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Auflistung nach Autor:in "Friedel, Caroline"

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  • Zeitschriftenartikel
    Algorithmische Systembiologie mit Petrinetzen – Von qualitativen zu quantitativen Systemmodellen
    (Informatik-Spektrum: Vol. 32, No. 4, 2009) Birzele, Fabian; Csaba, Gergely; Erhard, Florian; Friedel, Caroline; Küffner, Robert; Petri, Tobias; Windhager, Lukas; Zimmer, Ralf
    Die algorithmische Systembiologie ist ein aktuelles Teilgebiet der Bioinformatik. Petrinetze [28] werden seit Jahrzehnten für die Modellierung von Systemen und seit ca. 15 Jahren auch in der netzwerk-orientierten Bioinformatik [17, 27] verwendet. In der algorithmischen Systembiologie werden Petrinetze einerseits zur Repräsentation von biologischem Wissen in Form von qualitativen Netzwerkmodellen und andererseits für die Analyse ihrer dynamischen Eigenschaften und ihre quantitative Simulation eingesetzt. Die Auswahl relevanter Teilnetze aus großen qualitativen Netzen und ihre Umsetzung in quantitative Modelle erfordern neue methodische Ansätze.
  • Zeitschriftenartikel
    Bioinformatics advances biology and medicine by turning big data troves into knowledge
    (Informatik Spektrum: Vol. 40, No. 2, 2017) Gagneur, Julien; Friedel, Caroline; Heun, Volker; Zimmer, Ralf; Rost, Burkhard
    Informatics and life sciences (molecular biology and medicine) are undoubtedly the most rapidly growing and most dynamic endeavors of modern society. Computational biology or bioinformatics describes the rising field that integrates those endeavors. Over the last 50 years, the field has shifted focus from the study of individual genes and proteins (1967–1994), to that of entire organisms (19952015), and more recently to studying the diversity of populations. The increasing amount of big data created by the life sciences is challenging already by its volume alone. Even more challenging is the high intrinsic complexity of the data. In addition, the data are changing at a breathtaking speed; most data generated in 2016 probes conditions that had not been anticipated 15 years ago. Precision medicine and personalized health are just two descriptors of how modern biology will become relevant for improving our health. All new drugs have at some point have bioinformatics tools in their development. Similarly, there would not be any digital medicine without the bioinformatics expertise or any advances without mastering machine learning tools turning raw data into valuable insights and decisions.