Modeling of static and dynamic components of bio-nanorobotic systems

Computational modeling techniques can facilitate to achieve in applications of biological nanocomponents for the development of potential bio-nanorobotic systems through representing the properties of these components. This work focuses on the current library of components for the development of bio-nanorobots and considers the recently reported static (e.g. C60 and C70 fullerenes) and dynamic (e.g. kinesin and muscle myosin nanomotors) components for modeling. This thesis proposes two new computational techniques to model these static and dynamic components. The first modeling technique applies graph algorithms to compute a new set of optimal weighted structural properties of C60 and C70 fullerenes. C60 and C70 fullerene nanoparticles are composed of 60 and 70 equivalent carbon atoms, respectively, arranged as hollow cages. In this technique, the graph-based structural models of the fullerenes are proposed using their real structural information. Then, these graph-based structural models of the fullerenes and graph algorithms based on dynamic programming are applied to compute a new set of optimal weighted physical properties of the components including Wiener, hyper-Wiener, Harary and reciprocal Wiener indices as well as Hosoya and hyper-Hosoya polynomials. In addition, a graph algorithm based on greedy methods is employed to compute a new set of optimal weighted electronic properties of the fullerenes via computing their Minimum Weight Spanning Trees (MWSTs). The computed optimal weighted physical and electronic properties of the fullerenes showed a good agreement with the mathematics of the properties as well as the principles of the employed graph algorithms. The second modeling technique applies agent technology to propose comprehensive structural and behavioral models of kinesin and muscle myosin protein nanomotors that can introduce the nanomotors as autonomous and intelligent nanosystems. In this technique, kinesin and muscle myosin nanomotors are introduced as physical intelligent agents. Agent-based structural models of the nanomotors are proposed using composite diagram of Unified Modeling Language (UML) to introduce the sensors and actuators of the nanomotors. Moreover, agent-based behavioral models of the nanomotors are developed using state machine diagrams of UML to illustrate the internal autonomous and intelligent decision-making processes of the nanomotors. The agent-based behavioral state machine models of the nanomotors are validated with comparing their mathematical definitions, developed as Deterministic Finite Automatons (DFAs) and their respective grammars, to the natural behaviors of the nanomotors. Finally, the behavioral DFA models of the nanomotors are implemented as their software agent models. Accordingly, the outputs of the software agent models were in good agreement with the natural behaviors of the nanomotors.

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