Investigation of the roles of CheD in the Bacillus subtilis chemotactic signal transduction

Abstract The two-component based chemotaxis signal transduction system allows flagellated bacteria to sense their surrounding chemical environment and move towards more favorable conditions. The attractant signals can be sensed by transmembrane chemoreceptors, and then transmitted to the histidine kinase CheA. Once activated, CheA interacts with the response regulator CheY through phosphorelay, which causes a change in the rotation of the flagella. The direction of flagella rotation determines whether a cell swims straight or just tumbles. Cells also need adaptation to respond to a change in chemical concentrations, and return to their prestimulated level. Adaptation in the B. subtilis chemotaxis system is achieved by three coordinated systems: the methylation system, the CheC/CheD/CheY-p system and the CheV system. CheD, the previously identified receptor deamidase, was shown to be critical to the ability of B. subtilis to perform chemotaxis and is the main focus of this study. This study started from characterization of the enzymatic mechanism of CheD. Results showed that CheD deamidase uses a cysteine hydrolase mechanism. The catalytic triad consisting of Cys33-His50-Thr27, and Ser27 is essential for receptor recognition and binding. In addition, in this study CheC was found to inhibit CheD’s deamidase activity. Through mutant screening, Phe102 on CheD was found to be the essential site to interact with CheC. Furthermore, the CheD/CheC interaction is necessary for the robust chemotaxis in vivo as demonstrated by the cheD (F102E) mutant, which lacks the ability to swim on swarm plates. Despite its deamidase activity, we hypothesized that CheD’s main role is its involvement in the CheD-CheC-CheY-p negative feedback pathway during adaptation. In particular, CheD is likely to help stabilize the transient kinase-activating state through binding to receptors. When CheY-p level is increased, CheC-CheY-p complex may attract CheD away from receptors. In this study, CheC-CheD binding kinetics with CheY or CheYp presence was successfully obtained by a series of SPR experiments. The increased affinity of CheD for CheC in presence of CheYp but not CheY makes likely the hypothesis that CheC-CheD-CheY interact as part of a negative feedback pathway during adaptation. Last, the interaction between CheD and chemoreceptor McpC was studied in order to better understand the role of CheD in adaptation. Results showed that Q304 and Q305 on McpC are essential to recruit CheD. Additionally, the reduced levels of CheD in mcpC (Q304A) or (Q305A) mutants suggested that the dynamic interaction between CheD and receptors is vital to maintain the normal CheD level. These findings suggest more complicated roles of CheD than its previously identified function as a receptor deamidase, and will lead to a clearer picture of the coordination of the three adaptational systems in the B. subtilis chemotactic sensory transduction system.

Hybrid mathematical and informational modeling of beam-to-column connections

The analysis of steel and composite frames has traditionally been carried out by idealizing beam-to-column connections as either rigid or pinned. Although some advanced analysis methods have been proposed to account for semi-rigid connections, the performance of these methods strongly depends on the proper modeling of connection behavior. The primary challenge of modeling beam-to-column connections is their inelastic response and continuously varying stiffness, strength, and ductility. In this dissertation, two distinct approaches—mathematical models and informational models—are proposed to account for the complex hysteretic behavior of beam-to-column connections. The performance of the two approaches is examined and is then followed by a discussion of their merits and deficiencies. To capitalize on the merits of both mathematical and informational representations, a new approach, a hybrid modeling framework, is developed and demonstrated through modeling beam-to-column connections. Component-based modeling is a compromise spanning two extremes in the field of mathematical modeling: simplified global models and finite element models. In the component-based modeling of angle connections, the five critical components of excessive deformation are identified. Constitutive relationships of angles, column panel zones, and contact between angles and column flanges, are derived by using only material and geometric properties and theoretical mechanics considerations. Those of slip and bolt hole ovalization are simplified by empirically-suggested mathematical representation and expert opinions. A mathematical model is then assembled as a macro-element by combining rigid bars and springs that represent the constitutive relationship of components. Lastly, the moment-rotation curves of the mathematical models are compared with those of experimental tests. In the case of a top-and-seat angle connection with double web angles, a pinched hysteretic response is predicted quite well by complete mechanical models, which take advantage of only material and geometric properties. On the other hand, to exhibit the highly pinched behavior of a top-and-seat angle connection without web angles, a mathematical model requires components of slip and bolt hole ovalization, which are more amenable to informational modeling. An alternative method is informational modeling, which constitutes a fundamental shift from mathematical equations to data that contain the required information about underlying mechanics. The information is extracted from observed data and stored in neural networks. Two different training data sets, analytically-generated and experimental data, are tested to examine the performance of informational models. Both informational models show acceptable agreement with the moment-rotation curves of the experiments. Adding a degradation parameter improves the informational models when modeling highly pinched hysteretic behavior. However, informational models cannot represent the contribution of individual components and therefore do not provide an insight into the underlying mechanics of components. In this study, a new hybrid modeling framework is proposed. In the hybrid framework, a conventional mathematical model is complemented by the informational methods. The basic premise of the proposed hybrid methodology is that not all features of system response are amenable to mathematical modeling, hence considering informational alternatives. This may be because (i) the underlying theory is not available or not sufficiently developed, or (ii) the existing theory is too complex and therefore not suitable for modeling within building frame analysis. The role of informational methods is to model aspects that the mathematical model leaves out. Autoprogressive algorithm and self-learning simulation extract the missing aspects from a system response. In a hybrid framework, experimental data is an integral part of modeling, rather than being used strictly for validation processes. The potential of the hybrid methodology is illustrated through modeling complex hysteretic behavior of beam-to-column connections. Mechanics-based components of deformation such as angles, flange-plates, and column panel zone, are idealized to a mathematical model by using a complete mechanical approach. Although the mathematical model represents envelope curves in terms of initial stiffness and yielding strength, it is not capable of capturing the pinching effects. Pinching is caused mainly by separation between angles and column flanges as well as slip between angles/flange-plates and beam flanges. These components of deformation are suitable for informational modeling. Finally, the moment-rotation curves of the hybrid models are validated with those of the experimental tests. The comparison shows that the hybrid models are capable of representing the highly pinched hysteretic behavior of beam-to-column connections. In addition, the developed hybrid model is successfully used to predict the behavior of a newly-designed connection.