Genome-scale models (GEMs) are structured representations of a target organism’s metabolism based on existing genetic, biochemical, and physiological information. These models store the available knowledge of the physiology and metabolic behaviour of organisms and summarise this knowledge in a mathematical description. Flux balance analysis uses GEMs to make predictions about cellular metabolism through the solution of a constrained optimisation problem. The gene inactivity moderated by metabolism and expression (GIMME) approach further constrains FBA by means of transcriptomics data. The underlying idea is to deactivate those reactions for which transcriptomics is below a given threshold. GIMME uses a unique threshold for the entire cell. Therefore, non-essential reactions can be deactivated, even if they are required to meet the production of a certain external metabolite, because of their low associated transcript expression values. Here, we propose a new approach to enable the selection of different transcriptomics thresholds for different cell compartments or modules, such as cellular organelles and specific metabolic pathways. The approach was compared with the original GIMME in the analysis of a number of examples related to yeast batch fermentation for the production of ethanol from glucose or xylose. In some cases, the original GIMME results in biological unfeasibility, while the compartmentalised version successfully recovered flux distributions. The method is implemented in the python-based toolbox MEWpy and can be applied to other metabolic studies, opening the opportunity to obtain more refined and realistic flux distributions, which explain the connections between genotypes, environment and phenotypes.
Due to global warming, coral reefs have been directly impacted with heat stress, resulting in mass coral bleaching. Within the coral species, some are more heat resistant, which calls for an investigation towards interventions that can enhance coral resilience for other heat-susceptible species. Studying heat-resistant corals’ microbial communities can provide a potential insight to the composition of heat-susceptible corals and how their resilience is achieved. So far, techniques to efficiently classify such vast microbiome data are not sufficient. In this paper, we present an optimal machine learning based pipeline for identifying the biomarker bacterial composition of heat-tolerant coral species versus heat-susceptible ones. Through steps of feature extraction, feature selection/engineering, and machine leaning training, we apply this pipeline on publicly available 16S rRNA sequences of corals. As a result, we have identified the correlation based feature selection filter and the Random Forest classifier to be the optimal pipeline, and determined biomarkers that are indicators of thermally sensitive corals.
COVID-19 can mutate rapidly, resulting in new variants which could be more malignant. To recognize the new variant, we must identify the mutation parts by locating the nucleotide changes in the DNA sequence of COVID-19. The identification is by processing sequence alignment. In this work, we propose a method to perform multiple sequence alignment via deep reinforcement learning effectively. The proposed method integrates a progressive alignment approach by aligning each pairwise sequence center to deep Q networks. We designed the experiment by evaluating the proposed method on five COVID-19 variants: alpha, beta, delta, gamma, and omicron. The experiment results showed that the proposed method was successfully applied to align multiple COVID-19 DNA sequences by demonstrating that pairwise alignment processes can precisely locate the sequence mutation up to 90% . Moreover, we effectively identify the mutation in multiple sequence alignments fashion by discovering around 10.8% conserved region of nitrogenous bases.
Neoantigen detection is the most critical step in developing personalized vaccines in cancer immunology. However, neoantigen detection depends on the correct pMHC binding and presentation prediction. Furthermore, transformers and transfer learning have a high impact on NLP tasks. Since amino acids and proteins are like words and sentences, the pMHC binding and presentation prediction problem could be considered an NLP task. Thus, this work proposed using a BERT architecture pre-trained in 250 million proteins (ESM-1b), then we will use a BiLSTM in cascade. Our preliminary results evaluated a small BERT (TAPE) model achieving 0.80 of AUC on the netMHCpanII3.2 dataset.
Cancer immunology is a new alternative to traditional cancer treatments like radiotherapy and chemotherapy. There are some strategies, but neoantigen detection for developing cancer vaccines are methods with a high impact in recent years. However, neoantigen detection depends on the correct prediction of peptide-MHC binding. Furthermore, transformers are considered a revolution in artificial intelligence with a high impact on NLP tasks. Since amino acids and proteins could be considered like words and sentences, the peptide-MHC binding prediction problem could be seen as a NLP task. Therefore, in this work, we performed a systematic literature review of deep learning and transformer methods used in peptide-MHC binding and presentation prediction. We analyzed how ANNs, CNNs, RNNs, and Transformer are used.
