Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease

A base composition analysis of natural patterns for the preprocessing of metagenome sequences

Background: On the pretext that sequence reads and contigs often exhibit the same kinds of base usage that is also observed in the sequences from which they are derived, we offer a base composition analysis tool. Our tool uses these natural patterns to determine relatedness across sequence data. We introduce spectrum sets (sets of motifs) which are permutations of bacterial restriction sites and the base composition analysis framework to measure their proportional content in sequence data. We suggest that this framework will increase the efficiency during the pre-processing stages of metagenome sequencing and assembly projects. Results: Our method is able to differentiate organisms and their reads or contigs. The framework shows how to successfully determine the relatedness between these reads or contigs by comparison of base composition. In particular, we show that two types of organismal-sequence data are fundamentally different by analyzing their spectrum set motif proportions (coverage). By the application of one of the four possible spectrum sets, encompassing all known restriction sites, we provide the evidence to claim that each set has a different ability to differentiate sequence data. Furthermore, we show that the spectrum set selection having relevance to one organism, but not to the others of the data set, will greatly improve performance of sequence differentiation even if the fragment size of the read, contig or sequence is not lengthy. Conclusions: We show the proof of concept of our method by its application to ten trials of two or three freshly selected sequence fragments (reads and contigs) for each experiment across the six organisms of our set. Here we describe a novel and computationally effective pre-processing step for metagenome sequencing and assembly tasks. Furthermore, our base composition method has applications in phylogeny where it can be used to infer evolutionary distances between organisms based on the notion that related organisms often have much conserved code

Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning

Design of a novel continuous flow reactor for low pH viral inactivation

Currently the Biopharamaceutical industry is moving from operating in batch mode to continuous manufacturing. Low pH viral inactivation is a highly effective and a common method used in monoclonal antibody purification processes. During this unit operation the product is pooled and held, presenting a major bottle neck to end-to-end continuous downstream processing. Moving from a holding tank to a tubular reactor would provide for a means of processing materials continuously. The major challenges with tubular reactors for this application include limiting and characterizing the axial dispersion to ensure sufficient incubation time. The main objective of this work was to design and characterization of the residence time distribution (RTD), exit age of fluid elements leaving the reactor, of a continuous tubular reactor (CTR) for low pH viral inactivation. The following CTR design criteria were generated to streamline integration into the downstream purification process: (1) a ≤ 5 psi pressure drop along the length of the tube, (2) radial mixing within the reactor without moving parts to minimize axial dispersion, (3) a minimum residence time (MRT) approach was used to ensure that the desired product holding time was met, (4) operating at the laminar flow regime to limit shear on the product and minimize the pressure drop along the tube length while operating at flow rates sufficient for a 100 L bioreactor continuous process. Curved pipes offer improved radial mixing due to the formation of Dean Vortices via centrifugal forces. Thus, to reduce axial dispersion, the reactor as designed to include curvature in flow path via alternating 270 turns which also induced changes in the flow direction with each turn or flow inversions. A modular design with incubation chambers that can be connected in series was generated and evaluated using computation fluid dynamic (CFD) simulation before a final design was 3D printed and experimentally evaluated. Comprehensive computational fluid dynamics modeling in ANSYS Fluent of the CTR via velocity profile and secondary flow streamlines show enhanced radial mixing due to secondary flows and changes in flow direction. CFD simulation results were validated by pulsed tracer experiments and were in sufficient agreement, RTD variance values within 6.7%, with the computational model. Scaling the CTR with length to ~115.1 m at 50 ml/min, resulted in a MRT of 70.4 ± 0.46 mins with a pressure drop of ~0.7 psi. With increased length the dimensionless RTD profiles become more symmetrical and tighter about the mean residence time, indicating a smaller deviation from plug flow with increased length. Further scalability of the design is currently under investigation via generation and CFD analysis of a geometrical scale-down model for viral clearance studies

Landslide susceptibility mapping in North-East Wales

This is an Accepted Manuscript of an article published by Taylor & Francis in Geomatics, Natural Hazards and Risk on 4th October 2011, available online: https://doi.org/10.1080/19475705.2011.600778In North-East Wales, United Kingdom, slope instability is a known environmental hazard which has caused significant damage to the built environment in the recent past. This paper reports on the creation of a digital landslide inventory for North-East Wales and the use of a Geographical Information System (GIS) to create landslide susceptibility models that are applicable to landslide hazard management in the area. The research undertaken has resulted in the most comprehensive landslide inventory of North-East Wales to date, documenting 430 landslides within the area. Landslide susceptibility models created within a GIS using a statistical (multiple logistic regression) approach, divide the landscape of North-East Wales into areas of ‘low’, ‘moderate’ and ‘high’ landslide susceptibility using calculated probability values. These models indicate that 8% of the surface exposure of drift deposits and 12% of the area of solid geology is of high or very high susceptibility to slope instability. Validation tests have demonstrated the accuracy of these models and their potential value in a predictive sense. The digital landslide database and susceptibility models created are readily available to interested stakeholders, and may be useful tools in land-use planning, development of civil contingency plans and as guidance for the insurance industry

Climate Change and Extreme Weather Adaptation Options for Transportation Assets in the Bay Area Pilot Project

The Metropolitan Transportation Commission (MTC), the San Francisco Bay Conservation and Development Commission (BCDC), the California Department of Transportation, District 4 (Caltrans) and San Francisco Bay Area Rapid Transit District (BART) have partnered on a collaborative sub- regional pilot project to assess adaptation options for a subset of key transportation assets vulnerable to sea level rise in Alameda County. This study builds on the Adapting to Rising Tides: Transportation Vulnerability and Risk Assessment Pilot Project which was completed in 2011 and identified representative critical transportation assets vulnerable to sea level rise. Both projects were funded by the Federal Highway Administration. The first study developed detailed risk profiles for approximately 30 transportation assets including road, rail and transit. Having identified the risks, and in order to move from assessment to action, three focus areas within Alameda County containing \u2018core\u2019 transportation assets and \u2018adjacent\u2019 community assets were selected for further study to ensure a thorough understanding of their vulnerabilities. Once that enhanced vulnerability had been assessed, a set of detailed, representative adaptation strategies have been developed as potential solutions to protect key bridge, highway, transit and community assets from future inundation