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Parametrisierte Algorithmik bioinformatischer Probleme

Fachliche Zuordnung Theoretische Informatik
Förderung Förderung von 2009 bis 2016
Projektkennung Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 162571619
 
Erstellungsjahr 2018

Zusammenfassung der Projektergebnisse

We have developed efficient & exact algorithms for numerous computationally hard problems in bioinformatics. We have given particular focus to developing algorithms which perform fast in practice. Noteworthy examples include the Cluster Editing problem, where our algorithms can solve instances exactly with tens of thousands of vertices and edge modifications; and the Maximum Colorful Subtree problem, where our code solves millions of instances every day for the interpretation of metabolomics data. All problems have direct applications in bioinformatics and biology, be it clustering, phylogenetics, biochemistry, metabolomics, or the analysis of genome rearrangements. On the theoretical side, we have developed numerous parameterized algorithms with improved worst-case running times for NP-hard problems; for other problems, we have proven that parameterized algorithms are very unlikely to exist. In bioinformatics research, the objective function of a problem is usually only a “crutch” used to find the optimum structure, whereas the value of the objective function has little or no meaning. Even elaborate heuristics for an NP-hard problem, which are capable of finding solutions with objective function value very close to the optimum, can result in solutions which are structurally very dissimilar from the optimum structure. We were able to show that this is not only a theoretical possibility, but happens regularly for real-world instances. This underlines the importance of finding exact solutions for bioinformatics problems; the structure of heuristic solutions, including local search heuristics such as Markov chain Monte Carlo, may deviate significantly from the optimal solution.

Projektbezogene Publikationen (Auswahl)

 
 

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