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Proceedings of the IEEE

Issue 8 • Date Aug. 2008

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Displaying Results 1 - 21 of 21
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • Proceedings of the IEEE publication information

    Page(s): C2
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    Freely Available from IEEE
  • Table of contents

    Page(s): 1245 - 1246
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    Freely Available from IEEE
  • The Quantum Limit to Moore's Law

    Page(s): 1247 - 1248
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    Freely Available from IEEE
  • Editorial to the Special Issue on Computational Systems Biology

    Page(s): 1249 - 1253
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    Freely Available from IEEE
  • CellDesigner 3.5: A Versatile Modeling Tool for Biochemical Networks

    Page(s): 1254 - 1265
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1379 KB) |  | HTML iconHTML  

    Understanding of the logic and dynamics of gene-regulatory and biochemical networks is a major challenge of systems biology. To facilitate this research topic, we have developed a modeling/simulating tool called CellDesigner. CellDesigner primarily has capabilities to visualize, model, and simulate gene-regulatory and biochemical networks. Two major characteristics embedded in CellDesigner boost its usability to create/import/export models: 1) solidly defined and comprehensive graphical representation (systems biology graphical notation) of network models and 2) systems biology markup language (SBML) as a model-describing basis, which function as intertool media to import/export SBML-based models. In addition, since its initial release in 2004, we have extended various capabilities of CellDesigner. For example, we integrated other systems biology workbench enabled simulation/analysis software packages. CellDesigner also supports simulation and parameter search, supported by integration with SBML ODE Solver, enabling users to simulate through our sophisticated graphical user interface. Users can also browse and modify existing models by referring to existing databases directly through CellDesigner. Those extended functions empower CellDesigner as not only a modeling/simulating tool but also an integrated analysis suite. CellDesigner is implemented in Java and thus supports various platforms (i.e., Windows, Linux, and MacOS X). CellDesigner is freely available via our Web site. View full abstract»

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  • The Simbios National Center: Systems Biology in Motion

    Page(s): 1266 - 1280
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1566 KB) |  | HTML iconHTML  

    Physics-based simulation is needed to understand the function of biological structures and can be applied across a wide range of scales, from molecules to organisms. Simbios (the national center for physics-based simulation of biological structures, http://www.simbios.stanford.edu/</weblink>) is one of seven NIH-supported national centers for biomedical computation. This article provides an overview of the mission and achievements of Simbios, and describes its place within systems biology. Understanding the interactions between various parts of a biological system and integrating this information to understand how biological systems function is the goal of systems biology. Many important biological systems comprise complex structural systems whose components interact through the exchange of physical forces, and whose movement and function is dictated by those forces. In particular, systems that are made of multiple identifiable components that move relative to one another in a constrained manner are multibody systems. Simbios' focus is creating methods for their simulation. Simbios is also investigating the biomechanical forces that govern fluid flow through deformable vessels, a central problem in cardiovascular dynamics. In this application, the system is governed by the interplay of classical forces, but the motion is distributed smoothly through the materials and fluids, requiring the use of continuum methods. In addition to the research aims, Simbios is working to disseminate information, software and other resources relevant to biological systems in motion. View full abstract»

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  • Multigraph Conditions for Multistability, Oscillations and Pattern Formation in Biochemical Reaction Networks

    Page(s): 1281 - 1291
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (476 KB) |  | HTML iconHTML  

    We represent interactions among biochemical species using a directed multigraph, which is a generalization of a more commonly used digraph. We show that network properties that are known to lead to multistability or oscillations, such as the existence of a positive feedback cycle, can be generalized to ldquocritical subnetworksrdquo that can contain several cycles. We also derive corresponding graph-theoretic conditions for pattern formation for the respective reaction-diffusion models. We present as an example a model for cell cycle and apoptosis along with bifurcation diagrams and sample solutions that confirm the predictions obtained with the help of the multigraph network conditions. View full abstract»

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  • The Future of Large-Scale Collaborative Proteomics

    Page(s): 1292 - 1309
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1107 KB) |  | HTML iconHTML  

    The postgenomics era has witnessed a rapid change in biological methods for knowledge elucidation and pharmacological approaches to biomarker discovery. Differential expression of proteins in health and disease holds the key to early diagnosis and accelerated drug discovery. This approach, however, has also brought an explosion of data complexity not mirrored by existing progress in proteome informatics. It has become apparent that the task is greater than that can be tackled by individual laboratories alone and large-scale open collaborations of the new human proteome organization (HUPO) have highlighted major challenges concerning the integration and cross-validation of results across different laboratories. This paper describes the state-of-the-art proteomics workflows (two-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry) and their utilization by the participants of the HUPO initiatives towards comprehensive mapping of the brain, liver, and plasma proteomes. Particular emphasis is given to the limitations of the underlying data analysis techniques for large-scale collaborative proteomics. Emerging paradigms including statistical data normalization, direct image registration, spectral libraries, and high-throughput computation with Web-based bioinformatics services are discussed. It is envisaged that these methods will provide the basis for breaking the bottleneck of large-scale automated proteome mapping and biomarker discovery. View full abstract»

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  • Computational Systems Bioinformatics and Bioimaging for Pathway Analysis and Drug Screening

    Page(s): 1310 - 1331
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2590 KB) |  | HTML iconHTML  

    The premise of today's drug development is that the mechanism of a disease is highly dependent upon underlying signaling and cellular pathways. Such pathways are often composed of complexes of physically interacting genes, proteins, or biochemical activities coordinated by metabolic intermediates, ions, and other small solutes and are investigated with molecular biology approaches in genomics, proteomics, and metabonomics. Nevertheless, the recent declines in the pharmaceutical industry's revenues indicate such approaches alone may not be adequate in creating successful new drugs. Our observation is that combining methods of genomics, proteomics, and metabonomics with techniques of bioimaging will systematically provide powerful means to decode or better understand molecular interactions and pathways that lead to disease and potentially generate new insights and indications for drug targets. The former methods provide the profiles of genes, proteins, and metabolites, whereas the latter techniques generate objective, quantitative phenotypes correlating to the molecular profiles and interactions. In this paper, we describe pathway reconstruction and target validation based on the proposed systems biologic approach and show selected application examples for pathway analysis and drug screening. View full abstract»

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  • Fuzzy Fractal Analysis of Molecular Imaging Data

    Page(s): 1332 - 1347
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1714 KB) |  | HTML iconHTML  

    Recent advances in biomedicine, pharmacology, and biotechnology open doors to the understanding how diseases are developed at the molecular and physiological level. This gain of understanding tremendously helps facilitate the design and discovery of drugs for therapeutic treatment. Despite the advances in the technology and new knowledge in systems biology, drug discovery is still a low process without utilizing scientific computations that allow precise and rapid analysis of biological processes under trials. This paper particularly addresses fractals as a computational tool for analyzing molecular imaging data that appear to be very useful sources of information for understanding the interactions and behaviors of complex biological networks and the development of predictive medicine. We study herein some fractal characteristics of fluorescent microscope images of peroxisomes, and propose the conceptual frameworks of fuzzy mixture fractal dimensions and fractal distortion measures for bioimage classification. View full abstract»

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  • Synthetic Images of High-Throughput Microscopy for Validation of Image Analysis Methods

    Page(s): 1348 - 1360
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (936 KB) |  | HTML iconHTML  

    Automated image analysis provides a powerful tool when quantifying various characteristics of cell populations. Previously, the validation of image analysis results has been a task of expert biologist, who has manually analyzed the images and provided the ground truth to which the proposed analysis results have been compared. The traditional validation approach, prone to errors and variation, is unfeasible in the emergence of high-throughput measurement systems which make human-based analysis excessively laborious. The systems biology approach for studying, e.g., cellular activity massively in parallel, often lending on high-throughput microscopy, further increases the need for efficient, validated computational methods. As a solution for the problem, we propose a computational framework for simulating fluorescence microscopy images of cell populations. The simulation framework allows generation of synthetic images with realistic characteristics including the ground truth for validation. Thus, the simulation enables validation and performance analysis for various analysis algorithms. By creating a parameterized model of cells based on a given population, the simulator is able to create different cell types. The proposed modular framework, combined with the ability to create high-throughput measurements, provides a powerful tool for validating image analysis methods in traditional microscopy as well as in high content screening. Moreover, we use experimental data to study the validity of the proposed modeling approach. View full abstract»

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  • Modeling and Analyzing Biological Oscillations in Molecular Networks

    Page(s): 1361 - 1385
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (747 KB) |  | HTML iconHTML  

    One of the major challenges for postgenomic biology is to understand how genes, proteins, and small molecules dynamically interact to form molecular networks which facilitate sophisticated biological functions. In this paper, we present a survey on recent developments on modelling molecular networks and analyzing synchronization of bio-oscillators in multicellular systems from the viewpoint of systems biology. Attention will be focused on deriving general theoretical results to understand the dynamical behaviors of biological systems based on nonlinear dynamical and control theory. Specifically, we first describe the stochastic and deterministic approaches to model molecular networks and give a brief comparison between them. Then, we explain how to construct a molecular network, in particular, a gene regulatory network with specific functions, e.g., switches and oscillators, in individual cells at the molecular level by using feedback systems, and how to model a general multicellular system with the consideration of external fluctuations and intercellular coupling to study the general cooperative behaviors for a population of bio-oscillators. Finally, as an illustrative example, a synthetic multicellular system is designed to show how synchronization is effectively achieved and how dynamics of individual cells is efficiently controlled. Some recent developments and perspectives of analysis on biological oscillations in future are also discussed. View full abstract»

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  • Signals and Systems: Towards a Systems Biology of Signal Transduction

    Page(s): 1386 - 1397
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (531 KB) |  | HTML iconHTML  

    Systems biology seeks to understand how the behavior of cells and organisms arises from the collective interactions of their component molecules. I will discuss how signal transduction- the process by which cells sense and respond to external signals - is being reconsidered from a systems perspective. This relies on ideas and concepts from the physical sciences coupled to new experimental strategies. I will outline some of the challenges through work in our laboratory on epidermal growth factor signalling. View full abstract»

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  • Stability and Time-Delay Modeling of Negative Feedback Loops

    Page(s): 1398 - 1410
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (613 KB) |  | HTML iconHTML  

    Negative feedback loops are a common cellular motif underlying many important gene regulation pathways, and therefore a clear understanding of their dynamics is important for the study of gene regulation. Here we analyze the linear stability of negative feedback loops with and without explicit time delays. When the degradation rates of each loop component are identical, we derive the analytical solution of the condition of marginal stability. In the most general case, when the degradation rates of each component differ, we derive a novel expansion of the condition of marginal stability in terms of the geometric mean and variance of the degradation rates. We use these results to demonstrate how the mathematical representation of a negative feedback loop can be simplified such that the stability characteristics are maintained. Finally we apply these results to study the stability structure of the p53-Mdm2 feedback loop. By simplifying a model involving nuclear and cytoplasmatic molecular species of mdm2 mRNA and Mdm2 protein to a model involving only nuclear components and a time delay (which summarizes transcriptional, nuclear import and export, and translational time scales), we demonstrate how our methods allow for an elucidation of the dynamic instabilities recently observed experimentally in the p53-Mdm2 system as the transcription of p53 changes from low to middle to high rates. View full abstract»

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  • Centrality, Network Capacity, and Modularity as Parameters to Analyze the Core-Periphery Structure in Metabolic Networks

    Page(s): 1411 - 1420
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1696 KB) |  | HTML iconHTML  

    Genome-scale metabolic networks of organisms are normally very large and complex. Previous studies have shown that they are organized in a hierarchical and modular manner. In particular, a core-periphery modular organization structure has been proposed for metabolic networks. However, no methods or parameters are available in the literature to quantitatively evaluate or find the hierarchical and modular structure of metabolic networks. In this paper, we propose a parameter called ldquocore coefficientrdquo to quantitatively evaluate the core-periphery structure of a metabolic network. This parameter is defined based on the concept of closeness centrality of metabolites and a newly defined parameter: network capacity. To find or define the core and the periphery modules of a metabolic network, we further developed a method to decompose metabolic networks based on a quantitative parameter of modularity and a procedure of core extraction. The method has been developed with genome-scale metabolic networks of five representative organisms, which include Aeropyrum pernix, Bacillus subtilis, Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens . The results were compared with two artificially generated network models. View full abstract»

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  • The Lymph Node B Cell Immune Response: Dynamic Analysis In-Silico

    Page(s): 1421 - 1443
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3610 KB)  

    Lymph nodes are organs in which lymphocytes respond to antigens to generate, among other cell types, plasma cells that secrete specific antibodies and memory lymphocytes for enhanced future responses to the antigen. To achieve these ends, the lymph node (LN) has to orchestrate the meeting and interactions between the antigen and various cell types including the rare clones of B cells and T cells bearing receptors for the antigen. The process is dynamic in essence and involves chemotaxis of responding cells through various anatomical compartments of the LN and selective cell differentiation, proliferation and programmed death. Understanding the LN requires a dynamic integration of the mass of data generated by extensive experimentation. Here, we present a fully executable, bottom-up computerized model of the LN using the visual language of Statecharts and the technology of reactive animation (RA) to create a dynamic front-end. We studied the effects of amount of antigen and LN size on the emergent properties of lymphocyte dynamics, differentiation and anatomic localization. The dynamic organization of the LN visualized by RA sheds new light on how the immune system transforms antigen stimulation into a highly sensitive, yet buffered response. View full abstract»

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  • Electrical Engineering Hall of Fame: Dugald C. Jackson

    Page(s): 1444 - 1446
    Save to Project icon | Request Permissions | PDF file iconPDF (524 KB)  
    Freely Available from IEEE
  • Future Special Issues/Special Sections of the Proceedings

    Page(s): 1447 - 1448
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    Freely Available from IEEE
  • IEEE Potentials is looking for article submissions

    Page(s): C3
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    Freely Available from IEEE
  • [Back cover]

    Page(s): C4
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    Freely Available from IEEE

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H. Joel Trussell
North Carolina State University