the entries of the matrix A. We then use ideas from Vester's Sensitivity Model, more precisely, the Impact Matrix, and the determination of the Systemic Roles, to understand the interactions among the genes and their role in the system. The method identifies prominent outliers, that is, the most active, reactive, buffering and critical genes in the network. Numerical examples for different datasets containing mRNA transcript levels during the cell cycle of budding yeast are presented
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    Citation - WoS: 2
    Citation - Scopus: 5
    Vester's Sensitivity Model for Genetic Networks With Time-Discrete Dynamics
    (Springer international Publishing Ag, 2014) Moreno, Liana Amaya; Defterli, Ozlem; Fuegenschuh, Armin; Weber, Gerhard-Wilhelm; Amaya Moreno, Liana; Fügenschuh, Armin
    We propose a new method to explore the characteristics of genetic networks whose dynamics are described by a linear discrete dynamical model x(t+1) = Ax(t). The gene expression data x(t) is given for various time points and the matrix A of interactions among the genes is unknown. First we formulate and solve a parameter estimation problem by linear programming in order to obtain the entries of the matrix A. We then use ideas from Vester's Sensitivity Model, more precisely, the Impact Matrix, and the determination of the Systemic Roles, to understand the interactions among the genes and their role in the system. The method identifies prominent outliers, that is, the most active, reactive, buffering and critical genes in the network. Numerical examples for different datasets containing mRNA transcript levels during the cell cycle of budding yeast are presented.