In eukaryotic cells, a key regulatory step in target gene expression is the recognition of the core promoter by the TFIID complex. TFIID is composed of the TATA-box binding protein (TBP) and TBP-Associated Factors (TAFs) (Goodrich and Tjian, 2010; Hochheimer and Tjian, 2003; Naar et al., 2001). To understand gene regulation at the core promoter, I have been focusing on one component of the core promoter recognition complex-TAF3 that was originally identified as a subunit of the TFIID complex in HeLa cells (Gangloff et al., 2001). It was later found that, while other TFIID subunits are destroyed during myogenesis, TAF3 is selectively retained in myotubes in a specialized complex with TBP-related factor 3, TRF3 (Deato and Tjian, 2007) and the sub-nuclear localization of TAF3 serves as another potential mechanism to influence transcription during myogenesis (Yao et al., 2011). Intriguingly, TAF3 recognizes trimethylated histone H3 lysine 4 (H3K4me3) (Vermeulen et al., 2007), which is associated with actively transcribed genes and also with silent developmental genes that are poised for activation upon ES cell differentiation (Mikkelsen et al., 2007).

In our recent studies, surprisingly it was found that TAF3 is highly enriched in ES cells. More importantly, during in vitro EB formation, which mimics early embryogenesis, TAF3 protein levels were selectively reduced. However, the levels of other canonical TAFs and TBP remained stable. Functional and gene expression analysis revealed that high levels of TAF3 are dispensable for ES cell self-renewal but are required for endoderm lineage differentiation and to prevent premature specification of neuroectoderm and mesoderm. Genome-wide binding studies by ChIP-seq found that unlike other canonical TFIID subunits such as TBP and TAF1 that bind only promoters, TAF3 targets both promoters and distal enhancer-like sites in ES cells.

Importantly, TAF3 sites were further clustered into four distinct classes based on the context of co-localization with other factors. Briefly, as well as TAF3, class 1 regions are promoter regions enriched for TFIID, Pol II and H3K4me3. Their proximity (< 1kb) to the TSS of known genes confirms that they are predominantly TFIID-bound promoters. By contrast, Class 2 regions have low levels of TFIID and H3K4me3 but are enriched for Oct4/Nanog/Sox2 and mediator components. Class 3 regions are specifically enriched for TAF3, CTCF and cohesin subunits. Finally, class 4 regions are not enriched for any of the factors we considered besides TAF3. Regions from the last three Classes are generally far away (~10's kb) from the TSS.

Further studies revealed that genes down-regulated upon TAF3 depletion are selectively associated with regions enriched for TAF3, CTCF and cohesin (Class 3) and TAF3 only (Class 4), while no such association was seen for genes up-regulated upon TAF3 depletion. Thus, it's likely that class 3 and class 4 regions are required for the efficient expression of genes in their vicinity and depletion of TAF3 interferes with this function. Indeed, our parallel biochemical experiments confirmed a direct protein-protein interaction between that TAF3 and CTCF. This novel interaction was further shown to mediate DNA looping between promoter distal sites and core promoters to regulate proper transcription activation. Notably, we also found that the class 2 binding regions are enriched around up-regulated genes upon TAF3 depletion, consistent with a mechanism of transcription repression by TAF3 in association with Oct4/Nanog/Sox2. Together, our data support the model that TAF3 orchestrates a complex long-distance chromatin interaction network that safeguards the finely-balanced transcriptional programs underlying ES pluripotency.

Big data is considered to play an important role in the fourth industrial revolution, which requires engineers and computers to fully utilize data to make smart decisions and improve the performance of industrial processes and of their control and safety systems. Traditionally, industrial process control systems rely on a (usually linear) data-driven model with parameters that are identified from industrial/simulation data, and in certain cases, for example, in profit-critical control loops, on first-principles models (with data-determined model parameters) that describe the underlying physico-chemical phenomena. However, modeling large-scale, complex nonlinear processes continues to be a major challenge in process systems engineering. Modeling is particularly important now and into the future, as process models are key elements of advanced model-based control systems, e.g., model predictive control (MPC) and economic MPC (EMPC).

Due to the wide variety of applications, machine learning models have great potential, yet, the development of rigorous and systematic methods for incorporating machine learning techniques in nonlinear process control and operational safety is in its infancy. Traditionally, operational safety of chemical processes has been addressed through process design considerations and through a hierarchical, independent design of control and safety systems. However, the consistent accidents throughout chemical process plant history (including several high profile disasters in the last decade) have motivated researchers to design control systems that explicitly account for process operational safety considerations. In particular, a new design of control systems such as model predictive controllers (MPC) that incorporate safety considerations and can be coordinated with safety systems has the potential to significantly improve process operational safety and avoid unnecessary triggering of alarms systems, where machine learning techniques can be utilized to derive dynamic process models. However, the rigorous design of safety-based control systems poses new challenges that cannot be addressed with traditional process control methods, including, for example, proving simultaneous closed-loop stability and safety. On the other hand, cybersecurity has become increasingly important in chemical process industries in recent years as cyber-attacks that have grown in sophistication and frequency have become another leading cause of process safety incidents. While the traditional methods of handling cyber-attacks in control systems still rely partly on human analysis and mainly fall into the area of fault diagnosis, the intelligence of cyber-attacks and their accessibility to control system information has recently motivated researchers to develop cyber-attack detection and resilient operation control strategies to address directly cybersecurity concerns.

Motivated by the above considerations, this dissertation presents the use of machine learning techniques in model predictive control, operational safety and cybersecurity for chemical processes described by nonlinear dynamic models. The motivation and organization of this dissertation are first presented. Then, the use of machine learning techniques to develop data-driven nonlinear dynamic process models to be used in model predictive controllers is presented, followed by the discussion of real-time implementation with online learning of machine leaning models and of physics-based machine learning modeling methods. Subsequently, the MPC and economic MPC schemes that use control Lyapunov-barrier functions (CLBF) are presented in detail with rigorous analysis provided on their closed-loop stability, operational safety and recursive feasibility properties. Next, the development of machine-learning-based CLBF-MPC schemes is presented with process stability and safety analysis. Finally, the development of an integrated detection and control system for process cybersecurity is developed, in which several types of intelligent cyber-attacks, machine learning detection methods and resilient control strategies are presented. Throughout the dissertation, the control methods are applied to numerical simulations of nonlinear chemical process examples to demonstrate their effectiveness and performance.

Logic synthesis is one of the key stages in the computer-aided design (CAD) flow for a field programmable gate array (FPGA) based design. It usually consists of a series of optimization iterations to improve the quality of results (QoR) of the design. Besides the traditional optimization objectives (e.g., performance, area, power), the reliability is becoming a main concern as modern FPGAs have advanced to 20nm technology, due to reduction in core voltage, decrease in transistor geometry, and increase in switching speed. However, existing techniques for enhancing the reliability of FPGA based designs fall behind industrial needs in terms of cost (e.g., area and power overhead), CAD flow, runtime, and the FPGA architecture.

To address the problems, this dissertation proposes several novel logic synthesis algorithms. The first algorithm seeks a formal method to improve the reliability of FPGA based designs while incurring minimal area and power overhead. The algorithm formulates the problem of the FPGA reliability under random faults as a stochastic satisfiability (SSAT) based Boolean matching, and employs robust templates to rewrite the look-up table (LUT) based netlist, to maximize the stochastic yield rate. To ensure not breaking the current CAD flow, a logic synthesis algorithm is presented that performs a SAT-based in-place reconfiguration in the LUT to mask soft errors, without changing of the functionality and topology of the LUT based netlist. In addition, the dissertation proposes three fast in-place logic synthesis algorithms targeting the modern FPGA architecture including both LUTs and interconnects, which perform simulation guided netlist analyses and utilize don't cares in the netlist to enhance the reliability of the design. The effectiveness of the proposed algorithms are verified by experimental results.