Representing and analysing molecular and cellular function in the computer

Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

Visual Debugging of Object-Oriented Systems with the Unified Modeling Language

The Department of Defense (DoD) is developing a Joint Battlespace Infosphere, linking a large number of data sources and user applications. Debugging and analysis tools are required to aid in this process. Debugging of large object-oriented systems is a difficult cognitive process that requires understanding of both the overall and detailed behavior of the application. In addition, many such applications linked through a distributed system add to this complexity. Standard debuggers do not utilize visualization techniques, focusing mainly on information extracted directly from the source code. To overcome this deficiency, this research designs and implements a methodology that enables developers to analyze, troubleshoot and evaluate object-oriented systems using visualization techniques. It uses the standard UML class diagram coupled with visualization features such as focus+context, animation, graph layout, color encoding and filtering techniques to organize and present information in a manner that facilitates greater program and system comprehension. Multiple levels of abstraction, from low-level details such as source code and variable information to high-level structural detail in the form of a UML class diagram are accessible along with views of the program s control flow. The methods applied provide a considerable improvement (up to 1110%) in the number of classes that can be displayed in a set display area while still preserving user context and the semantics of UML, thus maintaining system understanding. Usability tests validated the application in terms of three criteria software visualization, debugging, and general system usability

Visualization of Metabolic Networks

The metabolism constitutes the universe of biochemical reactions taking place in a cell of an organism. These processes include the synthesis, transformation, and degradation of molecules for an organism to grow, to reproduce and to interact with its environment. A good way to capture the complexity of these processes is the representation as metabolic network, in which sets of molecules are transformed into products by a chemical reaction, and the products are being processed further. The underlying graph model allows a structural analysis of this network using established graphtheoretical algorithms on the one hand, and a visual representation by applying layout algorithms combined with information visualization techniques on the other. In this thesis we will take a look at three different aspects of graph visualization within the context of biochemical systems: the representation and interactive exploration of static networks, the visual analysis of dynamic networks, and the comparison of two network graphs. We will demonstrate, how established infovis techniques can be combined with new algorithms and applied to specific problems in the area of metabolic network visualization. We reconstruct the metabolic network covering the complete set of chemical reactions present in a generalized eucaryotic cell from real world data available from a popular metabolic pathway data base and present a suitable data structure. As the constructed network is very large, it is not feasible for the display as a whole. Instead, we introduce a technique to analyse this static network in a top-down approach starting with an overview and displaying detailed reaction networks on demand. This exploration method is also applied to compare metabolic networks in different species and from different resources. As for the analysis of dynamic networks, we present a framework to capture changes in the connectivity as well as changes in the attributes associated with the network’s elements

Analysis of Graph Layout Algorithms for Use in Command and Control Network Graphs

This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a multiple objective statistics designed to inform on the algorithms performance in both aesthetic characteristics defined in literature, as well as some characteristics defined by the research sponsor. The results suggest that the Dot algorithm performs better with respect to the sponsor defined characteristics, whereas the Fruchterman-Reingold algorithm performs better on aesthetic characteristics

Doctor of Philosophy

dissertationA broad range of applications capture dynamic data at an unprecedented scale. Independent of the application area, finding intuitive ways to understand the dynamic aspects of these increasingly large data sets remains an interesting and, to some extent, unsolved research problem. Generically, dynamic data sets can be described by some, often hierarchical, notion of feature of interest that exists at each moment in time, and those features evolve across time. Consequently, exploring the evolution of these features is considered to be one natural way of studying these data sets. Usually, this process entails the ability to: 1) define and extract features from each time step in the data set; 2) find their correspondences over time; and 3) analyze their evolution across time. However, due to the large data sizes, visualizing the evolution of features in a comprehensible manner and performing interactive changes are challenging. Furthermore, feature evolution details are often unmanageably large and complex, making it difficult to identify the temporal trends in the underlying data. Additionally, many existing approaches develop these components in a specialized and standalone manner, thus failing to address the general task of understanding feature evolution across time. This dissertation demonstrates that interactive exploration of feature evolution can be achieved in a non-domain-specific manner so that it can be applied across a wide variety of application domains. In particular, a novel generic visualization and analysis environment that couples a multiresolution unified spatiotemporal representation of features with progressive layout and visualization strategies for studying the feature evolution across time is introduced. This flexible framework enables on-the-fly changes to feature definitions, their correspondences, and other arbitrary attributes while providing an interactive view of the resulting feature evolution details. Furthermore, to reduce the visual complexity within the feature evolution details, several subselection-based and localized, per-feature parameter value-based strategies are also enabled. The utility and generality of this framework is demonstrated by using several large-scale dynamic data sets

A layout algorithm for hierarchical graphs with constraints

A new method is developed for reducing edge crossings in the layout of directed graphs for display. The method will reduce edge crossings in graphs which have constraints on the location or movement of some of the nodes. This has not been available in previously published methods. An analysis of the strategies used to choose rank pairs for edge crossing reduction shows that this choice will dramatically affect the amount of crossings eliminated. This method is directly applicable to the reduction of edge crossings in the general graph

A new dynamical layout algorithm for complex biochemical reaction networks

BACKGROUND: To study complex biochemical reaction networks in living cells researchers more and more rely on databases and computational methods. In order to facilitate computational approaches, visualisation techniques are highly important. Biochemical reaction networks, e.g. metabolic pathways are often depicted as graphs and these graphs should be drawn dynamically to provide flexibility in the context of different data. Conventional layout algorithms are not sufficient for every kind of pathway in biochemical research. This is mainly due to certain conventions to which biochemists/biologists are used to and which are not in accordance to conventional layout algorithms. A number of approaches has been developed to improve this situation. Some of these are used in the context of biochemical databases and make more or less use of the information in these databases to aid the layout process. However, visualisation becomes also more and more important in modelling and simulation tools which mostly do not offer additional connections to databases. Therefore, layout algorithms used in these tools have to work independently of any databases. In addition, all of the existing algorithms face some limitations with respect to the number of edge crossings when it comes to larger biochemical systems due to the interconnectivity of these. Last but not least, in some cases, biochemical conventions are not met properly. RESULTS: Out of these reasons we have developed a new algorithm which tackles these problems by reducing the number of edge crossings in complex systems, taking further biological conventions into account to identify and visualise cycles. Furthermore the algorithm is independent from database information in order to be easily adopted in any application. It can also be tested as part of the SimWiz package (free to download for academic users at [1]). CONCLUSION: The new algorithm reduces the complexity of pathways, as well as edge crossings and edge length in the resulting graphical representation. It also considers existing and further biological conventions to create a drawing most biochemists are familiar with. A lot of examples can be found on [2]

A layout algorithm for undirected compount graphs

Cataloged from PDF version of article.Graph layout is an important problem in information visualization. All datadriven graph-based information visualization systems require some sort of an automatic geometry generation mechanism, as it is generally not directly available from the data being modeled. This is why graph layout problem has been studied extensively. However, for the case of compound graphs, there are still important gaps in this area. We present a new, elegant algorithm for undirected compound graph layout. The algorithm is based on the traditional force-directed layout scheme with extensions to handle nesting, varying node sizes, and possibly other application-specific constraints. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory. The algorithm has also been successfully implemented as part of a pathway integration and analysis toolkit named Patika for drawing complicated biological pathways with compartmental constraints and arbitrary nesting relations to represent molecular complexes and various types of pathway abstractions.Giral, ErhanM.S