University of North Carolina, Chapel Hill

To create artificial living systems that mimic the shape-dependent signaling of natural cells

  • Amount $1,586,250
  • City Chapel Hill, NC
  • Investigator Ronit Freeman
  • Year 2021
  • Program Research
  • Sub-program Matter-to-Life

Natural cells routinely use shape as a vector for acquiring and disseminating information about their environment.  Detecting the shape of a cell they have encountered can impart important information about their neighbor, and thus inform what sort of response would be most adaptive.  The underlying mechanisms that allow organisms to process this topological information, however, are not well understood.  This grant funds a multi-disciplinary a team led by Ronit Freeman at the University of North Carolina at Chapel Hill to better understand these mechanisms by attempting to create an artificial cell-like entity that mimics natural cells’ ability to detect shape.  Grant funds will support three interrelated research efforts. We know that cells use proteins to detect the shape of fellow cells, but the proteins that perform this function can be 10 to 100 times smaller than the cells they are measuring.  How these natural systems bridge that length gap is poorly understood.  Using advanced atomic scale microscopy, Freeman’s team will observe the behavior and structure of these shape-detecting proteins and attempt to reverse engineer synthetic versions that could perform similar functions in a synthetic cell. A second effort will focus on signal transduction, the process of converting shape data collected at the cell membrane into physical and biochemical signals that can be passed to a cell’s interior.  The research team will attempt to create synthetic pathways that mimic signal transduction mechanisms thought to operate in natural cells, allowing them to use detected topological information to effect changes in behavior of the synthetic cell itself.  Third, the team will use advanced techniques to model how topological information can spread through communities of cells, affecting the behavior or characteristics of entire cell collectives.  Such modeling has, to date, mainly confined itself to the chemical aspects of cellular communication. Freeman and her team will expand these efforts, incorporating physical variables such as the flow of mass and momentum, membrane elasticity, flow across interfaces, and cell deformation into their model.

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