Hinczewski Lab

Theoretical Biophysics Research Group

Research overview


Our group is interested in using statistical physics to understand a variety of biological phenomena at scales from single molecules to cells and beyond. We seek theoretical models that address an essential question: how does ordered behavior and function emerge from complex, stochastic interactions involving many degrees of freedom? Click on the topics below for more information on current and past areas of research:

In order to latch onto their surroundings, cells exploit an array of interactions between receptor proteins embedded in their surfaces and external binding partners. These adhesion bonds are often subject to significant mechanical forces, for example a white blood cell halting at a site of inflammation in a rapidly flowing blood vessel. Single molecule experiments have measured the force response of many adhesion proteins, and we would like to exploit this data to understand the conformational changes in protein structure that determine the bond kinetics. At a larger scale, to what extent is a bond's survival time over a range of forces optimized for the physiological conditions in which the interaction is likely to occur?

Read more:
PLoS Comput. Biol. 14, e1006399 (2018). 
J. Struct. Biol. 197, 50 (2017). 
Proc. Natl. Acad. Sci. 111, 9048 (2014).  
Receptors in the cell surface have a communications role, by binding to signaling molecules like hormones and initiating cascades of chemical reactions in the cell interior. The ability of the cell to accurately process and respond to these environmental cues is limited by the noise introduced in the reaction networks that propagate and amplify the signal. We are interested in the ways cells have evolved to cope with this noise, particularly the striking parallels between biological noise filtering mechanisms and those in human-designed communications systems. Ideas from the latter, like Wiener-Kolmogorov optimal filter theory, turn out to have direct implications for biochemical signaling circuits.

Read more:
2018 IEEE International Symposium on Information Theory (ISIT), 2545 (2018). 
Phys. Rev. E 96, 012406 (2017). 
IEEE Trans. Mol. Biol. Multi-Scale Commun. 2, 16 (2016) (2016). 
J. Phys. Chem. B 120, 6166 (2016). 
Phys. Rev. X 4, 041017 (2014). 
An ongoing collaboration with two experimental groups at Case, the Strangi Nanoplasmonics and Gurkan Biomanufactoring and Microfabrication labs: we are interested in theoretically characterizing and optimizing novel designs for microfluidic biosensing based on metamaterials and nanoplasmonics. This involves understanding the optics underlying the sensor readout, as well as the dynamics of the biomolecular interactions at the sensor surface.

Read more:
Adv. Opt. Mater. 1900081 (2019). 
EPJ Appl. Metamat. 4, 1 (2017). 
Nature Materials 15, 621 (2016). 
One way in which cells maintain spatial organization is through motor proteins that bind cargo and move it along networks of cytoskeletal filaments. These proteins—myosins, kinesins, dynein among others—exist in a host of different forms, and we are interested in the common structural design principles behind their dynamics. What explains a motor's transport efficiency, its perseverance under load, its distribution of step sizes and binding locations? By altering elements of their structure, can we bioengineer motors toward specific biomedical applications?

Read more:
Proc. Natl. Acad. Sci. 110, E4059 (2013). 
Research highlight: Nature Physics 9, 692 (2013).
The precision and stability of optical tweezers have given us tantalizing glimpses of individual biomolecules folding and unfolding under force. However the theory relating what is observed in the lab—the displacements of optically trapped beads—and the actual conformational changes of the molecule, is still incomplete. We are working on methods to allow experimentalists to reliably extract intrinsic molecular properties like free energy landscapes and folding dynamics.

Read more:
Proc. Natl. Acad. Sci. 113, E3852 (2016). 
Proc. Natl. Acad. Sci. 111, 11359 (2014).  
Proc. Natl. Acad. Sci. 110, 4500 (2013).  
Proc. Natl. Acad. Sci. 107, 21493 (2010).  
Polymers are a central motif in cellular systems, and we are interested in applying theories of polymer dynamics to describe biological processes: from the fluctuations of DNA chains and their role in the association of DNA-binding proteins, to how semiflexible polymers like cytoskeletal filaments respond to tension.

Read more:
Macromolecules 44, 6972 (2011).  
J. Chem. Phys. 132, 135103 (2010).  
EPL 88, 18001 (2009).  

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