Computational or in silico modeling is a subset of systems biology that is concerned with integrating large datasets using complex data structures and algorithms to accurately recreate the behaviour of a system. It is used to determine how the internal components of complex biological systems interact with each other and with external systems. Computational modeling approaches have been applied to a number of biological systems at a variety of spatial and temporal scales to complement and enhance experimental research. For example computational modeling approaches are currently used in design and development of the next generation of cardiac pacemakers. Despite the attractive prospects of applying computational modeling systems in understanding complex events leading to conception and development of embryo, the application of in silico modeling to reproductive medicine is still in its infancy. In recent years we have tried to advance development of an in silico model of gamete interaction with female reproductive tract in our laboratory by creating a 3-D model of oviduct. Integration of this 3-D model with simple sperm movements has created a relatively simple in silico model that can help us to understand sperm transport in the female reproductive tract. We hope progression in this field will allow us to create simulations that are more complex and incorporate different factors in their predictions of sperm movements. These simulations will help us in understanding sperm transport within female reproductive tract and will be the first steps in reaching the ultimate goal of this project that is to predict the milieu of reproductive tract at the time of conception.

The morphology of porous silicon (p-Si) depends on several parameters such as the doping type and the carriers’ concentration of the crystalline silicon substrate. The electrolytes used in the p- Si fabrication also have an important role. The final structure determines if p-Si is luminescent or suitable for photonic applications. Experimental results on p-Si produced by electrochemical etching show that although the carriers are greatly reduced by the etching process, boron atoms remain in the bulk. The study of p-type porous silicon nanostructures by means of anab initio computational simulation might help to understand how boron atoms influence the p-Si final structure. Here, we report electronic and topological properties of ten p-type porous silicon structures as an extension of our previous paper on p-type crystalline silicon. Our results suggest that the boron atoms can not remain bonded on the porous surface but do so in the bulk. The presence of impurities changes the bond distance of their neighbors within a radius of 5 Ao. The energy of the models is essentially the same for all the boron positions in the silicon backbone. The high electronic density around the boron impurity could influence the trajectory of an HF ion entering a p-Si pore during the fabrication process.

The hydrodynamics of a new centrifugal tray named “Push valve centrifugal tray” has been studied. This tray uses a radial arrangement of the directional vapor valves (push valves) to create a centrifugal force and a pipe downcomer with a unique structure to distribute the liquid flow over the tray. A 3D computational fluid dynamics model was developed using an unsteady state Eulerian-Eulerian framework. The study examined dry and total pressure drop, velocity streamlines, water volume fraction, and radial velocity. Results showed that the “Push valve centrifugal tray” can control the high momentum of the gas flow at high capacities and use it for better two-phase contact and, ultimately, two-phase separation. The proposed new pipe downcomer also has unique characteristics, such as uniform liquid distribution and eliminating liquid stagnant points on the tray. The tray can be an effective and inexpensive way to debottleneck and retrofit the tray columns.

Introduction: Somatostatin (SST) is a versatile hormone that plays a key role in inhibiting the secretion of several pituitary hormones. It is well known that SST participates in the regulation of other essential proteins throughout the body. The abnormal function of the S ST protein is still not fully understood. In this study, the disease susceptible Single Nucleotide Polymorphisms (SNPs) in SST were classified by using different computational algorithms. Materials and Methods: Sequence-based and structure-based computational tools were employed to classify the most disease susceptible nsSNPs that would have the most harmful effects on SST protein. Docking and molecular dynamic simulations were performed to compare the ability of the normal SST and its most deleterious mutants to bind with corresponding SSTRs to assess the potential role of these nsSNPs to alter protein-protein interactions. Results: Two nsSNPs, namely L13P, and G104S, were considered to have the most severe functional consequences on the 3D structure of SS T. These results were confirmed by molecular dynamic simulations. Docking of SST and its mutant models with SST receptors (SSTR1-SSTR5) showed remarkable roles of both mutant L13P and G104S in altering the binding of SST with SSTR2 and SSTR5. Conclusions: The findings of the present study provide the first comprehensive in silico prediction for assessing the damaging effects of nsSNPs on SST, which may help in a better understanding of how the altered SST would impact the overall health of the body. This study may provide a platform to conduct large-scale experiments on the genetic polymorphism of SST.

A computational approach is proposed which is aimed at quantitatively assessing human recognition abilities when objects undergo different variations in the image. During the recent decades, as a result of its high accuracy and speed, human visual system has been considered an idolfora variety ofcomputational object recognition algorithms in machine vision. Therefore, quantification of its behavior in different situations can lead tobetter modeling algorithms. In this study, human ability is evaluated in an object recognition task in which object images underwent different levels of variation in lighting conditions, pose, size and position. To do this, a variation-controlled object image dataset is generated and presented to humans as well as to a computational model of visual cortex. The computational model is used to measure the effect of each variation on object recognition. Human behavioral results show a decline in recognition performance when objects underwent pose variation. The performance suppression is shown to be the result of disability of untangling object representations in highlevels of pose variation. Quantitatively speaking, images which underwent variations in lighting, pose, size and position, experienced respectively 0. 57, 0. 33, 0. 55 and 0. 73 of representational enhancement travelling from pixel to visual cortex space.

Ginsenoside F1 (G-F1) is biologically an active compoud isolated from Korean Panax ginseng Meyer. In the present study, the potential therapeutic effect of G-F1 were investigated by computational target fishing approaches including ADMET prediction, biological activity prediction from chemical structure, molecular docking, and molecular dynamics methods. Results were suggested to express the biological activity of G-F1 against p38 MAP kinase protein. The p38 MAP kinase protein is an important signal transducing enzyme involved in many cellular regulations, including signaling pathways, pain and inflammation. Numerous studies are shown that an abnormal activation of p38 MAP kinase leads to variety of diseases. The pharmacokinetic result proves that G- F1 can act non-toxic drug like molecule. In addition, molecular level interaction results of G- F1 with p38 MAP kinase active (binding) sites residues clearly defines its inhibitory action on p38 MAP kinase. Further, molecular dynamics study also supported p38 MAP kinase and G-F1 structural stability. Findings from out study will assist to discover the active drug like molecules from Panax ginseng with help of molecular modeling techniques.

Scorpion venom is a rich source of toxins which have great potential to develop new therapeutic agents. Scorpion chloride channel toxins (ClTxs), such as Chlorotoxin selectively inhibit human Matrix Methaloproteinase-2 (hMMP-2). The inhibitors of hMMP-2 have potential use in cancer therapy. Three new ClTxs, meuCl14, meuCl15 and meuCl16, derived from the venom transcriptome of Iranian scorpion, M. eupeus (Buthidea family), show high sequence identity (71. 4%) with Chlorotoxin. Here, 3-D homology model of new ClTxs were constructed. The models were optimized by Molecular Dynamics simulation based on MDFF (molecular dynamics flexible fitting) method. New ClTxs indicate the presence of CSα β folding of other scorpion toxins. A docking followed by steered molecular dynamics (SMD) simulations to investigate the interactions of meuCl14, meuCl15, and meuCl16 with hMMP-2 was applied. The current study creates a correlation between the unbinding force and the inhibition activities of meuCl14, meuCl15 and meuCl16 to shed some insights as to which toxin may be used as a drug deliverer. To this aim, SMD simulations using Constant Force Pulling method were carried out. The SMD provided useful details related to the changes of electrostatic, van de Waals (vdW), and hydrogen-bonding (H-bonding) interactions between ligands and receptor during the pathway of unbinding. According to SMD results, the interaction of hMMP-2 with meuCl14 is more stable. In addition, Arginine residue was found to contribute significantly in interaction of ClTxs with hMMP-2. All in all, the present study is a dynamical approach whose results are capable of being implemented in structure-based drug design.

Turbocharger systems enhance the engine power and efficiency, reduce its pollution, and downsize the engine volume. As the significance of exploiting turbochargers in gasoline engines is surging among automobile manufacturers, the necessity of improvement in the system components becomes more critical. This paper investigates the impacts of a turbocharger turbine bypass and wastegate geometry alterations on the performance and the flow field characteristics via numerical simulations. The numerical method verification and mesh independence study are performed for the original geometry. The simulation results of the altered geometries indicate that better alignment between the bypassed and the bulk flows leads to higher efficiency of the turbocharging system. In addition, the bent or elbow-shaped and protruded turbine walls immediately downstream of the wheel are found to be unfavorable. It is also uncovered that if the wastegate shape and the housing walls are designed in such a way that the effects of ensuing vortices are minimized, it improves the stage efficiency, which is desirable for two-stage turbochargers. Furthermore, a novel manufacturable design is proposed in this study, which increases the efficiency and useful power by 19. 9% and 6. 7%, respectively.