Journal of Computer Science & Systems Biology

The term robotics comes from robots. Many governments, commercial and individual bodies are working in the area of Robotics due to its importance in defence, personal and commercial uses. Much research has been put on it, but we could not succeed to a satisfactory level. In this area a small success is also seems as an achievement. But seeing the future commitments of robotics, we are in a need of perfect working robots. Robots are combination of machine plus human intelligence. Where the computer part is concerned; robots are always appreciated as timings of actions are fast; calculations are accurate, remote access is there. But we fail when we see the human part of these robots. Reason is very simple we want to constitute artificial brain into these robots and we cannot constitute something which infect we don't know. When biological science do not claim to have proper in depth working knowledge of brain how we can constitute it with the help of semiconductors and capacitors. Well, indeed it is a problematic challenge that must be sorted out. With this paper I am proposing the idea of combining robotics with biological brains, as artificial DNA's are recently added into our inventions and recent biology is not far away to produce artificial brains.

The Tuberculosis is the classical human mycobacterial disease, caused by Mycobacterium tuberculosis. The disease primarily affect the lung and causes pulmonary tuberculosis, as well as affect intestine, bone, joints, meninges, lymph nodes, skin and other tissue of the body, causing extra pulmonary tuberculosis. Thus there arises the need to understand the relationships among various parameters of these proteins for prediction of their classes, structures and functionality. The computational approaches for prediction of their classes are fast and economical therefore can be used to complement the existing wet lab techniques. Realizing their importance, in this paper an attempt has been made to correlate them with their amino acid composition and predict them with fair accuracy. This is a novel method where Mycobacterium Tuberculosis has been classified on the basis of amino acid composition using Support Vector Machine. The SVM has been implemented using SVM Light package. The method discriminates different strains of Mycobacterium Tuberculosis. The performance of the method was evaluated using 10-fold cross-validation where accuracy of 100% was obtained.

Now a day's customers are more interested in value-added mobile applications. In order to attract more customers to such mobile applications, a solid, secure and robust trading model is necessity. This paper proposes such a secure trading model named Mobile Electronic Payment (MEP) for the mobile commerce (m-commerce) over wireless mobile networks, which applies the emerging ID-based cryptography for key agreement and authentication. Our MEP attempts to ease the computational cost, reduce the memory space requirement in mobile devices, and meet the requirements for secure trading: avoidance of excessiveness and double expenditure, fairness, user ambiguity and privacy. Our design is transparent to the bearer networks and is of low deployment cost. We expect that our MEP provides a viable trading architecture model for the future mobile applications.

Metabolomics is the newly emerging field of ‘omics’ research.Its Comprehensive and simultaneous systematic determination of metabolite levels in the metabolome and their changes over time.This review outlines how metabolomics is emerging in today’s life. It presents metabolomics methodology and focuses on the current and potential applications of metabolomics in different diseases with particular attention to its use as a biomarker in diagnosis, prognosis, and therapeutic evaluation. We think this review will appeal to both translational researchers and clinicians as it reviews up-to-date evidence on the utility of metabolomics, an often poorly understood topic. The report also highlights the use of metabolomics in maximizing and sustaining revenues post-marketing and in the development of clinical diagnostics.