Network analysis of neurodegenerative disease complexity
Goal: Work includes; reconstruction of metabolic pathways, understanding networks on the basis of network properties and predicting candidate bio-markers which are responsible for phenotypic variations.
Development of computational metabolic network analysis framework
Goal: A number of neurodegenerative disorders usually referred to as tauopathies and characterized by the disappearance or disintegration of tau protein from microtubules. Alzheimer’s disease, Pick’s disease, Parkinson’s disease directly or indirectly associated with tauopathy. Tau is a protein that is usually associated with the microtubule. Microtubules are the backbone of neurons and tau provides support to microtubule stability. Hyperphosphorylation of Tau leads to its separation from the microtubule, consequently forming neurofibrillary tangles and resulting in a condition of dementia. Therapeutic implication on tauopathy is symptomatic as there is no exact regulation mechanism known to date. This project helps in a comprehensive study of biomarkers and pathways involved in tauopathy to decipher the complexity of the system resulting in candidate drug target for prognosis and diagnosis of neurodegenerative disorders.
Cancer complexome study under system biology framework
Goal: This project deals with various network approaches to solve the cancer disease complexity using graph theory, mathematical modeling and simulation studies.
Deciphering regulatory mechanisms in plantomics
Goal: The current project incorporates transcriptomics, proteomics and metabolomics studies for molecular mechanistic understanding and regulatory flux control at the in-silico level.
16S rRNA secondary structure prediction and comparative analysis of thermophiles
Goal: The project aims at the comparative analysis of various strains of Thermus aquaticus from Himachal Pradesh, India.
Oculocutaneous Albinism
Goal: The project focuses on molecular and dynamics study of OCA-6.
Machine Learning on Expression Profiles
Goal: Machine Learning algorithms on transcriptome and genome profiles to understand genotype to phenotype relationships.
Image analysis using Deep Learning
Goal: Image classification using available TensorFlow, Keras, Python libraries.
Network inference for structural and spectral analysis
Goal: Understanding and identifying missing links in data layers.