A protein-protein interaction network aligner study in the multi-objective domain.

BACKGROUND AND OBJECTIVE: The protein-protein interaction (PPI) network alignment has proven to be an efficient technique in the diagnosis and prevention of certain diseases. However, the difficulty in maximizing, at the same time, the two qualities that measure the goodness of alignments (topological and biological quality) has led aligners to produce very different alignments. Thus making a comparative study among alignments of such different qualities a big challenge. Multi-objective optimization is a computer method, which is very powerful in this kind of contexts because both conflicting qualities are considered together. Analysing the alignments of each PPI network aligner with multi-objective methodologies allows you to visualize a bigger picture of the alignments and their qualities, obtaining very interesting conclusions. This paper proposes a comprehensive PPI network aligner study in the multi-objective domain. METHODS: Alignments from each aligner and all aligners together were studied and compared to each other via Pareto dominance methodologies. The best alignments produced by each aligner and all aligners together for five different alignment scenarios were displayed in Pareto front graphs. Later, the aligners were ranked according to the topological, biological, and combined quality of their alignments. Finally, the aligners were also ranked based on their average runtimes. RESULTS: Regarding aligners constructing the best overall alignments, we found that SAlign, BEAMS, SANA, and HubAlign are the best options. Additionally, the alignments of best topological quality are produced by: SANA, SAlign, and HubAlign aligners. On the contrary, the aligners returning the alignments of best biological quality are: BEAMS, TAME, and WAVE. However, if there are time constraints, it is recommended to select SAlign to obtain high topological quality alignments and PISwap or SAlign aligners for high biological quality alignments. CONCLUSIONS: The use of the SANA aligner is recommended for obtaining the best alignments of topological quality, BEAMS for alignments of the best biological quality, and SAlign for alignments of the best combined topological and biological quality. Simultaneously, SANA and BEAMS have above-average runtimes. Therefore, it is suggested, if necessary due to time restrictions, to choose other, faster aligners like SAlign or PISwap whose alignments are also of high quality.

Iterative Level-0: A new and fast algorithm to traverse mating networks calculating the inbreeding and relationship coefficients.

In population medical genetics, the study of autosomal recessive disorders in highly endogamous populations is a major topic where calculating the inbreeding and relationship coefficients on mating networks is crucial. However, a challenge arises when dealing with large and complex mating networks, making their traversal difficult during the calculation process. For this calculation, we propose using Iterative Level-0 (IL0) as a new and faster algorithm that traverses mating networks more efficiently. The purpose of this work is to explain in detail the IL0 algorithm and prove its superiority by comparing it with two algorithms based on the best-known algorithms in the area: Depth First Search (DFS) and Breadth First Search (BFS). A Cytoscape application has been developed to calculate the inbreeding and relationship coefficients of individuals composing any mating network. In this application, the IL0 proposal together with DFS-based and BFS-based algorithms have been implemented. Any user can access this freely available Cytoscape application (https://apps.cytoscape.org/apps/inbreeding) that allows the comparison between the IL0 proposal and the best-known algorithms (based on DFS and BFS). In addition, a diverse set of mating networks has been collected in terms of complexity (number of edges) and species (humans, primates, and dogs) for the experiments. The runtime obtained by the IL0, DFS-based, and BFS-based algorithms when calculating the inbreeding and relationship coefficients proved the improvement of IL0. In fact, a speedup study reflected that the IL0 algorithm is 7.60 to 127.50 times faster than DFS-based and BFS-based algorithms. Moreover, a scalability study found that the growth of the IL0 runtime has a linear dependence on the number of edges of the mating network, while the DFS-based and BFS-based runtimes have a quadratic dependence. Therefore, the IL0 algorithm can solve the problem of calculating the inbreeding and relationship coefficients many times faster (up to 127.50) than the two algorithms based on the famous DFS and BFS. Furthermore, our results demonstrate that IL0 scales much better as the complexity of mating networks increases.

Systematic analysis of putative phage-phage interactions on minimum-sized phage cocktails.

The application of bacteriophages as antibacterial agents has many benefits in the "post-antibiotic age". To increase the number of successfully targeted bacterial strains, phage cocktails, instead of a single phage, are commonly formulated. Nevertheless, there is currently no consensus pipeline for phage cocktail development. Thus, although large cocktails increase the spectrum of activity, they could produce side effects such as the mobilization of virulence or antibiotic resistance genes. On the other hand, coinfection (simultaneous infection of one host cell by several phages) might reduce the potential for bacteria to evolve phage resistance, but some antagonistic interactions amongst phages might be detrimental for the outcome of phage cocktail application. With this in mind, we introduce here a new method, which considers the host range and each individual phage-host interaction, to design the phage mixtures that best suppress the target bacteria while minimizing the number of phages to restrict manufacturing costs. Additionally, putative phage-phage interactions in cocktails and phage-bacteria networks are compared as the understanding of the complex interactions amongst bacteriophages could be critical in the development of realistic phage therapy models in the future.

Computational design of phage cocktails based on phage-bacteria infection networks.

The misuse and overuse of antibiotics have boosted the proliferation of multidrug-resistant (MDR) bacteria, which are considered a major public health issue in the twenty-first century. Phage therapy may be a promising way in the treatment of infections caused by MDR pathogens, without the side effects of the current available antimicrobials. Phage therapy is based on phage cocktails, that is, combinations of phages able to lyse the target bacteria. In this work, we present and explain in detail two innovative computational methods to design phage cocktails taking into account a given phage-bacteria infection network. One of the methods (Exhaustive Search) always generates the best possible phage cocktail, while the other method (Network Metrics) always keeps a very reduced runtime (a few milliseconds). Both methods have been included in a Cytoscape application that is available for any user. A complete experimental study has been performed, evaluating and comparing the biological quality, runtime, and the impact when additional phages are included in the cocktail.