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Create novel biotechnology / biomedical applications using artificial cellular systems

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 cellular systems are minimal, robust, efficient, and easy to control. They compose of proteins, genes, and materials from both natural and synthetic sources. To date, my lab has constructed synthetic protein-synthesis networks in cell-free systems, artificial cells, artificial bacteria, 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.

    Representative publications

  • K. Justus, T. Hellebrekers, D. Lewis, A. Wood, C. Ingham, C. Majidi, P. LeDuc, C. Tan. A biosensing soft robot: Autonomous parsing of chemical signals through integrated organic and inorganic interfaces. Science Robotics, 4(31), eaax0765, 2019.
  • F. Villarreal, M. Chavez, Y. Ding, J. Fan, T. Pan, and C. Tan. Synthetic microbial consortia enable rapid assembly of multi-protein complexes. Nature Chemical Biology, 14 (1), 29, 2018
  • D. Lewis, R. Vanella, C. vo, L. Rose, M. Nash, and C. Tan. Engineered stochastic adhesion between microbes as a protection mechanism against environmental stress. Cellular and Biomolecular Engineering – Special Issue for Young Innovator Award, 11 (5), 367-382, 2018.

Reveal operating principles of protein-synthesis networks in artificial cellular systems

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 in artificial cellular systems, including artificial cells, artificial bacteria, cell-free systems, and biohybrid robots. We address the question using quantitative modeling, molecular biology, and real-time imaging methods.

    Representative publications

  • C. Meyer, L. Contreras-Llano, Y. Liu, R. Pasula, S. Lim, M. Longo, C. Tan. Holistic engineering of cell-free systems through proteome-reprogramming synthetic circuits. Nature Communications, 2020
  • C. Tan, S. Saurabh, M. Bruchez, R. Schwartz, and P. LeDuc. Shaping gene expression in artificial cellular systems by cell-inspired molecular crowding. Nature Nanotechnology, 8 (8), 602-608, 2013.
  • C. Tan, P. Marguet, and L. You. Emergent bistability by a growth-modulating positive feedback circuit. Nature Chemical Biology, 5, 842-848, 2009.

Develop molecular, mathematical, and microfluidic tools that enable the multi-dimensional study of artificial cellular systems

The dynamics of protein networks in artificial cellular systems, are controlled by at least 36 translation machinery, the machinery that generates energy and amino acids, and other proteins that fold, degrade, and maintain proteins. It has been challenging to systematically study the interactions between these factors that govern the dynamics of protein networks in artificial cellular systems. My lab has developed unique CRISPR-Cas and microfluidics tools for the multi-dimensional study of protein networks.

    Representative publications

  • F. Wu, J. Shim, and C. Tan. Orthogonal tuning of gene expression noise using CRISPR-Cas. Nucleic Acids Research, 2020.
  • J Wang, K Deng, C Zhou, Z Fang, C Meyer, KU Deshpande, Z Li, X Mi, Q Luo, BD Hammock, C Tan*, Y Chen*, T Pan*. Microfluidic cap-to-dispense (μCD): a universal microfluidic–robotic interface for automated pipette-free high-precision liquid handling. Lab on a Chip;19(20):3405-15, 2019 (*co-corresponding authors)
  • P. Dhar, C. Tan, S. Somani, L. Ye, A. Sairam, M. Chitre, Z. Hao, and K. Sakharkar. “Cellware—a multi-algorithmic software for computational systems biology.” Bioinformatics 20, no. 8 (2004): 1319-1321, 2004

Our toolbox

 Real-time microscopy
 High-throughput assay
 High-throughput cloning and CRISPR-Cas
 Artificial cells & cell-free
 Matlab, C++, Python
 High-performance computing; Machine learning

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