The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs).

Hepatitis C virus (HCV) infection is a worldwide healthcare problem; however, traditional treatment methods have failed to cure all patients, and HCV has developed resistance to new drugs. Systems biology-based analyses could play an important role in the holistic analysis of the impact of HCV on hepatocellular metabolism. Here, we integrated HCV assembly reactions with a genome-scale hepatocyte metabolic model to identify metabolic targets for HCV assembly and metabolic alterations that occur between different HCV progression states (cirrhosis, dysplastic nodule, and early and advanced hepatocellular carcinoma (HCC)) and healthy liver tissue. We found that diacylglycerolipids were essential for HCV assembly. In addition, the metabolism of keratan sulfate and chondroitin sulfate was significantly changed in the cirrhosis stage, whereas the metabolism of acyl-carnitine was significantly changed in the dysplastic nodule and early HCC stages. Our results explained the role of the upregulated expression of BCAT1, PLOD3 and six other methyltransferase genes involved in carnitine biosynthesis and S-adenosylmethionine metabolism in the early and advanced HCC stages. Moreover, GNPAT and BCAP31 expression was upregulated in the early and advanced HCC stages and could lead to increased acyl-CoA consumption. By integrating our results with copy number variation analyses, we observed that GNPAT, PPOX and five of the methyltransferase genes (ASH1L, METTL13, SMYD2, TARBP1 and SMYD3), which are all located on chromosome 1q, had increased copy numbers in the cancer samples relative to the normal samples. Finally, we confirmed our predictions with the results of metabolomics studies and proposed that inhibiting the identified targets has the potential to provide an effective treatment strategy for HCV-associated liver disorders.

One of the most significant graph data analysis tasks is graph classification, as graphs are complex data structures used for illustrating relationships between entity pairs. Graphs are essential in many domains, such as the description of chemical molecules, biological networks, social relationships, etc. Real-world graphs are complicated and large. As a result, there is a need to find a way to represent or encode a graph’s structure so that it can be easily utilized by machine learning models. Therefore, graph embedding is considered one of the most powerful solutions for graph representation. Inspired by the Doc2Vec model in Natural Language Processing (NLP), this paper first investigates different ways of (sub)graph embedding to represent each graph or subgraph as a fixed-length feature vector, which is then used as input to any classifier. Thus, two supervised classifiers—a deep neural network (DNN) and a convolutional neural network (CNN)—are proposed to enhance graph classification. Experimental results on five benchmark datasets indicate that the proposed models obtain competitive results and are superior to some traditional classification methods and deep-learning-based approaches on three out of five benchmark datasets, with an impressive accuracy rate of 94% on the NCI1 dataset.

The minimum dominating set (MDSet) comprises the smallest number of graph nodes, where other graph nodes are connected with at least one MDSet node. The MDSet has been successfully applied to extract proteins that control protein–protein interaction (PPI) networks and to reveal the correlation between structural analysis and biological functions. Although the PPI network contains many MDSets, the identification of multiple MDSets is an NP-complete problem, and it is difficult to determine the best MDSets, enriched with biological functions. Therefore, the MDSet model needs to be further expanded and validated to find constrained solutions that differ from those generated by the traditional models. Moreover, by identifying the critical set of the network, the set of nodes common to all MDSets can be time-consuming. Herein, the authors adopted the minimisation of metabolic adjustment (MOMA) algorithm to develop a new framework, called maximisation of interaction adjustment (MOIA). In MOIA, they provide three models; the first one generates two MDSets with a minimum number of shared proteins, the second model generates constrained multiple MDSets ( $k$-MDSets), and the third model generates user-defined MDSets, containing the maximum number of essential genes and/or other important genes of the PPI network. In practice, these models significantly reduce the cost of finding the critical set and classifying the graph nodes. Herein, the authors termed the critical set as the $k$-critical set, where $k$ is the number of MDSets generated by the proposed model. Then, they defined a new set of proteins called the $(k-1)$-critical set, where each node belongs to $(k-1)$ MDSets. This set has been shown to be as important as the $k$-critical set and contains many essential genes, transcription factors, and protein kinases as the $k$-critical set. The $(k-1)$-critical set can be used to extend the search for drug target proteins. Based on the performance of the MOIA models, the authors believe the proposed methods contribute to answering key questions about the MDSets of PPI networks, and their results and analysis can be extended to other network types.

Due to the widespread distribution of coronavirus and the existence of a massive quantity of data on social networking sites, particularly Twitter, there was an urgent need to develop a model that evaluates users’ emotions and determines how they feel about the pandemic. However, the absence of resources to assist Sentiment Analysis (SA) in Arabic hampered the completion of this endeavor. This work presents the ArSentiCOVID lexicon, the first and largest Arabic SA lexicon for COVID-19 that handles negation and emojis. We design a lexicon-based sentiment analyzer tool that depends mainly on the ArSentiCOVID lexicon to perform a three-way classification. Furthermore, we employ the sentiment analyzer to automatically assemble 42K annotated Arabic tweets for COVID-19. We conduct two experiments. First, we test the effect of applying negation and emoji rules to the created lexicon. The results indicate that after applying the emoji, negation, and both rules, the F-score improved by 2.13%, 4.13%, and 6.13%, respectively. Second, we applied an ensemble method that combines four feature groups (n-grams, negation, polarity, and emojis) as input features for eight Machine Learning (ML) classifiers. The results reveal that Random Forest (RF) and Support Vector Machine (SVM) classifiers work best, and that the four feature groups combined are best for representing features produced the maximum accuracy of (92.21%), precision (92.23%), recall (92.21%), and F-score (92.23%) with 3.2% improvement over the base model.

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we developed a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions (16304 reactions in total). The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data revealed that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.