Journal of Computer Science & Systems Biology

Genetic algorithms are widely used for solving a multitude of complex problems. Methods for increasing the genetic algorithms accuracy are of great importance. One such method consist of using the fixed point theory combined with an improved genetic algorithm. This paper aims to provide such an algorithm with an increased convergence accuracy. In order to increase the algorithm operates on a simplicial triangulation over the searching space. We use the crossover, mutation and increase dimension genetic operators to obtain a convergent population that contains only fully labelled simplexes. In order to obtain better results, we use a custom increase dimension operator that significantly boosts the overall fitness. The increase dimension operator converts non-labelled individuals into nearly labelled/fully labelled ones. The solution, the global optimum point, is obtained after applying the Hessian matrix onto the final population. Our obtained results clearly show an improvement over existing work due to implementation particularities of the algorithm.

Cancer chemoresistance (including adaptive resistance) has emerged as a barrier in developing successful chemotherapeutic strategies. We use Monte Carlo simulation based single cell analysis to provide insights into the regulatory mechanisms for generating chemoresistance under TRAIL (death ligand) induction. Based on stochastic computer simulations we elucidate systems biology of cancer cell apoptosis (at the level of single cells) and search for an optimal death ligand from a group of recently studied TRAIL affinity variants. In addition to assessing the population level behavior in cell death activation under induction of TRAIL/TRAIL-variant, Monte Carlo approach allows us to analyze cell-to-cell stochastic fluctuations in time-to-death that has implications for generating resistant cancer cells. We discuss application of Monte Carlo simulations in the context of developing more personalized approaches in treating various cancers. Initial findings indicate single cell in silico approaches can be utilized for disease subtype classification and in characterizing a given tumor, and, for finding an optimal strategy (such as network modules to target and ligands needed) in targeting a given tumor.

The wireless devices have become the integral part of our everyday life. These devices are being used for many purposes such as for Internet and Telecommunication. Being a theoretical approach, the purpose of this study is to investigate the effects of wireless devices on human body. These devices emit the harmful radiations which cause the diseases like male infertility, brain tumor, hearing impairment, fetus, effect on eyes, etc. Besides, these radiations severely affect other various parts of human body. This study investigated three major diseases, i.e., brain tumor, male infertility and hearing impairment. The survey/investigations have been conducted from number of medical practitioners working in various hospitals and medical centers of District Sukkur and Khairpur Mirs�. The results, collected through interviews and survey, show the intensity of harm of different wireless devices; Mobile phone is the most effective device with 96%, Bluetooth Device 32%, Laptop 54%, Tablet PC 14% and Wireless router 20%.

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Despite regular medication management, many patients continue to have seizures. Thus, there is a need of more tailored therapy and consequently more sophisticated and accurate seizure diagnostic tools. Background EEG activity is used by physicians for finding information regarding dysfunction of associated central nervous system and risk of seizures. Considering its importance, background activity is exploited in this work for computation of relative entropy, Cauchy-Schwartz divergence, change in median absolute deviation, change in normalized coefficient of variation and change in Katz fractal dimension. The results are highly promising and comparative study suggests that considering background activity outperforms the other techniques.

Testicular cancer develops in one or both of the testicles in young men. Rab8b is a member of the Rab small G protein family, participates in intracellular trafficking events at the site of the adherence junction dynamics in the testis. Overexpression of Rab8b and loss of functioning adherence junction accelerates the tumorigenesis in testis. In the present work, the computer aided high throughput virtual screening studies are applied to identify potent leads for human Rab8b protein. The homology model of Rab8b of 207 amino acid residues chain length was evaluated based on the crystal structure of an appropriate template, and reveals the presence of 6 α-helices and 6 β-strands. The energy of the generated model was minimized and the model was validated using ProSA PROCHECK and ERRAT server tools. The active site was identified using the computational binding site prediction tools like CASTp, efindsite seversand Sitemap of Schrödinger, which show that the residues (GLU33 to GLN60) are important for binding. The molecular interactions of Rab8b with its natural substrate Rabin 8, were examined by in silico protein-protein docking studies using patchDock tool, and the results were corroborated with the active site identified from the computational tools. Virtual screening studies were carried out with ligand databases using Glide docking program of Schrödinger suite. The ligands obtained from XP docking with high Glide score and Glide energy, were subjected to QikProp module to predict their ADME properties. These ligands, based on the pharmacokinetic properties, which are new entities, were considered as novel potent inhibitors in cancer therapy.