Biointelligence

December 14, 2009

Applications of Systems Biology in Drug Discovery

Filed under: Bioinformatics,Chemoinformatics,Systems Biology — Biointelligence: Education,Training & Consultancy Services @ 4:33 am
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Till date we have made a lot of posts on Systems Biology, its applications and it scope. Indeed, Systems Biology has brought a big revolution in cell biology and pathway analysis. When seen in combination with treatment of diseases and drug discovery, it proves even more handy. Here we discuss Systems Biology in combination with drug discovery.

The goal of modern systems biology is to understand physiology and disease from the level of molecular pathways, regulatory networks, cells, tissues, organs and ultimately the whole organism. As currently employed, the term ‘systems biology’ encompasses many different approaches and models for probing and understanding biological complexity, and studies of many organisms from bacteria to man. Much of the academic focus is on developing fundamental computational and informatics tools required to integrate large amounts of reductionist data (global gene expression, proteomic and metabolomic data) into models of regulatory networks and cell behavior. Because biological complexity is an exponential function of the number of system components and the interactions between them, and escalates at each additional level of organization.

There are basically three advances in the practical applications of systems biology to drug discovery. These are:

1. Informatic integration of ‘omics’ data sets (a bottom-up approach)

Omics approaches to systems biology focus on the building blocks of complex systems (genes, proteins and metabolites). These approaches have been adopted wholeheartedly by the drug industry to complement traditional approaches to target identification and validation, for generating hypotheses and for experimental analysis in traditional hypothesis-based methods.

2. Computer modeling of disease or organ system physiology from cell and organ response level information available in the literature (a top-down approach to target selection, clinical indication and clinical trial design).
The goal of modeling in systems biology is to provide a framework for hypothesis generation and prediction based on in silico simulation of human disease biology across the multiple distance and time scales of an organism. More detailed understanding of the systems behavior of intercellular signaling pathways, such as the identification of key nodes or regulatory points in networks or better understanding of crosstalk between pathways, can also help predict drug target effects and their translation to organ and organism level physiology.

3.  The use of complex human cell systems themselves to interpret and predict the biological activities of drugs and gene targets (a direct experimental approach to cataloguing complex disease-relevant biological responses).

Pathway modeling as yet remains too disconnected from systemic disease biology to have a significant impact on drug discovery. Top-down modeling at the cell-to-organ and organism scale shows promise, but is extremely dependent on contextual cell response data. Moreover, to bridge the gap between omics and modeling, we need to collect a different type of cell biology data—data that incorporate the complexity and emergent properties of cell regulatory systems and yet ideally are reproducible and amenable to storing in databases, sharing and quantitative analysis.

This is how Systems Biology has aided in Drug Discovery Research and paved its path to cure many vital diseases.

Read our other posts on Systems Biology – https://biointelligence.wordpress.com/category/systems-biology/

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