Self-assembly is a ubiquitous, natural, robust process in many living organisms and occurs through coordinated action of independent objects/entities leading to the formation of well-defined patterns. For instance, microtubules form a framework for structures such as the spindle apparatus that appears during cell division. A goal of this project is to develop an algorithmic framework to identify the intelligence of encoded patterns in self-assembly systems. Insights gained from this project are important to computational science and engineering in order to develop novel engineered self-assembly systems. The novelty of the algorithmic framework lies in the combination of stochastic discrete event simulations and molecular/cellular level models in cellular automata. This approach will enable the cooperation of multiple discrete events at multiple temporal levels of the self-assembly process to provide precise control of such events that exhibits specific behaviors observed in such systems. A discrete dynamical cellular automata model will be developed to represent the local behavior of interacting tubulin dimers in forming a microtubules lattice.
The research will provide a hybrid algorithmic framework for the wider algorithmic community. Other broader Impacts include multidisciplinary training for students, and integration of research into undergraduate/graduate courses in computer science and mechanical engineering curricula. Outreach activities involve developing and organizing workshops related to self-assembly algorithms and framework. The proposed MTetris game is intended to attract middle and high school students to STEM fields. The results will be disseminated through journal and conference publications and freely available online.
