With the continuing increase in availability of biological data and improvements to biological models, biological network analysis has become a promising area of research. An emerging technique for the analysis of biological networks is through network alignment. Network alignment has been used to calculate genetic distance, similarities between regulatory structures, and the effect of external forces on gene expression, and to depict conditional activity of expression modules in cancer. Network alignment is algorithmically complex, and therefore we must rely on heuristics, ideally as efficient and accurate as possible. The majority of current techniques for network alignment rely on precomputed information, such as with protein sequence alignment, or on tunable network alignment parameters, which may introduce an increased computational overhead. Our presented algorithm, which we call Node Fingerprinting (NF), is appropriate for performing global pairwise network alignment without precomputation or tuning, can be fully parallelized, and is able to quickly compute an accurate alignment between two biological networks. It has performed as well as or better than existing algorithms on biological and simulated data, and with fewer computational resources. The algorithmic validation performed demonstrates the low computational resource requirements of NF.
Molecular photoswitches like spiropyrans derivatives offer exciting possibilities for the development of analytical platforms incorporating photo-responsive materials for functions such as light-activated guest uptake and release and optical reporting on status (passive form, free active form, guest bound to active form). In particular, these switchable materials hold tremendous promise for microflow-systems, in view of the fact that their behaviour can be controlled and interrogated remotely using light from LEDs, without the need for direct physical contact. We demonstrate the immobilisation of these materials on microbeads which can be incorporated into a microflow system to facilitate photoswitchable guest uptake and release. We also introduce novel hybrid materials based on spiropyrans derivatives grafted onto a polymer backbone which, in the presence of an ionic liquid, produces a gel-like material capable of significant photoactuation behaviour. We demonstrate how this material can be incorporated into microfluidic platforms to produce valve-like structures capable of controlling liquid movement using light.
Due to recent advancements in high-throughput sequencing technologies, progressively more protein-protein interactions have been identified for a growing number of species. Subsequently, the protein-protein interaction networks for these species have been further refined. The increase in the quality and availability of these networks has in turn brought a demand for efficient methods to analyze such networks. The pairwise alignment of these networks has been moderately investigated, with numerous algorithms available, but there is very little progress in the field of multiple network alignment. Multiple alignment of networks from different organisms is ideal at finding abnormally conserved or disparate subnetworks. We present a fast and accurate algorithmic approach, Node Handprinting (NH), based on our previous work with Node Fingerprinting, which enables quick and accurate alignment of multiple networks. We also propose two new metrics for the analysis of multiple alignments, as the current metrics are not as sophisticated as their pairwise alignment counterparts. To assess the performance of NH, we use previously aligned datasets as well as protein interaction networks generated from the public database BioGRID. Our results indicate that NH compares favorably with current methodologies and is the only algorithm capable of performing the more complex alignments.
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