Volume 1 Issue 1
Authors: Alberto Arteta; Luis Fernando Mingo; Nuria Gomez
Abstract: Within the membrane computing research field, there are many papers about software simulations and a few about resources utilization. The use of proper algorithms can make a big difference in how the resources utilization improves. The key is finding a way to optimize the resources usage and the resolution of complex problems at the same time when using bio-inspired models. This is one of the most important goals that researchers face presently. Transition P-systems (Membrane computing) are bio-inspired, parallel and non-deterministic systems which simulate membranes behaviour when processing information. This paper describes a method that finds the way in between optimizing the use of computer resources and optimizing the time to solve complex problems; therefore, these techniques not only improve the best performance obtained so far by the membrane systems when solving complex problems but also improve the utilization of computer resources.
Keywords: Membrane Systems, Parallel Models, Bio-Inspired Systems, Natural Computing, Evolution Rules Application
Authors: Andigoni Malousi; Ioanna Chouvarda; Sofia Kouidou; Nicos Maglaveras
Abstract: DNA methylation is a tissue-specific, sequence-dependent and dynamic process, regulated by complex developmental mechanisms. Recent genome-wide studies provide compelling evidence for long-range sequence-dependence of this process, thus necessitating the computational identification of cytosine methylation and its deviations at individual CpG sites, processes which might reveal the activation of various epigenetic regulatory mechanisms. The present study deals with the prediction of methylation at single CpG sites located both in CpG islands and non-CpG islands, relying solely on the intrinsic information of the 401nt sequences centred at the candidate methylated CpG cytosines. The proposed method was evaluated on human methylation data available by the Human Epigenome Project. The proposed Sequential Minimization Optimization classifier, when trained with the most informative features, gives highly accurate identification of the hypermethylated and hypomethylated sequences (Sn=85.35%, Sp=90.72%). Post-classification analysis of the correctly and mistakenly classified methylation targets revealed that the classification accuracy varies depending on the co-localization of the CpG sites with genomic features involved in gene expression, such as CpG islands and exonic/intronic sequences. This observation reinforces the importance of incorporating site-specific features in the predictive model and the development of site-specific classifiers. Datasets and source code are available upon request.
Keywords: Predictive Model; DNA Methylation; Local Context; Sequential Minimization Optimization; Post-Classification Analysis
Authors: Amin Mojiri; Behrouz Larki; Shahin Gavanji
Abstract: Many transition metals are essential for the nutrition of plants. However, because of the potential toxicity, strict regulation of these ions is also critical for plant cells. In order to maintain metal homeostasis, plants have evolved diverse mechanisms to ensure appropriate concentrations of essential metal ions, as well as to remove nonessential forms by controlling their accumulation, distribution and sequestration. The purpose of this article is to study the bioinformatics of metal binding sites in some proteins in helianthus annuus, oryza sativa, brassica juncea, silene paradoxa, pisum sativum, silene vulgaris and triticum aestivum. Amino acid sequences of Type 2 Metallothionein in Helianthus annuus, Metallothionein in Oryza sativa (Metallothionein-like protein 1A), Metallothionein-like protein type 2, MT2-28 in Brassica juncea, Type 1 Metallothionein in Pisum sativum, Type 2 Metallothionein in Pisum sativum, Metallothionein in Silene vulgaris, Metallothionein in Triticum aestivum and Metallothionein in Silene paradoxa was taken from NCBI. Bioinformatic prediction in protein sequences using web server MetalDetector showed that the protein, in its 13 amino acids (Cysteine), in the sites “3, 4, 8, 10, 14, 16, 20, 23, 63, 65, 67, 71 and 76” possess interaction property with heavy metals. Sequence of amino acids containing metal binding sites property in helianthus annuus and oryza sativa had been checked using T-Coffee and MultAlin. Results showed that the mentioned sequences have similarities in many amino acids and these similar sequences possess interaction property with heavy metals.
Keywords: Prediction; Metal; Bioinformatics; Helianthus Annuus; Oryza Sativa
Authors: Georgina Mirceva; Slobodan Kalajdziski; Andrea Kulakov
Abstract: Protein molecules are involved in many processes in the cells of living organisms, thus the knowledge about their functions in these processes is very important. There are various methods for annotating protein structures. The databases that hold knowledge about the proteins’ annotations cover a portion of the known protein structures. Therefore, one of the most attractive issues in bioinformatics community is automatic discovery of protein functions. In our previous research we proposed several methods for protein tertiary structure retrieval. In this paper we introduce a method for determining protein functions based on our protein ray-based descriptor. First, the query structure is compared with the training structures. Then, the annotations of the query are determined based on the annotations of its nearest neighbours. The results show that our method achieves 0.82 micro precision and 0.74 micro recall. We also compare the annotations inferred by our methods with the annotations with evidence code IEA, and against the annotations with evidence codes different from IEA. Similarly, we compare the annotations inferred by our method with the annotations stored in InterPro and UniProtKB databases.
Keywords: Protein Structure; Protein Function; Protein Ray-Based Descriptor; Nearest Neighbours; Multi-Label Learning
Authors: André Rodrigues Gurgel da Silva; Camila Alves Zané; Diego de Freitas Coêlho; Edgar Silveira; Elias Basile Tambourgi
Abstract: The ATPS modelling and optimization have been studied for decades after researchers started using them for extraction and purification of biomolecules from any complex extract. Aqueous Two-Phase System is a well-known liquid-liquid extraction technique in which both phases are mainly constituted by water and that has experienced an intensive growing since Albertson described it. In this article, a literature review is going to be presented, raising a set of information about case studies in which most commonly used thermodynamic models and artificial neural networks were applied to similar scenarios and evaluating which one could be more appropriated to prediction of ATPS extraction or predict its parameters based on its successful studies.
Keywords: Thermodynamic Modelling; Artificial Neural Networks; Aqueous Two-Phase Systems