IT Driven ‘Omics’ Technology in Health Management

IT Driven ‘Omics’ Technology in Health Management

Rini Raphael

Research Scholar, Department of Zoology Periyar E V R College (Autonomous) Tiruchirapalli, Tamil Nadu.

Abstract Emergence of information technology into the field of biology has promoted cosmic understanding of complex nature of life at all levels of understanding including sub cellular, cellular, molecular as well as system level. Knowledge at the gene, protein, protein- protein interaction leads to the different interdisciplinary areas such as bioinformatics, nanoinformatics and other areas with the suffixes omics. Omic technologies espouse a holistic view of the molecules that make up a cell, tissue or organism and can be applied for the greater understanding of normal physiological processes in screening, diagnosis and prognosis as well as aiding our understanding of the aetiology of diseases

Keywords Information technology, omics technology, health management

I.INTRODUCTION

Emergence of information technology into the field of biology has promoted cosmic understanding of complex nature of life. As Cheney (2003) states: Understanding the role of IT for science is important because of the central role of science in todays information society. Advances in science affect economic performance and the achievement of societal goals, from health to national security. As a result, it is important for policy makers to understand how IT affects the quality and productivity of science [1].Even though information technology has been used in the relm of biological science for a long time, many of them were hesitating to adopt computational models in life science. But in the present scenario, attitude towards the application of computer assisted technologies in bioscience changed transforming biology with a new visage. Lander et al. (1991) observed that biology had been going through major changes driven by computing for the previous ten years [2]. Since the discovery in the 1950s of how genetic information is coded in DNA, the biosciences have increasingly become much more dependent on IT. Now biology is much more reliant on information technology as it renders its services at all levels of understanding including sub cellular, cellular, molecular as well as system level.

II.OMIC TECHNOLOGIES

Omic technologies adopt a holistic view of the molecules that make up a cell, tissue or organism. They are aimed primarily at the universal detection of genes (genomics), mRNA (transcriptomics), proteins (proteomics) and metabolites (metabolomics) in a specific biological sample in a non-targeted and non-biased manner. This can also be referred to as high-dimensional biology; the integration of

these techniques is called systems biology[3,4]. Omic technology can be applied not only for the greater understanding of normal physiological processes but also in disease processes where they play a role in screening, diagnosis and prognosis as well as aiding our understanding of the aetiology of diseases. [5]

1. Genomics

   Genomics is the methodical study of an organisms genome. There are different databases that allow the submission, storage, analysis and data mining of gene sequences. International Nucleotide Sequence Databases (INSD) consisiting of DNA Data Bank of Japan (National Institute of Genetics), EMBL (European Bioinformatics Institute) , GenBank (National Center for Biotechnology Information) are repositories for nucleotide sequence that exchange new and updated data on a daily[6]. BioGraph, ConsensusPathDB, Entrez, mGen containing four of the world leading databases GenBank, Refseq, EMBL and DDBJ. EcoCyc a database that describes the genome and the biochemical machinery of the model organism, Ensembl, Repbase, integrall,EuPathDB[6] are some of the other commonly entrusted genomic databases

   .

   Microarray technology that advanced significantly in recent years has made gene analysis faster and easier.DNA microarrays measure differences in DNA sequence between individuals and the expression of thousands of genes can be analyzed simultaneously. They can reveal abnormalities such as chromosomal insertions and deletions or abnormal chromosomal numbers in a process called comparative genomic hybridization. [5] Availability of genome sequences of pathogens has provided a incredible amount of information that can be constructive in drug target and vaccine target recognition

2. Proteomics

   The proteome is defined as the set of all expressed proteins in a cell, tissue or organism.[7] Proteomics aims to characterise information flow within the cell and the organism, through protein pathways and networks,[8] with the eventual aim of understanding the functional relevance of proteins.[9]Proteomics Identifications Database (PRIDE), ProteomeScout, MitoMiner,are some of the major proteomics databases and UniProt , Swiss-Prot, PROSITE, Pfam etc are some of the protein sequence databases widely accepted all over the world. The proteome is a dynamic reflection of both genes and the environment and is thought to hold special promise for biomarker discovery because proteins are most likely to be ubiquitously affected in disease and disease

   response.[10 ]This is reflected in the many protein disease biomarkers already available (e.g. CA125 and alpha- fetoprotein)

3. Metabolomics

   Metabolomics can generally be defined as the study of global metabolite profiles in a system (cell, tissue or organism) under a given set of conditions.[11] The metabolome is the final downstream product of gene transcription and, therefore, changes in the metabolome are amplified relative to changes in the transcriptome and the proteome.[12] Studying the reaction of various organisms to diverse stresses and environments at the genetic, transcript, protein, and metabolite levels by means of different methods and comparing these results with those of other organisms will fortify their incorporation into a systems biology scaffold. Recent progress in this field reveals that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights [13]

4. Pharmacogenomics

   Pharmacogenomics the intersection of genomics and pharmacology is the study of the role of inheritance in individual variation in drug response which can potentially be used to individualise and optimise drug therapy.[14] Pharmacogenomics is particularly significant for oncology, as severe systemic toxicity and unpredictable effectiveness are hallmarks of cancer therapies.[14] Systems approaches to anomalies such as cancer, cardiovascular disease and obesity give the chance to facilitate greatly the achievement of selecting new targets for treatments and drug development.

   1. COMPUTATIONAL DRUG DESIGNING

      The completion of the human genome project has revolutionised the field of drug-discovery against threatening human pathogens [15].The strategies for drug design and development are progressively shifting from the genetic approach to the genomic approach [16]. For Example Some ALL patients have evidently characterized chromosomal aberrations and the practical cost of these aberrations are not fully understood. Bioconductor tools were used to build up a new characterization of the disparity in gene expression between ALL patients with two specific forms of chromosomal translocation[17]. It is evident that diseases in biological organisms are caused by malfunction of a protein which is a product of a particular gene in performing its function in general. The control of the biological system is made possible when a drug molecue interacts with the active site of the target protein [18].

      Computational drug design involves finding the molecular structures that will create a strong interaction with the active site of the target protein and chemical activities that leads to protein folding. Modified forms of Gleeveca drug against Chronic myeloid leukaemia is a product of computational drug designing, that is effective against later stages of CML. [19]. The influence of the completed genome sequence on vaccine design approaches has been effective for organisms like Mycobacterium tuberculosis [20] Molecular profiling through metabolomics has made its presence in

      gynaecological oncology in which proteomic profiling was initially applied to human serum to find out ovarian cancer by Petricoin et al.[21] Gadducci et al.[22] that give a good overview of the markers associated with ovarian, endometrial and cervical cancer and the role proteomic profiling has to play. CA125 is the most reliable marker in ovarian cancer. In recent times the autoantibody against the S100A7 protein has been found to be elevated in early and late-stage ovarian cancer but the clinical usefulness has yet to be investigated[23] .There is Computer Aided Drug Design (CADD) Three dimensional database system runs to find out best suited pharmacophore for its availability, solubility and toxicity determination through what atom connected with other atom in what manner and how these two atoms close together in spatial three dimensional sense [24].

   2. CONCLUSION

      Information technology has great impact on diverse area of medical and biological research as well as practice for its new direction and perspective of ideas with cost effective design and protocol. Development of new softwares is enabling betterment of medical field. There are so many options to test drugs, vaccines using computational modelings of biological systems. As natural as well as anthropogenic reasons causes pollution, new diseases are emerging even at the genetic level perplexing the whole humanity. Here Computational simulations, biomodellings are inevitable for the better understanding, and treatment of the diseased world. Computational studies in biology make it possible to detect the rules of protein structure, function and protein-protein interactions. These will certainly speed up the drug design.

   3. REFERENCES

1. D.W Cheney, (2003) Implications of Information Technologies in Geo- and Bio-Sciences: A Literature Review available at http://www.sri.com/policy/csted/reports/sandt/it.

2. E.S Landers, R. Landgridge, and D.M. Saccocio, Computing in Molecular Biology: Mapping and interpreting Biological Information. In IEEE Computer 24, No. 11: pp. 6-13, 1991.

3. D.B Kell, The virtual human: towards a global systems biology of multiscale, distributed biochemical network models in Iubmb Life, 59, pp.68995, 2007. Doi: 10.1080/15216540701694252.

4. H.V Westerhoff, B.O Palsson. The evolution of molecular biology into systems biology, in Nature Biotechnol, 22, pp.124952, 2004. doi:10.1038/nbt1020.

5. P. R Horgan, L. C Kenney Omic technologies:genomics, transcriptomics,proteomics and metabolomics in The Obstetrician & Gynaecologist 13,pp.189195, 2011.

6. lList of biological databases(30.june.2015)Wikipedia available at https://en.wikipedia.org/wiki/List of biological databases, accessed on 24.7.2015

7. D. Theodorescu, H. Mischak, Mass spectrometry based proteomics in urine biomarker discovery, in Wld J Urol, 25, pp.43543, 2007. doi:10.1007/s00345-007-0206-3

8. E. Petricoin, K Zoon, E. Kohn, J. Barrett, L. Liotta, Clinical proteomics: translating benchside promise into bedside reality, Nat Rev,1,pp.68395,2002. doi:10.1038/nrd891

9. A.Vlahou, M .Fountoulakis, Proteomic approaches in the search for disease biomarkers, J Chromatogr B Analyt Technol Biomed Life Sci, 814, pp.1119, 2005. doi:10.1016/j.jchromb.2004.10.024

10. N. Rifai, M.A Gillette, S.A. Carr, Protein biomarker discovery and validation: the long and uncertain path to clinical utility in Nat Biotechnol, 24, pp.97183, 2006. doi:10.1038/nbt1235

11. R.Goodacre, S.Vaidyanathan, W.B Dunn, G.G Harrigan, D.B Kell, Metabolomics by numbers: acquiring and understanding global

    metabolite data, in Trends Biotechnol,22,pp.245 52,2004.doi:10.1016/j.tibtech.2004.03.007

12. E. Urbanczyk-Wochniak, A. Luedemann, J. Kopka, J .Selbig, U. Roessner-Tunali, L .Willmitzer, Parallel analysis of transcript and metabolic profiles: a new approach in systems biology, in EMBO Rep,4, pp.98993,2003.doi:10.1038/sj.embor.embor944

13. Denkert et al, Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery,in Genome Med,4(4),37,2012.

14. W.E Evans, M., V Relling, Moving towards individualized medicine with pharmacogenomics, in Nature,429,pp.4648, 2004. doi:10.1038/nature02626

15. J.W Watters, H.L McLeod, Cancer pharmacogenomics: current and future applications,in Biochim Biophys Acta Rev Cancer ,1603,99 111,2003.

16. L. Miesel, J .Greene,T. A. Black, Genetic strategies for antibacterial drug discovery. Nature Rev. Genet 4, pp.442-456, 2003.

17. M.Y Galperin, E. V. Koonin, searching for drug targets in microbial genomes. in Curr. Opin.Biotechnol. 10, pp.571-57, 1999.

18. Gentleman et al , Bioconductor: open software development for computational biology and bioinformatics ,in Genome Biology, 5(10) pp.80, 2004

19. Attila Gürsoy, Using Computers and Biology Towards Drug Discovery and Design,in kufrontier second issue,2007.

20. D. L Montgomery,. (). Tuberculosis vaccine design: Influence of the completed genome sequence.in Brief.Bioinform. 1, pp.289-296,2000.

21. E.F Petricoin, A.M Ardekani, B.A Hitt, P. J Levine, V.A Fusaro, S.M Steinberg, Use of proteomic patterns in serum to identify ovarian cancer,in Lancet,359,pp.5727, 2002. doi:10.1016/S0140- 6736(02)07746-2.

22. A.Gadducci, S. Cosio, A .Carpi, A. Nicolini, A.R Genazzani, Serum tumor markers in the management of ovarian, endometrial and cervical cancer, Biomed Pharmacother;58:24- 8,2004.doi:10.1016/j.biopha.2003.11.003.

23. A.Gagnon, J.H Kim, J.O Schorge, B .Ye, Liu B, K. Hasselblatt, Use of a combination of approaches to identify and validate relevant tumor- associated antigens and their corresponding autoantibodies in ovarian cancer patients.in Clin Cancer Res,14,76471, 2008.doi:10.1158/1078- 0432.CCR-07-0856.

24. Rapaka R. S, Hawk R.L, Medications Development: Drug Discovery, Databases, and Computer-Aided Drug Design. NIDA Research Monograph.134,pp. 129-131, 1993.