Ligand dependent intra and inter subunit communication in human tryptophanyl tRNA synthetase as deduced from the dynamics of structure networks

Homodimeric protein tryptophanyl tRNA synthetase (TrpRS) has a Rossmann fold domain and belongs to the 1c subclass of aminoacyl tRNA synthetases. This enzyme performs the function of acylating the cognate tRNA. This process involves a number of molecules (2 protein subunits, 2 tRNAs and 2 activated Trps) and thus it is difficult to follow the complex steps in this process. Structures of human TrpRS complexed with certain ligands are available. Based on structural and biochemical data, mechanism of activation of Trp has been speculated. However, no structure has yet been solved in the presence of both the tRNA(Trp) and the activated Trp (TrpAMP). In this study, we have modeled the structure of human TrpRS bound to the activated ligand and the cognate tRNA. In addition, we have performed molecular dynamics (MD) simulations on these models as well as other complexes to capture the dynamical process of ligand induced conformational changes. We have analyzed both the local and global changes in the protein conformation from the protein structure network (PSN) of MD snapshots, by a method which was recently developed in our laboratory in the context of the functionally monomeric protein, methionyl tRNA synthetase. From these investigations, we obtain important information such as the ligand induced correlation between different residues of this protein, asymmetric binding of the ligands to the two subunits of the protein as seen in the crystal structure analysis, and the path of communication between the anticodon region and the aminoacylation site. Here we are able to elucidate the role of dimer interface at a level of detail, which has not been captured so far.

Greedy Scheduling in Multihop Wireless Networks

We study the performance of greedy scheduling in multihop wireless networks where the objective is aggregate utility maximization. Following standard approaches, we consider the dual of the original optimization problem. Optimal scheduling requires selecting independent sets of maximum aggregate price, but this problem is known to be NP-hard. We propose and evaluate a simple greedy heuristic. Analytical bounds on performance are provided and simulations indicate that the greedy heuristic performs well in practice.

Novel Molecular Mechanisms of Arabidopsis Disease Resistance

Plants are capable of recognizing phytopathogens through the perception of pathogen-derived molecules or plant cell-wall degradation products due to the activities of pathogen-secreted enzymes. Such elicitor recognition events trigger an array of inducible defense responses involving signal transduction networks and massive transcriptional re-programming. The outcome of a pathogen infection relies on the balance between different signaling pathways, which are integrated by regulatory proteins. This thesis characterized two key regulatory components: a damage control enzyme, chlorophyllase 1 (AtCHL1), and a transcription factor, WRKY70. Their roles in defense signaling were then investigated. The Erwinia-derived elicitors rapidly activated the expression of AtCLH1 and WRKY70 through different signaling pathways. The expression of the AtCHL1 gene was up-regulated by jasmonic acid (JA) but down-regulated by salicylic acid (SA), whereas WRKY70 was activated by SA and repressed by JA. In order to elucidate the functions of AtCLH1 and WRKY70 in plant defense, stable transgenic lines were produced where these genes were overexpressed or silenced. Additionally, independent knockout lines were also characterized. Bacterial and fungal pathogens were then used to assess the contribution of these genes to the Arabidopsis disease resistance. The transcriptional modulation of AtCLH1 by either the constitutive over-expression or RNAi silencing caused alterations in the chlorophyll-to-chlorophyllide ratio, supporting the claim that chlorophyllase 1 has a role in the chlorophyll degradation pathway. Silencing of this gene led to light-dependent over-accumulation of the reactive oxygen species (ROS) in response to infection by Erwinia carotovora subsp. carotovora SCC1. This was followed by an enhanced induction of SA-dependent defense genes and an increased resistance to this pathogen. Interestingly, little effect on the pathogen-induced SA accumulation at the early infection was observed, suggesting that action of ROS might potentiate SA signaling. In contrast, the pathogen-induced JA production was significantly reduced in the RNAi silenced plants. Moreover, JA signaling and resistance to Alternaria brassicicola were impaired. These observations provide support for the argument that the ROS generated in chloroplasts might have a negative impact on JA signaling. The over-expression of WRKY70 resulted in an enhanced resistance to E. carotovora subsp. carotovora SCC1, Pseudomonas syringae pv. tomato DC3000 and Erysiphe cichoracearum UCSC1, whilst an antisense suppression or an insertional inactivation of WRKY70 led to a compromised resistance to E. carotovora subsp. carotovora SCC1 and to E. cichoracearum UCSC1 but not to P. syringae pv. tomato DC3000. Gene expression analysis revealed that WRKY70 activated many known defense-related genes associated with the SAR response but suppressed a subset of the JA-responsive genes. In particular, I was able to show that both the basal and the induced expression of AtCLH1 was enhanced by the antisense silencing or the insertional inactivation of WRKY70, whereas a reduction in AtCLH1 expression was observed in the WRKY70 over-expressors following an MeJA application or an A. brassicicola infection. Moreover, the SA-induced suppression of AtCLH1 was relieved in wrky70 mutants. These results indicate that WRKY70 down-regulates AtCLH1. An epistasis analysis suggested that WRKY70 functions downstream of the NPR1 in an SA-dependent signaling pathway. When challenged with A. brassicicola, WRKY70 over-expressing plants exhibited a compromised disease resistance while wrky70 mutants had the opposite effect. These results confirmed the WRKY70-mediated inhibitory effects on JA signaling. Furthermore, the WRKY70-controlled suppression of A. brassicicola resistance was mainly through an NPR1-dependent mechanism. Taking all the data together, I suggest that the pathogen-responsive transcription factor WRKY70 is a common component in both SA- and JA-dependent pathways and plays a crucial role in the SA-mediated suppression of JA signaling.

Posters versus lurkers. Improving participation in enterprise social networks through management interventions

This thesis investigated the phenomenon of underutilised Enterprise social networks (ESNs). Guided by established theories, we identified key reasons that drive ESN members to either post (i.e., create content) or lurk (i.e., read others' content) and examined the influence of three management interventions - aim to boost participation - on lurkers' and posters' beliefs and participation. We test our model with data collected from 366 members in Google⁺ communities in a large Australian retail organization. We find that posters and lurkers are motivated and hindered by different factors. Moreover, management interventions do not – always – yield the hoped-for results among lurkers.

Comparison of the Radar and Seeker Modes of Pursuer Guidance

This paper compares closed-loop performance of seeker-based and radar-based estimators for surface-to-air interception through 6-degree-of-freedom simulation using proportional navigation guidance.Ground radar measurements are evader range, azimuth and elevation angles contaminated by Gaussian noise. Onboard seeker measurements are pursuer-evader relative range, range rate also contaminated by Gaussian noise. The gimbal angles and line-of-sight rates in the gimbal frame,contaminated by time-correlated non-Gaussian noise with realistic numerical values are also available as measurements. In both the applications, extended Kalman filter with Gaussian noise assumption are used for state estimation. For a typical engagement, it is found that,based on Monte Carlo studies, seeker estimator outperforms radar estimator in terms of autopilot demand and reduces the miss distance.Thus, a seeker estimator with white Gaussian assumption is found to be adequate to handle the measurements even in the presence of non-Gaussian correlated noise. This paper uses realistic numerical values of all noise parameters.

Novel 3-dimensional sixfold interpenetrating diamondoid networks of copper(I) coordination polymers of polypyridyl ligands - Syntheses,characterization and crystal structures

Two new coordination polymers [Cu(L-1)(2)](n)(ClO4)(n)center dot 2nH(2)O (1), [Cu(L-2)(2)](n)(ClO4)(n)center dot 2nH(2)O (2) of polydentate imine/pyridyl ligands, L-1 and L-2 with Cu(I) ion have been synthesized and characterized by single crystal X-ray diffraction studies, elemental analyses, IR' UV-vis and NMR spectroscopy. They represent 3-dimensional, sixfold interpenetrating diamondoid network structures having large pores of dimension, 35 x 21 angstrom(2) in 1 and 38 x 19 angstrom(2) in 2, respectively.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

A flexible tool for integrated planning of active distribution networks

This paper presents a flexible and integrated planning tool for active distribution network to maximise the benefits of having high level s of renewables, customer engagement, and new technology implementations. The tool has two main processing parts: “optimisation” and “forecast”. The “optimization” part is an automated and integrated planning framework to optimize the net present value (NPV) of investment strategy for electric distribution network augmentation over large areas and long planning horizons (e.g. 5 to 20 years) based on a modified particle swarm optimization (MPSO). The “forecast” is a flexible agent-based framework to produce load duration curves (LDCs) of load forecasts for different levels of customer engagement, energy storage controls, and electric vehicles (EVs). In addition, “forecast” connects the existing databases of utility to the proposed tool as well as outputs the load profiles and network plan in Google Earth. This integrated tool enables different divisions within a utility to analyze their programs and options in a single platform using comprehensive information.

Mycobacterial Stress Regulation: The Dps ``Twin Sister'' Defense Mechanism and Structure-Function Relationship

In this work, we have tried to emphasize the connection between mycobacterial growth and regulation of gene expression. Utilization of multiple carbon sources and diauxic growth helps bacteria to regulate gene expression at an optimum level so that the inhospitable conditions encountered during nutrient depletion can be circumvented. These aspects will be discussed with respect to mycobacterial growth in subsequent sections. Identification and characterization of genes induced under such conditions is helpful to understand the physiology of the bacterium. Although it is necessary to compare the total expression profile of proteins as they transit from vegetative growth to stationary phase, at times a lot of insights can be deciphered from the expression pattern of one or two proteins. We have compared the protein expression and sigma factor selectivity of two such proteins in M. smegmatis to understand the differential regulation of genes playing diverse function in the same species. Some newer insights on the structure and function of one of the Dps proteins are also explained.

Allostery and conformational free energy changes in human tryptophanyl-tRNA synthetase from essential dynamics and structure networks

The interdependence of the concept of allostery and enzymatic catalysis, and they being guided by conformational mobility is gaining increased prominence. However, to gain a molecular level understanding of llostery and hence of enzymatic catalysis, it is of utter importance that the networks of amino acids participating in allostery be deciphered. Our lab has been exploring the methods of network analysis combined with molecular dynamics simulations to understand allostery at molecular level. Earlier we had outlined methods to obtain communication paths and then to map the rigid/flexible regions of proteins through network parameters like the shortest correlated paths, cliques, and communities. In this article, we advance the methodology to estimate the conformational populations in terms of cliques/communities formed by interactions including the side-chains and then to compute the ligand-induced population shift. Finally, we obtain the free-energy landscape of the protein in equilibrium, characterizing the free-energy minima accessed by the protein complexes. We have chosen human tryptophanyl-tRNA synthetase (hTrpRS), a protein esponsible for charging tryptophan to its cognate tRNA during protein biosynthesis for this investigation. This is a multidomain protein exhibiting excellent allosteric communication. Our approach has provided valuable structural as well as functional insights into the protein. The methodology adopted here is highly generalized to illuminate the linkage between protein structure networks and conformational mobility involved in the allosteric mechanism in any protein with known structure.

An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Correlation, Coding, and Cooperation in Wireless Sensor Networks

We consider a single-hop data-gathering sensor network, consisting of a set of sensor nodes that transmit data periodically to a base-station. We are interested in maximizing the lifetime of this network. With our definition of network lifetime and the assumption that the radio transmission energy consumption forms the most significant portion of the total energy consumption at a sensor node, we attempt to enhance the network lifetime by reducing the transmission energy budget of sensor nodes by exploiting three system-level opportunities. We pose the problem of maximizing lifetime as a max-min optimization problem subject to the constraint of successful data collection and limited energy supply at each node. This turns out to be an extremely difficult optimization to solve. To reduce the complexity of this problem, we allow the sensor nodes and the base-station to interactively communicate with each other and employ instantaneous decoding at the base-station. The chief contribution of the paper is to show that the computational complexity of our problem is determined by the complex interplay of various system-level opportunities and challenges.