Журнал компьютерных наук и системной биологии

Systems biology explains how higher level properties of complex biological systems arise from the interactions among their parts. This requires a combination of concepts from many disciplines, including biology, computer science, applied mathematics, physics and engineering. The importance and its various implementations in various fields like proteomics, genomics, metabolomics, docking studies etc., recent works and advancements are portrayed in this review article.

The collection of global metabolic data and their interpretation (both spectral and biochemical) using modern spectroscopic techniques and appropriate statistical approaches, are known as `metabolomics’. This review covers research on metabolomics, ranging from the development of specialized chemical analytical techniques to the construction of databases and methods for metabolic simulation. Furthermore we have also outlined the recent developments in elucidating the system-level functions of the bioconstituents of living organisms. Metabolomics has the potential to serve an important role in diagnosis and management of human conditions. As such the purpose of this systematic review is to summarize existing literature on metabolomics and its various techniques in terms of diagnostic accuracies and distinguishing metabolites. This article has also highlighted the novelty of the field of metabolomics with various detection methods. Here metabolomic methodologies are discussed briefly followed by a more detailed review of the use of metabolomics in integrated applications where metabolomics information has been combined with other “omic” data sets (proteomics, transcriptomics) to enable greater understanding of a biological system.

An emerging field for the analysis of biological systems is the study of the complete protein complement of the genome, the ‘proteome’. Proteomics is defined as a scientific approach used to elucidate all protein species within a cell or tissue. Emerging proteomic technologies have promised for early diagnosis and in advancing treatment directions. Application of these technologies has produced new biomarkers, diagnostic approaches, and understanding of disease biology. Here in this review we have outlined potential implications for clinical proteomics focused on applied research activities. This review presents a general survey of the recent development in array technologies from a proteomics perspective. The significance of informatics in proteomics will gradually increase because of the advent of high-throughput methods relying on powerful data analysis. Lastly, an attempt has been made to present novel biological entities named the bioinformatics tools developed to analyse the large protein– protein interaction networks they form, along with several new perspectives of the field. Bioinformatics is an integral part of proteomics research.

As a result of human genome project a large burst of genomic data comes. Researchers are trying to correlate this sequenced data to find out variations, which will help to study the effect of variations on disease progression. Single nucleotide polymorphism is one of the genetic markers which are most widely used in genetic association studies of a population. SNPs are DNA sequence variations that occur when a single nucleotide (A, T, C, or G) in the genome sequence is altered. SNPs found within a coding sequence are of particular interest to researchers because they are more likely to alter the biological function of a protein. Occasionally, SNPs can cause disease and can be used to search and isolate diseased gene. The SNPs found in this region and its linkage disequilibrium analysis to find out the effect of SNPs found and there correlation. However it is much easier, cheaper and faster than in vitro analysis, computational analysis will provide an insight to probable disease causing SNPs having some functional value which can be assayed in vitro. Present computational analysis is to find out SNPs in the chromosome 1.

A full understanding of biological function emerges only if we are able to integrate all relevant information at multiple levels of organization to recreate dynamic interactions. These dynamic interactions cannot be recreated purely by experimental observation and the only feasible approach is to develop mathematical and computational models which couple together the underlying complex interacting non-linear processes. It is thus particularly encouraging to revisit the force field parameterization on the basis of extended QM calculations (structures, energetics and phase transitions) in conjunction with available experimental information. Further levels of approximation can be built with the combination of important advances in methodology and computer codes. Moreover, the error controlled strategy applied for optimizing an empirical method for phospholipids is novel in this domain. Combining these strengths in an approach that builds membrane models, integrating adequate atomistic and electronic information, will represent a huge advance towards describing real membrane systems on a solid basis.

Antibiotic discovery aimed at conventional targets such as proteins and nucleic acids faces challenges from mutations and antibiotic resistance. Small molecule metabolites, however, can be considered resistant to change, as they do not undergo rapid mutations. Developing analogs or scavengers of essential microbial metabolites as antibiotics is a promising strategy that can delay drug resistance. The objective of this work was to identify microbial metabolites that are most suitable targets for antimicrobial discovery. We performed extensive literature mining and systems level pathway analysis to identify bacterial metabolites that fulfill the criteria for drug targets. The BioCyc interactive metabolic pathway maps and Pathway Tools software were used to corroborate our finding. We identified ten metabolites as potential candidates for developing novel antibiotics. These metabolites are Lipid II, meso-diaminopimelate, pantothenate, shikimate, biotin, L-aspartyl-4-phosphate, dTDP-?-L-rhamnose, UDP-Dgalacto- 1,4-furanose, des-N-acetyl mycothiol, and Siroheme. The article describes the selection criteria, analysis of metabolic pathways, and the potential role of each of the ten metabolites in therapeutic intervention as broadspectrum antibiotics with emphasis on M. tuberculosis. Our study revealed previously unexplored targets along with metabolites that are well established in antibiotic discovery. Identification of established metabolites strengthen our analyses while the newly discovered metabolites could lead to novel antimicrobials.