Self-assembly is a ubiquitous, naturally occurring process in many living organisms. Understanding the self-assembly process in nature might open up new and fundamental approaches to novel computational and informatics paradigms. This research will answer fundamental questions regarding how self-assembled tubular structures are formed and provide a novel computational multi-scale methodology for evaluating their mechanical properties. The novelty of the methodology stems from integrating models at multiple levels involving continuum/geometric and discrete approaches as compared to existing approaches. This research, which lies at the intersection of mechanical engineering, computer science, molecular biophysics and computational science, has direct implications for advances in nano- and biotechnologies with the goal of designing and manufacturing self-assembled structures for multiple health/medical applications including drug delivery and device components.

The research will combine discrete and geometric models along with coarse-grained molecular dynamics simulations to arrive at a multi-scale model that captures the microtubule self-assembly dynamics. In particular, the multi-scale model will emerge from a novel integration of local (atomistic) and global (discrete and geometric) representations of the microtubule self-assembly, including the interfaces at nano-level building blocks, mechano-chemical interactions, and the stochastic and temporal processes involved in forming microtubules. Simulations based on this multi-scale model will reveal some of the biological/geometric rules of microtubule self-assembly, generating multifunctional tubular structures with varied mechanical properties. The simulated self-assembled structures will be validated with other primitive data/models from the literature. The models at multiple levels represent major contributions in understanding the science behind the self-assembly of microtubules. These contributions may also enable future advances in the use of tubular structures in nano- and biotechnology applications ranging from sensing, actuation, self-repair, and drug delivery. Additionally, this project will positively impact the training of two graduate students who will create the multi-scale methodology. Research results will be presented at scientific meetings, and will be disseminated through www and YouTube media, thereby engaging the broader community. The project includes mentoring for undergraduates, high school students and underrepresented minorities.

Project Start
Project End
Budget Start
2016-08-01
Budget End
2019-07-31
Support Year
Fiscal Year
2016
Total Cost
$407,110
Indirect Cost
Name
University of Georgia
Department
Type
DUNS #
City
Athens
State
GA
Country
United States
Zip Code
30602