Visual analysis of anatomy ontologies and related genomic information

Challenges in scientific research include the difficulty in obtaining overviews of the large amount of data required for analysis, and in resolving the differences in terminology used to store and interpret information in multiple, independently created data sets. Ontologies provide one solution for analysis involving multiple data sources, improving cross-referencing and data integration. This thesis looks at harnessing advanced human perception to reduce the cognitive load in the analysis of the multiple, complex data sets the bioinformatics user group studied use in research, taking advantage also of users’ domain knowledge, to build mental models of data that map to its underlying structure. Guided by a user-centred approach, prototypes were developed to provide a visual method for exploring users’ information requirements and to identify solutions for these requirements. 2D and 3D node-link graphs were built to visualise the hierarchically structured ontology data, to improve analysis of individual and comparison of multiple data sets, by providing overviews of the data, followed by techniques for detailed analysis of regions of interest. Iterative, heuristic and structured user evaluations were used to assess and refine the options developed for the presentation and analysis of the ontology data. The evaluation results confirmed the advantages that visualisation provides over text-based analysis, and also highlighted the advantages of each of 2D and 3D for visual data analysis.Overseas Research Students Awards SchemeJames Watt Scholarshi

Providing visualisation support for the analysis of anatomy ontology data

BACKGROUND: Improvements in technology have been accompanied by the generation of large amounts of complex data. This same technology must be harnessed effectively if the knowledge stored within the data is to be retrieved. Storing data in ontologies aids its management; ontologies serve as controlled vocabularies that promote data exchange and re-use, improving analysis. The Edinburgh Mouse Atlas Project stores the developmental stages of the mouse embryo in anatomy ontologies. This project is looking at the use of visual data overviews for intuitive analysis of the ontology data. RESULTS: A prototype has been developed that visualises the ontologies using directed acyclic graphs in two dimensions, with the ability to study detail in regions of interest in isolation or within the context of the overview. This is followed by the development of a technique that layers individual anatomy ontologies in three-dimensional space, so that relationships across multiple data sets may be mapped using physical links drawn along the third axis. CONCLUSION: Usability evaluations of the applications confirmed advantages in visual analysis of complex data. This project will look next at data input from multiple sources, and continue to develop the techniques presented to provide intuitive identification of relationships that span multiple ontologies

An ontology for cell types.

We describe an ontology for cell types that covers the prokaryotic, fungal, animal and plant worlds. It includes over 680 cell types. These cell types are classified under several generic categories and are organized as a directed acyclic graph. The ontology is available in the formats adopted by the Open Biological Ontologies umbrella and is designed to be used in the context of model organism genome and other biological databases. The ontology is freely available at http://obo.sourceforge.net/ and can be viewed using standard ontology visualization tools such as OBO-Edit and COBrA.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Simplified ontologies allowing comparison of developmental mammalian gene expression

The Developmental eVOC ontologies presented are simplified orthogonal ontologies describing the temporal and spatial distribution of developmental human and mouse anatomy

Systems developmental biology: the use of ontologies in annotating models and in identifying gene function within and across species

Systems developmental biology is an approach to the study of embryogenesis that attempts to analyze complex developmental processes through integrating the roles of their molecular, cellular, and tissue participants within a computational framework. This article discusses ways of annotating these participants using standard terms and IDs now available in public ontologies (these are areas of hierarchical knowledge formalized to be computationally accessible) for tissues, cells, and processes. Such annotations bring two types of benefit. The first comes from using standard terms: This allows linkage to other resources that use them (e.g., GXD, the gene-expression [G-E] database for mouse development). The second comes from the annotation procedure itself: This can lead to the identification of common processes that are used in very different and apparently unrelated events, even in other organisms. One implication of this is the potential for identifying the genes underpinning common developmental processes in different tissues through Boolean analysis of their G-E profiles. While it is easiest to do this for single organisms, the approach is extendable to analyzing similar processes in different organisms. Although the full computational infrastructure for such an analysis has yet to be put in place, two examples are briefly considered as illustration. First, the early development of the mouse urogenital system shows how a line of development can be graphically formalized using ontologies. Second, Boolean analysis of the G-E profiles of the mesenchyme-to-epithelium transitions that take place during mouse development suggest Lhx1, Foxc1, and Meox1 as candidate transcription factors for mediating this process

Artificial Leaf for Biofuel Production and Harvesting: Transport Phenomena and Energy Conversion

textMicroalgae cultivation has received much research attention in recent decades due to its high photosynthetic productivity and ability to produce biofuel feedstocks as well as high value compounds for the health food, cosmetics, and agriculture markets. Microalgae are conventionally grown in open pond raceways or closed photobioreactors. Due to the high water contents of these cultivation systems, they require large energy inputs for pumping and mixing the dilute culture, as well as concentrating and dewatering the resultant biomass. The energy required to operate these systems is generally greater than the energy contained in the resultant biomass, which precludes their use in sustainable biofuel production. To address this challenge, we designed a novel photobioreactor inspired by higher plants. In this synthetic leaf system, a modified transpiration mechanism is used which delivers water and nutrients to photosynthetic cells that grow as a biofilm on a porous, wicking substrate. Nutrient medium flow through the reactor is driven by evaporation, thereby eliminating the need for a pump. This dissertation outlines the design, construction, operation, and modeling of such a synthetic leaf system for energy positive biofuel production. First, a scaled down synthetic leaf reactor was operated alongside a conventional stirred tank photobioreactor. It was demonstrated that the synthetic leaf system required only 4% the working water volume as the conventional reactor, and showed growth rates as high as four times that of the conventional reactor. However, inefficiencies in the synthetic leaf system were identified and attributed to light and nutrient limitation of growth in the biofilm. To address these issues, a modeling study was performed with the aim of balancing the fluxes of photons and nutrients in the synthetic leaf environment. The vascular nutrient medium transport system was also modeled, enabling calculation of nutrient delivery rates as a function of environmental parameters and material properties of the porous membrane. These models were validated using an experimental setup in which the nutrient delivery rate, growth rate, and photosynthetic yield were measured for single synthetic leaves. The synthetic leaf system was shown to be competitive with existing technologies in terms of biomass productivity, while requiring zero energy for nutrient and gas delivery to the microorganisms. Future studies should focus on utilizing the synthetic leaf system for passive harvesting of secreted products in addition to passive nutrient delivery.Mechanical Engineerin

Learning from Plants - A Biologically Inspired Multi-Cellular Approach towards Multi-Functional Adaptive Structure based on Fluidic Flexible Matrix Composite.

Plants have many attractive characteristics for developing multi-functional adaptive structures, such as high strength and toughness per unit density, self-healing and reconfiguration, and nastic motion with short response time and large deformation. The vision of this thesis research is to develop enabling knowledgebase and design methodologies to synthesize plant-inspired adaptive structures. More specifically, investigations will focus on achieving multiple mechanical functionalities concurrently, such as actuation, variable mechanical properties, and vibration control. To reach this vision, this thesis research adopts the concept of multi-cellular structure based on the fluidic flexible matrix composite (F2MC) cells. Because such concept offers a natural platform to incorporate design inspirations from plants into artificial adaptive structure study, both at the local level of individual cell development and at the global level of structure architectural design and synthesis. This thesis research identifies several critical issues related to the development of F2MC based cellular adaptive structure. It investigates the dynamic characteristics of a multi-cellular structure, where F2MC cells with different configurations are connected to each other not only mechanically but also fluidically. It discovers new dynamic functionalities that are not feasible in an individual cell, including vibration isolation and dynamic actuation with enhanced authority within a designated frequency band. It provides a list of unique architectural designs of the cellular structure based on rigorous mathematical principles, and compares their performance to gain design insights. Finally, it derives novel and comprehensive synthesis procedures that are capable of selecting appropriate design variables for the F2MC cells, so that the cellular structure can achieve multiple performance targets concurrently, such as desired variable stiffness, actuation authority, and spectral data. The plant inspired design principles, physical knowledgebase and synthesis methodologies developed from this thesis fully manifests the rich functionalities and design versatilities of the F2MC based multi-cellular structure. They could foster the adoption of such novel adaptive structure concept to advance the state of art of many engineering applications, including aviation and aerospace, soft robotics, and intelligent civil infrastructure. The biologically inspired, multiple-cell oriented approach towards developing adaptive structure could also create a paradigm shift in other related academic research.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107105/1/wilsonli_1.pd

Development and implementation of ontology-based systems for mammalian gene expression profiling

Philosophiae Doctor - PhDThe use of ontologies in the mapping of gene expression events provides an effective and comparable method to determine the expression profile of an entire genome across a large collection of experiments derived from different expression sources. In this dissertation I describe the development of the developmental human and mouse eVOC ontologies and demonstrate the ontologies by identifying genes showing a bias for developmental brain expression in human and mouse, identifying transcription factor complexes, and exploring the mouse orthologs of human cancer/testis genes.Model organisms represent an important resource for understanding the fundamental aspects of mammalian biology. Mapping of biological phenomena between model organisms is complex and if it is to be meaningful, a simplified representation can be a powerful means for comparison. The implementation of the ontologies has been illustrated here in two ways.Firstly, the ontologies have been used to illustrate methods to determine clusters of genes showing tissue-restricted expression in humans. The identification of tissue restricted genes within an organism serves as an indication of the finetuning in the regulation of gene expression in a given tissue. Secondly, due to the differences in human and mouse gene expression on a temporal and spatial level, the ontologies were used to identify mouse orthologs of human cancer/testis genes showing cancer/testis characteristics. With the use of model systems such as mouse in the development of gene-targeted drugs in the treatment of disease, it is important to establish that the expression characteristics and profiles of a drug target in the model system is representative of the characteristics of the target in the system for which it is intended

Spatial description-based approach towards integration of biomedical atlases

Biomedical imaging has become ubiquitous in both basic research and the clinical sciences. As technology advances the resulting multitude of imaging modalities has led to a sharp rise in the quantity and quality of such images. Whether for epi- demiological studies, educational uses, clinical monitoring, or translational science purposes, the ability to integrate and compare such image-based data has become in- creasingly critical in the life sciences and eHealth domain. Ontology-based solutions often lack spatial precision. Image processing-based solutions may have di culties when the underlying morphologies are too di erent. This thesis proposes a compro- mise solution which captures location in biomedical images via spatial descriptions. Three approaches of spatial descriptions have been explored. These include: (1) spatial descriptions based on spatial relationships between segmented regions; (2) spatial descriptions based on ducial points and a set of spatial relations; and (3) spatial descriptions based on ducial points and a set of spatial relations, integrated with spatial relations between segmented regions. Evaluation, particularly in the context of mouse gene expression data, a good representative of spatio-temporal bi- ological data, suggests that the spatial description-based solution can provide good spatial precision. This dissertation discusses the need for biomedical image data in- tegration, the shortcomings of existing solutions and proposes new algorithms based on spatial descriptions of anatomical details in the image. Evaluation studies, par- ticularly in the context of gene expression data analysis, were carried out to study the performance of the new algorithms