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Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.

We present a model of online Goal Babbling for the bootstrapping of sensorimotor coordination. By modeling infants' early goal-directed movements we show that inverse models can be bootstrapped within a few hundred movements even in very high-dimensional sensorimotor spaces. Our model thereby explains how infants might initially acquire reaching skills without the need for exhaustive exploration, and how robots can do so in a feasible way. We show that online learning in a closed loop with exploration allows substantial speed-ups and, in high-dimensional systems, outperforms previously published methods by orders of magnitude.

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For quasi-freestanding 2H-TaS 2 in monolayer thickness grown by in situ molecular beam epitaxy on graphene on Ir(111), we find unambiguous evidence for a charge density wave close to a 3 × 3 periodicity. Using scanning tunneling spectroscopy, we determine the magnitude of the partial charge density wave gap. Angle-resolved photoemission spectroscopy, complemented by scanning tunneling spectroscopy for the unoccupied states, makes a tight-binding fit for the band structure of the TaS 2 monolayer possible. As hybridization with substrate bands is absent, the fit yields a precise value for the doping of the TaS 2 layer. Additional Li doping shifts the charge density wave to a 2 × 2 periodicity. Unexpectedly, the bilayer of TaS 2 also displays a disordered 2 × 2 charge density wave. Calculations of the phonon dispersions based on a combination of density-functional theory, density-functional perturbation theory, and many-body perturbation theory enable us to provide phase diagrams for the TaS 2 charge density wave as functions of doping, hybridization and interlayer potentials, and offer insight into how they affect lattice dynamics and stability. Our theoretical considerations are consistent with the experimental work presented and shed light on previous experimental and theoretical investigations of related systems.