Limits and tradeoffs in the control of autocatalytic systems

Despite the complexity of biological networks, we find that certain common architectures govern network structures. These architectures impose fundamental constraints on system performance and create tradeoffs that the system must balance in the face of uncertainty in the environment. This means that while a system may be optimized for a specific function through evolution, the optimal achievable state must follow these constraints. One such constraining architecture is autocatalysis, as seen in many biological networks including glycolysis and ribosomal protein synthesis. Using a minimal model, we show that ATP autocatalysis in glycolysis imposes stability and performance constraints and that the experimentally well-studied glycolytic oscillations are in fact a consequence of a tradeoff between error minimization and stability. We also show that additional complexity in the network results in increased robustness. Ribosome synthesis is also autocatalytic where ribosomes must be used to make more ribosomal proteins. When ribosomes have higher protein content, the autocatalysis is increased. We show that this autocatalysis destabilizes the system, slows down response, and also constrains the system’s performance. On a larger scale, transcriptional regulation of whole organisms also follows architectural constraints and this can be seen in the differences between bacterial and yeast transcription networks. We show that the degree distributions of bacterial transcription network follow a power law distribution while the yeast network follows an exponential distribution. We then explored the evolutionary models that have previously been proposed and show that neither the preferential linking model nor the duplication-divergence model of network evolution generates the power-law, hierarchical structure found in bacteria. However, in real biological systems, the generation of new nodes occurs through both duplication and horizontal gene transfers, and we show that a biologically reasonable combination of the two mechanisms generates the desired network.

Synthesis, Oxidation and Photophysics of Perfluoroborated Tetrakis(pyrophosphito)diplatinate (II) and Density Functional Theory (DFT) Study of Electrochemical CO2 Reduction by Mn Catalysts

In the first part of this thesis (Chapters I and II), the synthesis, characterization, reactivity and photophysics of per(difluoroborated) tetrakis(pyrophosphito)diplatinate(II) (Pt(POPBF₂)) are discussed. Pt(POP-BF₂) was obtained by reaction of [Pt₂(POP)₄]^4- with neat boron trifluoride diethyl etherate (BF₃·Et₂O). While Pt(POP-BF₂) and [Pt₂(POP)₄]^4- have similar structures and absorption spectra, they differ in significant ways. Firstly, as discussed in Chapter I, the former is less susceptible to oxidation, as evidenced by the reversibility of its oxidation by I2. Secondly, while the first excited triplet states (T₁) of both Pt(POP-BF₂) and [Pt₂(POP)₄]^4- exhibit long lifetimes (ca. 0.01 ms at room temperature) and substantial zero-field splitting (40 cm^-1), Pt(POP-BF₂) also has a remarkably long-lived (1.6 ns at room temperature) singlet excited state (S₁), indicating slow intersystem crossing (ISC). Fluorescence lifetime and quantum yield (QY) of Pt(POP-BF₂) were measured over a range of temperatures, providing insight into the slow ISC process. The remarkable spectroscopic and photophysical properties of Pt(POP-BF₂), both in solution and as a microcrystalline powder, form the theme of Chapter II.

In the second part of the thesis (Chapters III and IV), the electrochemical reduction of CO₂ to CO by [(L)Mn(CO)₃]^- catalysts is investigated using density functional theory (DFT). As discussed in Chapter III, the turnover frequency (TOF)-limiting step is the dehydroxylation of [(bpy)Mn(CO)₃(CO₂H)]^0/- (bpy = bipyridine) by trifluoroethanol (TFEH) to form [(bpy)Mn(CO)₄]^+/0. Because the dehydroxylation of [(bpy)Mn(CO)₃(CO₂H)]^- is faster, maximum TOF (TOF_max) is achieved at potentials sufficient to completely reduce [(bpy)Mn(CO)₃(CO₂H)]⁰ to [(bpy)Mn(CO)₃(CO₂H)]^-. Substitution of bipyridine with bipyrimidine reduces the overpotential needed, but at the expense of TOF_max. In Chapter IV, the decoration of the bipyrimidine ligand with a pendant alcohol is discussed as a strategy to increase CO₂ reduction activity. Our calculations predict that the pendant alcohol acts in concert with an external TFEH molecule, the latter acidifying the former, resulting in a ~ 80,000-fold improvement in the rate of TOF-limiting dehydroxylation of [(L)Mn(CO)₃(CO₂H)]^-.

An interesting strategy for the co-upgrading of light olefins and alkanes into heavier alkanes is the subject of Appendix B. The proposed scheme involves dimerization of the light olefin, operating in tandem with transfer hydrogenation between the olefin dimer and the light alkane. The work presented therein involved a Ta olefin dimerization catalyst and a silica-supported Ir transfer hydrogenation catalyst. Olefin dimer was formed under reaction conditions; however, this did not undergo transfer hydrogenation with the light alkane. A significant challenge is that the Ta catalyst selectively produces highly branched dimers, which are unable to undergo transfer hydrogenation.

Distributed Optimal Control of Cyber-Physical Systems: Controller Synthesis, Architecture Design and System Identification

The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.