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Sci Tech
Making sense out of genomic data
IT IS common knowledge that the availability of DNA sequences of the human genome is one of the most powerful tools in disease research. An access to the DNA sequence of an individual promises to reduce drug side effects and to tailor medicine to the individual's genetic makeup.
It was believed by many in the field that the integration of the enormous amount of `genomics' data alone would quickly lead to the correlation of phenotype (clinical manifestations) with genotype (variations in gene sequence).
That goal is still far off, however, as the bulk of these `high-throughput' data are examined out of context — the underlying biological mechanisms.
One cannot understand the basis for a disease without the understanding of the alternative splicing forms of the related genes, the proteins for which they code, the complex networks of protein interactions that result, multiple levels of gene regulation and expression, and correlations to healthy and diseased tissue or clinical data.
The complexity of our biology requires a systemic understanding of genomic data. This new field of systems biology is rapidly becoming a leading approach to understanding human biology.
``Identifying all the genes and proteins in an organism is like listing all the parts of an airplane.
While such a list provides a catalogue of the individual components, by itself it is not sufficient to understand the complexity underlying the engineered object. We need to know how these parts are assembled to form the structure of the airplane'' (H.Kitano, 2002, Science, 295, 1662-1664).
Pharmaceutical and biotech companies have accumulated tremendous amounts of disease-related high-throughput data that need to be converted into knowledge and utilised in drug discovery.
All available `data integration platforms' suffer one major flaw: they lack an organizing `functional backbone' that is consistent with the fundamental mechanisms of biology. Indeed, it seems logical that in order to correlate gene sequences with clinical outcome, one first needs to understand the machinery of a living cell. A cell, like an automobile, is made of functional parts like genes and proteins. These parts can be connected to form functional pathways which in turn can be networked to create a blueprint of functions for a cell, a tissue, or an organ .A biotechnology start up called GeneGo is developing a technology called Systems Reconstruction (SR) to provide this needed functional backbone to organise the onslaught of genomics data into meaningful cellular roadmaps of networked pathways.
Dr. Tatiana Nikolskaya founder of GeneGo has mastered the art of metabolic reconstruction technology for eukaryotic organisms. This ultimately led to the creation of the Systems Reconstruction (SR).
The SR concept has been implemented into a proprietary computational platform of content databases, analytical tools and algorithms by GeneGo. Recently, the company launched the first of these, called MetaCoreT. This is, a database which combines analytical tools, data content, and algorithms for understanding the complex interconnected pathways that are affected in common human diseases.
MetaCoreT is the first commercially available computational platform for human Systems Biology and has been designed to assist pharmaceutical researchers in the areas of target selection, prioritisation, and validation, as well as biomarker identification.
It integrates all levels of cellular functionality — from the membrane receptors to signal transduction, transcription factors, and effect or networks — using in silico models to explore and to predict how different disease states, and different levels of the metabolites and the xenobiotics, can affect the performance of the system.
A unique feature of the MetaCoreT Platform is its ability to allow the researcher to integrate and visualise data from many different biological levels and types of experiments within the same system, and in the context of the existing knowledge in the literature and the extensive systems biology databases provided with the platform. It allows exploration of the relationships between what is happening at the level of the genome (such as genetic variation), transcriptome, proteome (including post-translational modifications and protein interactions), and metabolome.
According to Dr. Tatiana Nikolskaya, CEO of GeneGo, ``The data analysis tools available until now only allowed the researcher to look at one level of biological data at a time, such as RNA expression information. Clearly there are many downstream events, and complex interactions and reactions in biological systems that must be considered as well.''
``The `enterprise-wide' data storage systems have not provided the answer either,'' she added, ``as they organize data by workflow or project, and fail to provide a framework or ontogeny for how all the data is interrelated that is consistent with the fundamental mechanisms of biology. This is where MetaCoreT's strength lies.''
N. N. Sachitanand
in Bangalore
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