Research

Computational Life Sciences

Computational Life Sciences is a very important area in science and technology.

 

This group covers a wide variety of research areas which is being carried out from TCS Innovation Labs - Pune (TRDDC) and TCS Innovation Labs - Hyderabad.

The Research Areas under TCS Innovation Labs - Pune (TRDDC) include:

The Research Areas under TCS Innovation Labs - Hyderabad are listed below:

  • Biomedical text-mining for enhanced healthcare
  • Human genome analysis
  • Computational structural biology
  • Drug development
  • Human Microbiome and Health
    The microbial communities (i.e. microbiomes) residing in various sites of our body have been implicated in the onset and progression of several diseases and metabolic disorders. Understanding these microbiomes and deciphering their interaction patterns with the human host is expected to aid in the development of novel therapeutic and / or diagnostic strategies. The focus of our group is to understand these microbiomes using a metagenome informatics approach. In this regard, we are involved in several microbiome studies in collaboration with reputed national and international partners (from industry, academia and healthcare service providers). The key focus is on investigating diseases and metabolic disorders like Diabetes, Malnutrition, Preterm birth, Vitiligo and Colorectal Cancer.  Translational microbiomics research initiatives aimed at evaluating the efficacy of pre-/pro-biotic nutraceutical supplements and drug candidates are also underway.                         

    Systems Biology of Tuberculosis
    Nearly a third of the world’s population is infected with Mycobacterium tuberculosis, the causative agent of tuberculosis (TB). A majority of the infected population host the pathogen in a latent state, showing no active signs of infection, but remain susceptible to the disease caused by a re-activated pathogen. The latency (and subsequent re-activation) is directed through a complex network of interactions between the invading pathogen and the human host. One of the research focus of our group is to decipher these complex regulatory interactions and uncovering new therapeutic strategies to treat TB infection. We are involved in a National level TB-research initiative funded by the Department of Biotechnology (Govt. of India). Besides TCS, the project involves 9 other academic/research Institutions - including ICGEB, IISc, NCCS, AIIMS, etc. The project aims to employ a systems biology approach to decipher the intracellular dynamics of host-pathogen interactions in TB infection.  TCS' role in this project involves computational analysis (at systems level) of the enormous amounts of various experimental data generated by other participating institutions.

    Algorithms for Analysis and Management of Next Generation Sequencing  Data
    The volume of data generated in a typical genomics/metagenomics experiment using Next Generation Sequencing (NGS) platforms is typically in the order of terabytes. Performing large scale analysis on such data is not only challenging from a computational hardware perspective, but also from an algorithmic standpoint. Our group specializes in developing efficient software tools and algorithms to analyze genomics and metagenomics data generated from NGS platforms. We have also developed methods that enable efficient storage, archival and dissemination of such huge volumes of NGS data. Our algorithms and tools have been published in reputed peer-reviewed journals. We have also integrated all our published and/or patented algorithms into a unified one-stop analysis platform that can be employed for performing an accurate, meaningful end-to-end analysis of genomics and metagenomics data.                                                                                                

    Biomedical Text-mining for Enhanced Healthcare
    Searching for information has never been easier or more frustrating. Although several search engines such as Google and PubMed allow one to easily search through millions of documents in a matter of seconds, they provide very little extended analysis of the documents that can help the researcher create and test hypotheses. Our group works on tools to help address these kinds of issues in the biomedical domain. One of these is TPX, a fully automated tool that provides you with a detailed analysis of the documents retrieved from PubMed/local repository search results by identifying concepts that are present across the documents and associations between various concepts. Furthermore, the tool links unstructured text sources to information available from structured data sources, enabling users to fully navigate much of the known information. BioAppliance Genebook is another automated tool, which enables linking related information on genes through a single window, enabling fast, easy information extraction for various biomedical applications. Our current efforts are aimed at developing algorithms for the automated extraction of high quality gene disease.
                                                                                                                      

    Human Genome Analysis
    Working with the Center for Computational Biology at the University of California at Berkeley, we are developing methods for the interpretation of the genome variation. We have developed a pipeline for the automated execution for the exome and whole genome sequencing starting from the mapping of raw reads to the calling of variants. We are also working on integrated tools to analyze the called variants to extract biological insights. This effort draws upon our existing text-mining tools to create rich annotations of genes and proteins, coupled with the analysis of gene and protein networks to generate prioritized lists of candidate genes that may have a causative role in diseases.                                                                                                                    

    Computational Structural Biology
    Our project in computational structural biology uses knowledge-based and high performance computational methods to understand the role of the structure, interaction and dynamics of biological molecules to their function to identify druggable targets for pathogenic diseases. The research applies knowledge-based modeling and refinement to model biomolecules by high performance computing methods. Currently, proteins in the heme-biosynthesis pathway of Plasmodium falciparum, a lethal malaria causing intracellular pathogen are being modeled. The program phases include homology modelling, model analysis, model refinement, molecular dynamics simulations, principal component analysis and functional correlation. 

                                                                                                                       

    Drug Development
    Drug Development R&D Labs activities are focused on pharmacokinetic & pharmacodynamic (PK-PD) modeling of safety and efficacy data, obtained from pre-clinical and clinical experiments. PK and PD modeling is employed to establish a correlation of the concentration time relationship (i.e., PK) with the effect-concentration relationship (i.e. PD) in order to provide a better understanding of the time course of an effect after administration of drug. PK is the study of what the body does to a drug, i.e., its absorption, distribution, metabolism and excretion. PK modeling characterizes the blood or plasma concentration-time profiles following administration of a drug via various routes. On the other hand, PD - in general terms - seeks to define what a drug does to the body. PD modeling attempts to characterize measured physiological parameters before and after drug administration with the effect defined as the change in parameter relative to pre-dose or baseline value. The primary goal is to develop a suite of methods / tools, useful to carry out pharmacokinetic and pharmacodynamic data analyses.                                                                                                                   

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