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• genetics, genomics and molecular biology
• cell and developmental biology/regenerative medicine
• cancer/oncology
• neuroscience and mental health

• Medicine
• Paediatrics
• Pathology
• Public Health

• Child and Adolescent Psychiatry
• Health Informatics
• Neurology
• Oncology

We are particularly interested in studying how cell-fates are specified during development and miss-specified in diseases.
The research in my lab is aimed at achieving an integrated molecular and systems-level understanding of the mechanisms by which epigenetic machinery and transcription factors contribute to gene expression reprogramming that defines cell-type identity during development and how this communication is altered in diseases. To investigate these questions, we employ a multidisciplinary approach combining cutting-edge epigenetics and genomics together with computational biology tools in sophisticated and defined models.

Please refer to this page for details of our research:
https://www.tiwarilab.com/

Please refer to this page for our key publications:
https://www.tiwarilab.com/science-our-key-discoveries/

Unlocking single-cell genomics datasets for biomarker discovery in human diseases
This project aims to mine single-cell genomics datasets in combination with state-of-the-art computational biology methods for biomarker discovery in challenging human diseases to reveal disease mechanisms and facilitate early diagnosis and therapy.

Recently, single-cell gene expression profiling has provided ways to improve diagnostic measures and risk stratification for many diseases. Precise methods to determine the number and phenotype of cells in disease lesions using small population of cells is of huge importance for the prognosis, diagnosis and discovering the signaling pathways of interest to be targeted by specific therapeutics, in several diseases, including neurological disorders, infections, inflammatory bowel disease, cancers and autoimmune diseases such as rheumatoid arthritis.

The goal of this project is to use existing single cell gene expression and other such datasets from various diseases in the literature (and growing) and analyse them in different ways to identify new biomarkers. Single cell gene expression profiling will be implemented to explain the mechanism of disease development and discover new biomarkers by identifying key pathways and constructing networks and sub-networks of co-expressing proteins. The combination of different technologies and statistical analysis will provide novel and more effective methods to identify and validate new disease biomarkers.