Thesis Defense: Ofer Idan

Date and Time: Wednesday, May 7th at 1:00 pm
Location: BME Conference Room (ET351)
Sponsor: Prof. Henry Hess, PhD

Modeling Nanoscale Transport Systems

Abstract

Mathematical formulation and physical models are the foundation of scientic understanding
and technological advancement. Our ability to design experiments eectively is heavily dependent
on our physical understanding of the system under investigation, and careful mathematical
analysis is required in order to eectively progress from scientic concepts towards viable technologies.
With increasing system complexity, the focus of mathematical formulation has shifted
from simple, elegant models which rely on basic physical concepts to tailored, increasingly complex
solutions using high-powered simulations and numerical solutions. While these methods
may provide insights into specic systems, adapting these models to dierent systems is generally
dicult, even when the systems under question operate according to the same physical
laws. This is especially evident in nanobiotechnology, where the complexity of the systems studied
has given rise to experiment-driven focus. Our aim is to focus on the mathematical modeling
of transport processes in nanoscale systems, and to construct generalized, conceptual models
for three model systems, which in turn could be applied to many biological and engineered
systems.

The three model systems we use - enzyme cascades, coupled molecular motors and selfassembling
molecular shuttles provide a broad basis for generalized transport systems in
nanoscale systems. These systems combine diusive and active transport, as well as diverse
assembly conditions and multi-scale systems with size scales spanning nano- to millimeter sizes
and system complexity ranging from isolated two-component systems to multimolecular,
highly-coupled systems. By applying and adapting these basic models to increasingly complex
systems, we can both understand the physics behind nanoscale systems, as well as design these
systems with increased robustness, scalability and repeatability.


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