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.

Towards in situ Single Cell Systems Biology

Systems-level studies of biological systems rely on observations taken at a resolution lower than the essential unit of biology, the cell. Recent technical advances in DNA sequencing have enabled measurements of the transcriptomes in single cells excised from their environment, but it remains a daunting technical problem to reconstruct in situ gene expression patterns from sequencing data. In this thesis I develop methods for the routine, quantitative in situ measurement of gene expression using fluorescence microscopy.

The number of molecular species that can be measured simultaneously by fluorescence microscopy is limited by the pallet of spectrally distinct fluorophores. Thus, fluorescence microscopy is traditionally limited to the simultaneous measurement of only five labeled biomolecules at a time. The two methods described in this thesis, super-resolution barcoding and temporal barcoding, represent strategies for overcoming this limitation to monitor expression of many genes in a single cell. Super-resolution barcoding employs optical super-resolution microscopy (SRM) and combinatorial labeling via-smFISH (single molecule fluorescence in situ hybridization) to uniquely label individual mRNA species with distinct barcodes resolvable at nanometer resolution. This method dramatically increases the optical space in a cell, allowing a large numbers of barcodes to be visualized simultaneously. As a proof of principle this technology was used to study the S. cerevisiae calcium stress response. The second method, sequential barcoding, reads out a temporal barcode through multiple rounds of oligonucleotide hybridization to the same mRNA. The multiplexing capacity of sequential barcoding increases exponentially with the number of rounds of hybridization, allowing over a hundred genes to be profiled in only a few rounds of hybridization.

The utility of sequential barcoding was further demonstrated by adapting this method to study gene expression in mammalian tissues. Mammalian tissues suffer both from a large amount of auto-fluorescence and light scattering, making detection of smFISH probes on mRNA difficult. An amplified single molecule detection technology, smHCR (single molecule hairpin chain reaction), was developed to allow for the quantification of mRNA in tissue. This technology is demonstrated in combination with light sheet microscopy and background reducing tissue clearing technology, enabling whole-organ sequential barcoding to monitor in situ gene expression directly in intact mammalian tissue.

The methods presented in this thesis, specifically sequential barcoding and smHCR, enable multiplexed transcriptional observations in any tissue of interest. These technologies will serve as a general platform for future transcriptomic studies of complex tissues.