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Auflistung nach Autor:in "Rantanen, Ari"

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  • Konferenzbeitrag
    Ab initio prediction of molecular fragments from tandem mass spectrometry data
    (German Conference on Bioinformatics, 2006) Heinonen, Markus; Rantanen, Ari; Mielikäinen, Taneli; Pitkänen, Esa; Kokkonen, Juha; Rousu, Juho
    Mass spectrometry is one of the key enabling measurement technologies for systems biology, due to its ability to quantify molecules in small concentrations. Tandem mass spectrometers tackle the main shortcoming of mass spectrometry, the fact that molecules with an equal mass-to-charge ratio are not separated. In tandem mass spectrometer molecules can be fragmented and the intensities of these fragments measured as well. However, this creates a need for methods for identifying the generated fragments. In this paper, we introduce a novel combinatorial approach for predicting the structure of molecular fragments that first enumerates all possible fragment candidates and then ranks them according the cost of cleaving a fragment from a molecule. Unlike many existing methods, our method does not rely on hand-coded fragmentation rule databases. Our method is able to predict the correct fragmentation of small-to-medium sized molecules with high accuracy.
  • Konferenzbeitrag
    Planning isotopomer measurements for estimation of metabolic fluxes
    (German Conference on Bioinformatics 2005 (GCB 2005), 2005) Rantanen, Ari; Mielikäinen, Taneli; Rousu, Juho; Ukkonen, Esko
    Flux estimation by using isotopomer information of metabolites is currently the only method that can give quantitative estimates of the activity of metabolic pathways. However, the measurement of isotopomer distributions of intermediate metabolites is costly and tedious with current technologies. In this paper we study the question of finding the smallest subset of metabolites to measure that ensure an adequate level of the isotopomer information. We study the computational complexity of this optimization problem in the case of the so-called positional enrichment data, give exact and fast heuristic solutions and evaluate empirically the efficacy of the proposed methods.