Chandra:Research: Difference between revisions
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Development of Virtual mycobacterial cell models and their analysis to | Development of Virtual mycobacterial cell models and their analysis to | ||
*Address mechanism of drug action. | |||
*Identify most appropriate drug targets. | |||
*Study carbohydrate recognition in pathogenesis. | |||
*Carbohydrate recognition and immunological triggers | |||
The present focus in the laboratory different areas towards deriving appropriate system landscapes at different levels of hierarchy and different levels of model abstraction, and build mathematical models to simulate them. Such models will then be used to address (a) role of recognition of carbohydrates in tuberculosis pathogenesis (b) consequences of carbohydrate recognition in triggering specific immune mechanisms relevant to tuberculosis, (c) identify appropriate drug targets for tuberculosis and (d) study the pharmacodynamic profiles of the existing anti-mycobacterial drugs. Work has already been initiated in this direction by building a first level virtual cell of the mycobacterium. A model of this system is built using a systems biology modelling toolkit. A new algorithm that encodes advanced bioinformatics tools and methods, is under development, to define the minimum system model for a given purpose. As proof-of-concept, a simulation of the system defined for H2-antihistamines has been carried out, which reveals the paradoxical accumulation of histamine available for binding by H1-receptor, upon using H2-antihistamines, thus explaining their adverse effects. | The present focus in the laboratory different areas towards deriving appropriate system landscapes at different levels of hierarchy and different levels of model abstraction, and build mathematical models to simulate them. Such models will then be used to address (a) role of recognition of carbohydrates in tuberculosis pathogenesis (b) consequences of carbohydrate recognition in triggering specific immune mechanisms relevant to tuberculosis, (c) identify appropriate drug targets for tuberculosis and (d) study the pharmacodynamic profiles of the existing anti-mycobacterial drugs. Work has already been initiated in this direction by building a first level virtual cell of the mycobacterium. A model of this system is built using a systems biology modelling toolkit. A new algorithm that encodes advanced bioinformatics tools and methods, is under development, to define the minimum system model for a given purpose. As proof-of-concept, a simulation of the system defined for H2-antihistamines has been carried out, which reveals the paradoxical accumulation of histamine available for binding by H1-receptor, upon using H2-antihistamines, thus explaining their adverse effects. | ||
A long-term goal in the laboratory is to derive general principles and sub-system ontologies, their representation and incorporation into tractable models, so as to enable disease modelling. | A long-term goal in the laboratory is to derive general principles and sub-system ontologies, their representation and incorporation into tractable models, so as to enable disease modelling. | ||
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=IN-HOUSE PROJECTS | ==IN-HOUSE PROJECTS== | ||
===Value-added specific Databases=== | |||
*Development of an integrated lectin knowledge base, its usage in analysis of lectins, identification of potential applications of lectins in clinical practice, biotechnology and agriculture | *Development of an integrated lectin knowledge base, its usage in analysis of lectins, identification of potential applications of lectins in clinical practice, biotechnology and agriculture | ||
*Development of a new algorithm for molecular modelling | *Development of a new algorithm for molecular modelling | ||
*Application to G-protein coupled receptors; dopamine and histamine receptors | *Application to G-protein coupled receptors; dopamine and histamine receptors | ||
===Genome and proteome sequence analysis: ''Mycobacterium tuberculosis''=== | |||
*Identification of pathways, Identification of function for several proteins, comparative genomics, Identification and feasibility analysis of drug targets | *Identification of pathways, Identification of function for several proteins, comparative genomics, Identification and feasibility analysis of drug targets | ||
==Systems Biology== | ===Systems Biology=== | ||
*Metabolic Pathway Analysis, Flux Balance Analysis, Identification of newer drug targets based on flux analyses, Identification of protein–protein influences based on metabolic pathway participation | *Metabolic Pathway Analysis, Flux Balance Analysis, Identification of newer drug targets based on flux analyses, Identification of protein–protein influences based on metabolic pathway participation | ||
==Structural bioinformatics== | ===Structural bioinformatics=== | ||
*Algorithms for detecting signature interaction patterns in protein structures | *Algorithms for detecting signature interaction patterns in protein structures | ||
*Study of Determinants of molecular recognition | *Study of Determinants of molecular recognition | ||
*Analysis of fold and function determinants | *Analysis of fold and function determinants | ||
==COLLABORATIVE PROJECTS== | |||
===Protein structure determination by X-ray crystallography=== | |||
Crystal structures of: | Crystal structures of: | ||
*Garlic lectin | *Garlic lectin | ||
| Line 47: | Line 45: | ||
*Structural genomics of proteins from Mycobacterium tuberculosis | *Structural genomics of proteins from Mycobacterium tuberculosis | ||
===Molecular immunology/Immunoinformatics=== | |||
==Molecular immunology/Immunoinformatics== | |||
*Towards Rational Vaccine design: In-silico Identification of CTL epitopes from ''Mycobacterium tuberculosis'' – Implications to Vaccine design | *Towards Rational Vaccine design: In-silico Identification of CTL epitopes from ''Mycobacterium tuberculosis'' – Implications to Vaccine design | ||
==Structural pharmacology== | ===Structural pharmacology=== | ||
*Elucidating mechanisms of drug action | *Elucidating mechanisms of drug action | ||
*Structure-based drug design (lead identification, protein-ligand interactions) | *Structure-based drug design (lead identification, protein-ligand interactions) | ||
Revision as of 23:16, 13 December 2005
| Chandra Lab |
| Research |
| People |
| Courses |
| Software and Databases |
| Publications |
| Resources |
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Current Focus: Systems biology application to understanding diseases, immunological processes with particular emphasis on carbohydrate recognition. An obvious outcome will be an application of the obtained knowledge in drug and vaccine discovery.
Development of Virtual mycobacterial cell models and their analysis to
- Address mechanism of drug action.
- Identify most appropriate drug targets.
- Study carbohydrate recognition in pathogenesis.
- Carbohydrate recognition and immunological triggers
The present focus in the laboratory different areas towards deriving appropriate system landscapes at different levels of hierarchy and different levels of model abstraction, and build mathematical models to simulate them. Such models will then be used to address (a) role of recognition of carbohydrates in tuberculosis pathogenesis (b) consequences of carbohydrate recognition in triggering specific immune mechanisms relevant to tuberculosis, (c) identify appropriate drug targets for tuberculosis and (d) study the pharmacodynamic profiles of the existing anti-mycobacterial drugs. Work has already been initiated in this direction by building a first level virtual cell of the mycobacterium. A model of this system is built using a systems biology modelling toolkit. A new algorithm that encodes advanced bioinformatics tools and methods, is under development, to define the minimum system model for a given purpose. As proof-of-concept, a simulation of the system defined for H2-antihistamines has been carried out, which reveals the paradoxical accumulation of histamine available for binding by H1-receptor, upon using H2-antihistamines, thus explaining their adverse effects.
A long-term goal in the laboratory is to derive general principles and sub-system ontologies, their representation and incorporation into tractable models, so as to enable disease modelling.
IN-HOUSE PROJECTS
Value-added specific Databases
- Development of an integrated lectin knowledge base, its usage in analysis of lectins, identification of potential applications of lectins in clinical practice, biotechnology and agriculture
- Development of a new algorithm for molecular modelling
- Application to G-protein coupled receptors; dopamine and histamine receptors
Genome and proteome sequence analysis: Mycobacterium tuberculosis
- Identification of pathways, Identification of function for several proteins, comparative genomics, Identification and feasibility analysis of drug targets
Systems Biology
- Metabolic Pathway Analysis, Flux Balance Analysis, Identification of newer drug targets based on flux analyses, Identification of protein–protein influences based on metabolic pathway participation
Structural bioinformatics
- Algorithms for detecting signature interaction patterns in protein structures
- Study of Determinants of molecular recognition
- Analysis of fold and function determinants
COLLABORATIVE PROJECTS
Protein structure determination by X-ray crystallography
Crystal structures of:
- Garlic lectin
- RecA protein
- Phosphoglycerate kinase
- Structural genomics of proteins from Mycobacterium tuberculosis
Molecular immunology/Immunoinformatics
- Towards Rational Vaccine design: In-silico Identification of CTL epitopes from Mycobacterium tuberculosis – Implications to Vaccine design
Structural pharmacology
- Elucidating mechanisms of drug action
- Structure-based drug design (lead identification, protein-ligand interactions)
- Structure prediction, Molecular modelling and Fold recognition
- Structure of the globular domain and C-terminal domains of the rat linker histone
- Structure of the chromatosome particle
