Parameterized Algorithmics for Finding Exact Solutions of NP-Hard Biological Problems.

Fixed-parameter algorithms are designed to efficiently find optimal solutions to some computationally hard (NP-hard) problems by identifying and exploiting "small" problem-specific parameters. We survey practical techniques to develop such algorithms. Each technique is introduced and supported by case studies of applications to biological problems, with additional pointers to experimental results.

Developing fixed-parameter algorithms to solve combinatorially explosive biological problems.

Fixed-parameter algorithms can efficiently find optimal solutions to some computationally hard (NP-hard) problems. This chapter surveys five main practical techniques to develop such algorithms. Each technique is circumstantiated by case studies of applications to biological problems.

Simple and fast alignment of metabolic pathways by exploiting local diversity.

Motivation: An important tool for analyzing biological networks is the ability to perform homology searches, i.e. given a pattern network one would like to be able to search for occurrences of similar (sub)networks within a set of host networks.

Faspad: fast signaling pathway detection.

Unlabelled: Faspad is a user-friendly tool that detects candidates for linear signaling pathways in protein interaction networks based on an approach by Scott et al. (Journal of Computational Biology, 2006). Using recent algorithmic insights, it can solve the underlying NP-hard problem quite fast: for protein networks of typical size (several thousand nodes), pathway candidates of length up to 13 proteins can be found within seconds and with a 99.

Efficient detection of network motifs.

Motifs in a given network are small connected subnetworks that occur in significantly higher frequencies than would be expected in random networks. They have recently gathered much attention as a concept to uncover structural design principles of complex networks. Kashtan et al.

FANMOD: a tool for fast network motif detection.

Motifs are small connected subnetworks that a network displays in significantly higher frequencies than would be expected for a random network. They have recently gathered much attention as a concept to uncover structural design principles of complex biological networks. FANMOD is a tool for fast network motif detection; it relies on recently developed algorithms to improve the efficiency of network motif detection by some orders of magnitude over existing tools.