Systems biology lab

SYSTEMS BIOLOGY

Predicting the metabolic behavior of organisms has been challenging due to the vast number of metabolic reactions. Our lab develops modeling tools that simulate thousands of metabolic reactions in a human or microbial cell. In addition, we also simulate how this network of metabolic reactions is controlled by an equally complex set of regulators (regulatory network), giving us a unique systems perspective on metabolic regulation. We have applied methods that we developed such as PROM, DFA, GEMINI and ASTRIX to understand microbial, stem-cell, cancer, and brain metabolism. These tools can be applied for engineering microbial metabolism, manipulating stem cell epigenome, and for understanding metabolic disorders.

• News articles & commentaries -

  • New Approach Combines Metabolic, Regulatory Networks to Elucidate Cells' Activities, GenomeWeb

  • Metabolically constrained regulatory networks, Nature Genetics

  • Systems biology meets behavior, PNAS

• Research publications -

  • Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

  • Common biochemical properties of metabolic genes recurrently dysregulated in tumors

  • Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

  • Comprehensive mapping of pluripotent stem cell metabolism using dynamic genome-scale network modeling

  • Metabolic Constraint-based Refinement of Transcriptional Regulatory Networks

  • Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis

  • Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

DRUG DISCOVERY

Another major focus of our lab is to understand antibiotic resistance and design novel therapies using computational (machine learning) approaches. 100,000 people die and a million others are sickened by antibiotic resistant bacteria in the United States every year. There is an urgent need to develop high-throughput approaches to screen promising therapies to counter drug-resistance. Our lab is developing mechanistic AI platforms that can accelerate the discovery of new candidate drugs and multi-drug combinations for slowing the evolution of antibiotic resistant strains.

• News articles -

  • Scientists image tuberculosis regulatory-metabolic network

  • New Tool in the Fight Against Tuberculosis: Algorithm Enables Cell-Scale Simulations

  • Computational tools, antibiotics and drug discovery

  • 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

  • How an AI solution can design new tuberculosis drug regimens

• Research publications -

  • Granzyme B disrupts central metabolism and protein synthesis in bacteria to promote an immune cell death program

  • Chemogenomics and Orthology-based Design of Antibiotic Combination Therapies

  • Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis

  • Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

  • Transcriptomic Signatures Predict Regulators of Drug Synergy and Clinical Regimen Efficacy against Tuberculosis

  • A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Lectures/videos on our research