Create novel biotechnology / biomedical applications using synthetic protein networks
Living cells are conventional chassis for biotechnological applications, but they are complex, sensitive to perturbations, and difficult to control for some applications. We overcome the problem using bottom-up systems that mimic living cells. The artificial systems are minimal, robust, efficient, and easy to control. They consist of proteins, genes, and materials from both natural and synthetic sources. To date, my lab has constructed synthetic networks in cell-free systems, artificial cells, and soft-robot. My lab uses advanced cloning methods, proteomics, mass spectrometry, high-resolution imaging, and mathematical modeling. We are creating novel solutions for disease diagnostics, screening of biomolecules, and biochemical sensing.
Reveal operating principles of natural protein networks
Natural cellular networks consist of entities that are linked by various feedback and feedforward loops. They respond to environmental signals but are perturbed by environmental and cellular noise. The feedback loops and noise are not commonly considered in improving the performance of biotechnology and biomedical applications. We study how complex protein-synthesis networks generate emergent behavior. We address the question using quantitative modeling, molecular biology, and real-time imaging methods. In the process, we also develop high-throughput methods to synthesize, assemble, and screen multi-protein networks. We are studying protein networks relevant to cancer treatment and antibacterial treatment.
| Nikon microscope
|| Tecan Platereader
|| Gradient PCR
| Rotary Evaporator
| Benchtop Super-Centrifuge
|| Matlab & C++
|| High-performance computing