Temporal Control of Ion Channel Activation

Ion channels are a large class of integral membrane proteins that allow for the diffusion of ions across a cellular membrane and are found in all forms of life. Pentameric ligand-gated ion channels (pLGICs) comprise a large family of proteins that include the nicotinic acetylcholine receptor (nAChR) and the γ-aminobutyric acid (GABA) receptor. These ion channels are responsible for the fast synaptic transmission that occurs in humans and as a result are of fundamental biological importance. pLGICs bind ligands (neurotransmitters), and upon ligand-binding undergo activation. The activation event causes an ion channel to enter a new physical state that is able to conduct ions. Ion channels allow for the flux of ions across the membrane through a pore that is formed upon ion channel activation. For pLGICs to function properly both ligand-binding and ion channel activation must occur. The ligand-binding event has been studied extensively over the past few decades, and a detailed mechanism of binding has emerged. During activation the ion channel must undergo structural rearrangements that allow the protein to enter a conformation in which ions can flow through. Despite this great and ubiquitous importance, a fundamental understanding of the ion channel activation mechanism and kinetics, as well as concomitant structural arrangements, remains elusive.

This dissertation describes efforts that have been made to temporally control the activation of ligand-gated ion channels. Temporal control of ion channel activation provides a means by which to activate ion channels when desired. The majority of this work examines the use of light to activate ion channels. Several photocages were examined in this thesis; photocages are molecules that release a ligand under irradiation, and, for the work described here, the released ligand then activates the ion channel. First, a new water-soluble photoacid was developed for the activation of proton-sensitive ion channels. Activation of acid-sensing ion channels, ASIC2a and GLIC, was observed only upon irradiation. Next, a variety of Ru²⁺ photocages were also developed for the release of amine ligands. The Ru²⁺ systems interacted in a deleterious manner with a representative subset of biologically essential ion channels. The rapid mixing of ion channels with agonist was also examined. A detection system was built to monitor ion channels activation in the rapid mixing experiments. I have shown that liposomes, and functionally-reconstituted ELIC, are not destroyed during the mixing process. The work presented here provides the means to deliver agonist to ligand-gated ion channels in a controlled fashion.

Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Solar System Small-Body Demographics with the Palomar Transient Factory Survey

Observational studies of our solar system's small-body populations (asteroids and comets) offer insight into the history of our planetary system, as these minor planets represent the left-over building blocks from its formation. The Palomar Transient Factory (PTF) survey began in 2009 as the latest wide-field sky-survey program to be conducted on the 1.2-meter Samuel Oschin telescope at Palomar Observatory. Though its main science program has been the discovery of high-energy extragalactic sources (such as supernovae), during its first five years PTF has collected nearly five million observations of over half a million unique solar system small bodies. This thesis begins to analyze this vast data set to address key population-level science topics, including: the detection rates of rare main-belt comets and small near-Earth asteroids, the spin and shape properties of asteroids as inferred from their lightcurves, the applicability of this visible light data to the interpretation of ultraviolet asteroid observations, and a comparison of the physical properties of main-belt and Jovian Trojan asteroids. Future sky-surveys would benefit from application of the analytical techniques presented herein, which include novel modeling methods and unique applications of machine-learning classification. The PTF asteroid small-body data produced in the course of this thesis work should remain a fertile source of solar system science and discovery for years to come.