- Fighting antibiotic resistance using drug combinations: We are developing software tools (e.g. INDIGO, MAGENTA) to design drug combinations with enhanced potency and reduced potential for developing resistance. We also study pathogen metabolism and pathogen-immune interactions to discover new synergistic antibiotics against M. tuberculosis, S. aureus and other pathogens.
- Systems biology algorithms to understand metabolic regulation: Our lab is developing new modeling tools to simulate the activity of thousands of metabolic reactions in a human or microbial cell, giving us a unique systems perspective on metabolic regulation. We have applied the methods that we developed (e.g. PROM, DFA, GEMINI and ASTRIX) to understand microbial, stem-cell, cancer, and brain metabolism using omics datasets.
Learn more at: https://www.sriramlab.org/research
- Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. BioEssays, 2020.
- Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors, Genome Biology, 2019
- Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis, mBio, 2019
- Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling, Cell Reports, 2017
- Granzyme B disrupts central metabolism and protein synthesis in bacteria to promote an immune cell death program, Cell, 2017
See full list of publications at: https://www.sriramlab.org/publications
Recent news articles:
- Deep learning AI discovers surprising new antibiotics
- Closest look yet at killer T-cell activity could yield new approach to tackling antibiotic resistance
- ‘Nightmare bacteria:’ Michigan Engineers discuss how to combat antibiotic resistance
- A ‘decathlon’ for antibiotics puts them through more realistic testing
- AI in BME (BME 499.060): This course introduces students to AI and machine-learning algorithms and their applications in BME. The course is open to both graduate and undergraduate students.
Learn more at the course website: https://www.sriramlab.org/ai-bme