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Engineer novel synthetic cells

Natural cells are modified for broad applications by exploiting their ability to synthesize biomolecules, respond and adapt to environments, and change their structures dynamically. But, it remains challenging to engineer synthetic cells that are robust, safe, and effective, akin to microrobots. My lab integrates material and genetic approaches to create novel synthetic cells that respond to environmental stimuli. Our work will generate novel synthetic cells that are safe and effective for environmental remediation, cancer therapy, antipathogen treatment, and microbiome applications.

    Representative publications

  • C. Tan, P. Marguet, and L. You. Emergent bistability by a growth-modulating positive feedback circuit. Nature Chemical Biology, 5, 842-848, 2009.
  • 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.

Engineer novel synthetic vesicles

Natural cells secrete vesicles that are composed of phospholipids and biomolecules. Harnessing and mimicking these vesicles could create novel solutions for biomedical applications. But, creating synthetic vesicles that are multifunctional and “smart” remains challenging. My lab builds synthetic vesicles that mimic specific properties of living cells. We use synthetic biology approaches that integrate advanced cloning methods, proteomics, high-resolution imaging, and cell-free systems. The synthetic vesicles are minimal and functionalized with proteins, genes, and biomaterials. Our work will generate novel synthetic vesicles for disease theranostic and biochemical sensing.

    Representative publications

  • 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.
  • Y. Ding, LE. Contreras-Llano, E. Morris, M. Mao, C. Tan. Minimizing context-dependency of gene networks using artificial cells. ACS Applied Materials and Interfaces, 2018.

Understand and control the dynamics of synthetic biological networks

Biological networks consist of biomolecules linked by various feedback loops. These feedback loops operate under noisy cellular environments and can cause emergent behavior of cells. Understanding and controlling the flow of information through complex biological networks are crucial for the engineering of synthetic cells and vesicles. My lab uses mathematical modeling and quantitative measurements to reveal emergent dynamics of synthetic biological networks. We are among the first to discover the holistic interactions between genetic and non-genetic factors, including molecular crowding, antagonistic signaling pathways, and host-circuit interactions. Our work will create quantitative frameworks to predict and control the functions of synthetic biological networks.

    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
  • M. Jensen, E. Morris, H. Tran, M. Nash, and C. Tan. Stochastic ordering of complexoform protein assembly by genetic circuits. PLoS Comp Bio, 2021

Develop high-throughput synthetic biology platforms

Much of life science revolves around understanding and exploiting the function of proteins. Yet, only a small subset of proteins is routinely studied in basic research or used in applications. My lab integrates cell-free protein synthesis, molecular tools, microfluidics, and computational algorithms to accelerate the study of proteins. Our work will enable the high-throughput study of “difficult” and understudied proteins.

    Representative publications

  • 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
  • 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)
  • F. Wu, J. Shim, and C. Tan. Orthogonal tuning of gene expression noise using CRISPR-Cas. Nucleic Acids Research, 2020.

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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