Create novel biotechnology / biomedical applications using synthetic protein-synthesis 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 biomimetic 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 protein-synthesis networks in biotechnological and biomedical applications
Protein-synthesis networks consist of proteins 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, and how we can exploit the understanding to improve system performance. We address the question using quantitative modeling, molecular biology, and real-time imaging methods. We are studying protein-synthesis networks relevant to cancer development and antibacterial treatment.
| Nikon microscope
|| Tecan Platereader
|| Gradient PCR
| Rotary Evaporator
| Benchtop Super-Centrifuge
|| Matlab & C++
|| High-performance computing