Journal of Computer Science & Systems Biology

eThread, a meta-threading and machine learning-based approach, is designed to effectively identify structural templates for use in protein structure and function modeling from genomic data. This is an essential methodology for high-throughput structural bioinformatics and critical for systems biology, where extensive knowledge of protein structures and functions at the systems level is prerequisite. eThread integrates a diverse collection of algorithms, therefore its deployment on a large multi-core system necessarily requires comprehensive profiling to ensure the optimal utilization of available resources. Resource profiling of eThread and the single-threading component algorithms indicate as wide range of demands with respect to wall clock time and host memory. Depending on the threading algorithm used, the modeling of a single protein sequence of up to 600 residues in length takes minutes to hours. Full meta-threading of one gene product from E. coli proteome requires ~12h on average on a single state-of-the-art computing core. Depending on the target sequence length, the subsequent three-dimensional structure modeling using eThread/Modeller and eThread/TASSER-Lite takes additional 1-3 days of computing time. Using the entire proteome of E. coli, we demonstrate that parallel computing on a multi-core system follows Gustafson-Barsis' law and can significantly reduce the production time of eThread. Furthermore, graphics processor units can speedup portions of the calculations; however, to fully utilize this technology in protein threading, a substantial code development is required. eThread is freely available to the academic and non-commercial community as a user-friendly web-service at http://www.brylinski.org/ethread. We also provide source codes and step-by-step instructions for the local software installation as well as a case study demonstrating the complete procedure for protein structure modeling. We hope that genome-wide high-throughput structural bioinformatics using eThread will significantly expand our knowledge of protein structures and their molecular functions and contribute to the thriving area of systems biology.

Parameter estimation is very important for the analysis of models in Systems Biology. Stochastic models are of increasing importance. However parameter estimation of stochastic models is still in the early phase of development and there is need for efficient methods to estimate model parameters from time course data which is intrinsically stochastic, only partially observed and has measurement noise.

In this article a fast and efficient method that is well established in the field of parameter estimation for systems of ordinary differential equations (ODE) is adapted to stochastic models. The focus is on the objective function which is shown to have advantageous properties that make it directly applicable to problems in systems biology. The proposed method can deal with stochastic systems where the behaviour qualitatively differs from the corresponding deterministic description. It works with measurements from a single realization of the stochastic process, and with partially observed processes including measurement errors. The objective function is deterministic, therefore a wide range of optimization methods, from derivative based methods to global optimization to Bayesian techniques can be applied. The computational effort required is comparable to similar methods for parameter estimation in deterministic models. To construct the objective function a multiple shooting procedure is used in which the continuity constraints are relaxed to allow for stochasticity. Unobserved states are treated by enlarging the optimization vector and using resulting values from the forward integration. Test functions are suggested that allow to monitor the validity of the approximations involved in this approach. The quality of the method is evaluated for some example models with a statistic of 50 estimates from 50 stochastic realizations. It is shown that the method performs well compared to established approaches.

Today Distributed Network Systems are very popular due to incompatibility and uncertainty of centralized network based systems. This paper presents peer to peer distributed system. The designed distributed consists of group of PC terminals as node of distributed system. The tool has been developed which is software computer application that handles the communication between nodes of Distributed Systems. COM port based distributed system comprises with distributed terminals. These terminals are internet worked by using the wired communication network. We designed and developed the software tool which comprises with two computer applications as a program. We have mentioned the pin out diagram of RS232 cable. Paper illustrates that how the data transmission takes place during the data transfers operations. We have transferred three database files which contains the text data. The performance is measured and analyzed. Paper also contains the advantages; disadvantages of COM Port based distributed system.?

Present work is aimed to identify and understand the inhibiting nature of Pyrimidine class of compounds to Enoyl acyl carrier proteinreductase (Enoyl-ACP reductase), which is one of the main receptor proteins used in drug discovery for screening anti-leprosy agents. Series of Pyrimidine based compounds virtually designed using the Molecular mechanic technique. The designed molecules were docked using with crystal structure of Enoyl-ACP reductase (PDB ID : 2NTV) using Autodock molecular docking software. The method uses rigid-protein and flexible ligand-techniques to acquire maximum conformations of ligand molecules. The docking results were evaluated using the acquired binding energy values for each ligand-protein complex. Those molecules having higher negative binding energy values with higher hydrogen bonds are selected for further analysis. The selected molecules show better hydrophobic, electrostatic and steric interactions with receptor protein. It is reported that the presence of –CH2OH at R1 and –C6H5 at R2 and R3 positions enhance the negative binding energy (∆G kcal mol-1) values. Particularly –OC6H5 at R1and –OH at R2 help in increasing the interactions between ligand and protein. The results show the molecular level interactions and inhibit the receptor protein.

Since data analysis using technical computational model has profound influence on interpretation of the final results, basic understanding of the underlying model surrounding such computational tools is required for optimal experimental design by target users of such tools. Despite wide variation of techniques associated with clustering, cluster analysis has become a generic name in bioinformatics and is seen to discover the natural grouping(s) of a set of patterns, points or sequences. The aim of this paper is to analyze k-means by applying a step-by-step k-means walk approach using graphic-guided analysis to provide clear understanding of the operational mechanism of the k-means algorithm. Scattered graph was created using theoretical microarray gene expression data which is a simplified view of a typical microarray experiment data. We designate the centroid as the first three initial data points and applied Euclidean distance metrics in the k-means algorithm leading to assignment of these three data points as reference point to each cluster formation. A test is conducted to determine if there is a shift in centroid before the next iteration is attained. We were able to trace out those data points in same cluster after convergence. We observed that, as both the dimension of data and gene list increases for hybridization matrix of microarray data, computational implementation of k-means algorithm becomes more rigorous. Furthermore, the understanding of this approach will stimulate new ideas for further development and improvement of the k-means clustering algorithm especially within the confines of the biology of diseases and beyond. However, the major advantage will be to give improved cluster output for the interpretation of microarray experimental results, facilitate better understanding for bioinformaticians and algorithm experts to tweak k-means algorithm for improved run-time of clustering.