BackgroundDeveloping suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions.ResultsIn this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores.ConclusionsThe experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Polycyclic aromatic hydrocarbons such as benzo[a]pyrene (BaP) that activate the aryl hydrocarbon receptor (Ahr) pathway, and endocrine disruptors acting through the estrogen receptor pathway are among environmental pollutants of major concern. In this work, we exposed Atlantic cod (Gadus morhua) precision-cut liver slices (PCLS) to BaP (10 nM and 1000 nM), ethynylestradiol (EE2) (10 nM and 1000 nM), and equimolar mixtures of BaP and EE2 (10 nM and 1000 nM) for 48 h, and performed RNA-Seq based transcriptome mapping followed by systematic bioinformatics analyses. Our gene expression analysis showed that several genes were differentially expressed in response to BaP and EE2 treatments in PCLS. Strong up-regulation of genes coding for the cytochrome P450 1a (Cyp1a) enzyme and the Ahr repressor (Ahrrb) was observed in BaP treated PCLS. EE2 treatment of liver slices strongly up-regulated genes coding for precursors of vitellogenin (Vtg) and eggshell zona pellucida (Zp) proteins. As expected, pathway enrichment and network analysis showed that the Ahr and estrogen receptor pathways are among the top affected by BaP and EE2 treatments, respectively. Interestingly, two genes coding for fibroblast growth factor 3 (Fgf3) and fibroblast growth factor 4 (Fgf4) were up-regulated by EE2 in this study. To our knowledge, the fgf3 and fgf4 genes have not previously been described in relation to estrogen signaling in fish liver, and these results suggest the modulation of the FGF signaling pathway by estrogens in fish. The signature expression profiles of top differentially expressed genes in response to the single compound (BaP or EE2) treatment were generally maintained in the expression responses to the equimolar binary mixtures. However, in the mixture-treated groups, BaP appeared to have anti-estrogenic effects as observed by lower number of differentially expressed putative EE2 responsive genes. Our in-depth quantitative analysis of changes in liver transcriptome in response to BaP and EE2, using PCLS tissue culture provides further mechanistic insights into effects of the compounds. Moreover, the analyses demonstrate the usefulness of PCLS in cod for omics experiments.

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.

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Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present “DyCluster”, a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster.

Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models’ interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions.AvailabilityThe datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

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The availability of genome sequences, annotations and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model. Author summaryGenome-scale metabolic models (GEMs) are constructed based upon reconstructed networks that are carried out by an organism. The underlying biochemical knowledge in such networks can be transformed into mathematical models that could serve as a platform to answer biological questions. The availability of high-throughput biological data, including genomics, proteomics, and metabolomics data, supports the generation of such models for a large number of organisms. Nevertheless, challenges arise for non-model species which are typically less annotated. In this paper, we discuss these June 23, 2020 1/14 challenges and possible solutions in the context of generation of a draft liver reconstruction of Atlantic cod (Gadus morhua). We also show how experimental data, here gene expression data, can be mapped to the resulting model to understand the metabolic response of cod liver to environmental toxicants. 2 high-throughput genomic, proteomic, and metabolomic data, genome-scale metabolic 3 models (GEMs) have become fundamental tools in the systems biology of metabolism. 4 To develop a GEM, metabolic genes and their product enzymes are assembled into a 5 network of metabolic reactions carried out by an organism [42]. Such networks can be 6 used as a platform to answer relevant biological questions, by transforming biochemical 7 knowledge into a mathematical format and computing appropriate physiological 8 states [41, 46]. Using Boolean logic representation, gene-protein-reaction (GPR) 9 associations depict direct connections between genotype and metabolic capability [39]. 10 GEMs have a wide scope of applications, among which are the contextualization of 11 omics data, hypothesis-driven discovery, support for metabolic engineering, modeling 12 interact...