Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13–14, 2019, Proceedings

Research Article

Self-Assembly from a Single-Molecule Perspective

  • @INPROCEEDINGS{10.1007/978-3-030-24202-2_11,
        author={Kevin Pilkiewicz and Pratip Rana and Michael Mayo and Preetam Ghosh},
        title={Self-Assembly from a Single-Molecule Perspective},
        proceedings={Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13--14, 2019, Proceedings},
        proceedings_a={BICT},
        year={2019},
        month={7},
        keywords={Molecular communication Soft matter Statistical mechanics},
        doi={10.1007/978-3-030-24202-2_11}
    }
    
  • Kevin Pilkiewicz
    Pratip Rana
    Michael Mayo
    Preetam Ghosh
    Year: 2019
    Self-Assembly from a Single-Molecule Perspective
    BICT
    Springer
    DOI: 10.1007/978-3-030-24202-2_11
Kevin Pilkiewicz1,*, Pratip Rana2, Michael Mayo1, Preetam Ghosh2
  • 1: U.S. Army Engineer Research and Development Center
  • 2: Virginia Commonwealth University
*Contact email: Kevin.R.Pilkiewicz@usace.army.mil

Abstract

As manipulating the self-assembly of supramolecular and nanoscale constructs at the single-molecule level increasingly becomes the norm, new theoretical scaffolds must be erected to replace the thermodynamic and kinetics based models used to describe traditional bulk phase active syntheses. Like the statistical mechanics underpinning these latter theories, the framework we propose uses state probabilities as its fundamental objects; but, contrary to the Gibbsian paradigm, our theory directly models the transition probabilities between the initial and final states of a trajectory, foregoing the need to assume ergodicity. We leverage these probabilities in the context of molecular self-assembly to compute the overall likelihood that a specified experimental condition leads to a desired structural outcome. We demonstrate the application of this framework to a simple toy model in which three identical molecules can assemble in one of two ways and conclude with a discussion of how the high computational cost of such a fine-grained model can be overcome through approximation when extending it to larger, more complex systems.