Conferences related to Systems Biology

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2016 IEEE 55th IEEE Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.

  • 2014 IEEE 53rd Annual Conference on Decision and Control (CDC)

    Largest annual conference in control theory and its applications. Areas covered all applied math, communication, control, aerospace, biology, etc.

  • 2013 IEEE 52nd Annual Conference on Decision and Control (CDC)

    The 52nd IEEE Conference on Decision and Control will be held Tuesday through Friday, December 10-13, 2013 at the Congress Centre in Firenze, Italy. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss the latest advancements of the discipline, shape its future directions, and promote its diffusion among the scientific community at large. The 52nd CDC will feature the presentation of contributed and invited papers, as well as tutorial sessions and workshops. The CDC is hosted by the IEEE Control Systems Society (CSS), and is organized in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).

  • 2012 IEEE 51st Annual Conference on Decision and Control (CDC)

    The conference discusses advances in theory, design and application of control systems. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the control systems from analysis and design through simulation and hardware. Its scope shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organiz

  • 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011)

    This conference is dedicated to the advancement of the theory and practice of systems and control, bringing together an international community of researchers and practitioners to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.

  • 2010 49th IEEE Conference on Decision and Control (CDC)

    Theory and applications of control theory and control systems technology

  • 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC)

    This conference is dedicated to the advancement of the theory and practice of systems and control, bringing together an international community of researchers and practitioners to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

The 2013 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS’13) will be held in Houston, TX during November 17-19, 2013. GENSIPS’13 will provide a forum for signal processing researchers, bioinformaticians, computational biologists, biomedical engineers, and biostatisticians to exchange ideas and discuss the challenges confronting computational bioinformatics and systems biology communities due to the high modality of disparate high-throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics.

  • 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Computational bioinformatics and systems biology communities due to the high modality of disparate high-throughput data, high variability of data acquisition, high dimensionality of biomedical data, and high complexity of genomics and proteomics. The theme of GENSIPS 12 is Methods in Next-Generation Sequencing and Cancer Systems Biology and GENSIPS will feature prominent plenary speakers including John Quackenbush of Harvard University, Victor Velculescu of Johns Hopkins University, Eberhard Voit of Georgia Tech, Jinghui Zhang of St. Jude Children's Research Hospital, as well as special sessions in cancer research.

  • 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    To address the computational issues in the emerging field of computational genomics and proteomics, and to improve participation from not only within SP but computer science, statistics and biomedical communities. There are several advantages of hosting GENSIPS in San Antonio, which will help advance our goals.

  • 2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Recent advances in genomic studies have stimulated synergistic research in many cross - disciplinary areas. Genomic data presents enormous challenges for signal processing and statistics, which has led to the development of the new field of Genomic Signal Processing (GSP). The seventh IEEE International Workshop on Genomic Signal Processing and Statistics will provide an international scientific forum devoted to the area of GSP and its applications in system biology and medicine.

  • 2009 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

    Recent advances in genomic studies have stimulated synergistic research in many cross-disciplinary areas. Genomic data presents enormous challenges for signal processing and statistics, which has led to the development of the new field of Genomic Signal Processing (GSP). The seventh IEEE International Workshop on Genomic Signal Processing and Statistics will provide an international scientific forum devoted to the area of GSP and its applications in system biology and medicine. The aim of the workshop is to


2012 24th Chinese Control and Decision Conference (CCDC)

Chinese Control and Decision Conference is an annual international conference to create a forum for scientists, engineers and practitioners throughout the world to present the latest advancement in Control, Decision, Automation, Robotics and Emerging Technologies.


2012 46th Annual Conference on Information Sciences and Systems (CISS)

Theoretical advances, applications and ideas in the fields of information theory (including application to biological sciences); communication, networking, signal, image and video processing; systems and control; learning and statistical inference.


2012 5th International Conference on Biomedical Engineering and Informatics (BMEI)

BMEI is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and informatics. Specific topics include Biomedical imaging and visualization; Biomedical signal processing and analysis; etc.

  • 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI)

    CISP 11-BMEI 11 is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and informatics. Specific topics include Biomedical imaging and visualization; Biomedical signal processing and analysis; Biomedical instrumentation, devices, sensors, artificial organs, and nano technologies; Rehabilitation engineering; bioinformatics and medical informatics, etc.

  • 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI)

    BMEI 10 is a premier international forum for scientists and researchers to present the state of the art of biomedical engineering and biomedical informatics. It is co-located with the 3rd International Congress on Image and Signal Processing (CISP 2010) to promote interactions biomedical research and signal processing.


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Periodicals related to Systems Biology

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Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Intelligent Systems, IEEE

IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...


Nanobioscience, IEEE Transactions on

Basic and applied papers dealing both with engineering, physics, chemistry, and computer science and with biology and medicine with respect to bio-molecules and cells. The content of acceptable papers ranges from practical/clinical/environmental applications to formalized mathematical theory. TAB #73-June 2001. (Original name-IEEE Transactions on Molecular Cellular and Tissue Engineering). T-NB publishes basic and applied research papers dealing with the study ...




Xplore Articles related to Systems Biology

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ANN Based Protein Function Prediction Using Integrated Protein-Protein Interaction Data

Lei Shi; Young-Rae Cho; Aidong Zhang 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009

A major challenge in the post-genomic era is to determine protein function on a proteomic scale. There are only less than half of the actual functional annotations available for a typical proteome. The recent high-throughput bio- techniques have provided us large-scale protein-protein interaction data, and many studies have shown that function prediction from protein-protein interaction data is a promising way ...


A dynamic Bayesian framwork to learn temporal gene interactions using external knowledge

Umut Ağyüz; Şenol İşçi; Cengizhan Öztürk; Ahmet Ademoğlu; Hasan H. Otu 2013 8th International Symposium on Health Informatics and Bioinformatics, 2013

One of the main problems in systems biology is learning gene interaction networks from experimental data. This turns out to be a challenging task as the experimental data is sparse and noisy, and network learning algorithms are computationally intense. Bayesian Networks (BN) have become a popular choice for learning such networks as BNs avoid overfitting and are robust to noise. ...


Consensus in Continuous-Time Multiagent Systems Under Discontinuous Nonlinear Protocols

Bo Liu; Wenlian Lu; Tianping Chen IEEE Transactions on Neural Networks and Learning Systems, 2015

In this paper, we provide a theoretical analysis for nonlinear discontinuous consensus protocols in networks of multiagents over weighted directed graphs. By integrating the analytic tools from nonsmooth stability analysis and graph theory, we investigate networks with both fixed topology and randomly switching topology. For networks with a fixed topology, we provide a sufficient and necessary condition for asymptotic consensus, ...


New Descriptors of Evolutionary Information for Accurate Prediction of DNA and RNA-Binding Residues in Protein Sequences

Liangjiang Wang; Caiyan Huang 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009

Evolutionary information in terms of position-specific scoring matrix (PSSM) has been often used to construct classifiers for biological sequence analyses. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modeling specific sequence patterns. In this study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of ...


Parameter-dependent Lyapunov functional for stability of time-delay systems with polytopic-type uncertainties

Yong He; Min Wu; Jin-Hua She; Guo-Ping Liu IEEE Transactions on Automatic Control, 2004

This note concerns the problem of the robust stability of a linear system with a time-varying delay and polytopic-type uncertainties. In order to construct a parameter-dependent Lyapunov functional for the system, we first devised a new method of dealing with a time-delay system without uncertainties. In this method, the derivative terms of the state, which is in the derivative of ...


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Educational Resources on Systems Biology

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eLearning

ANN Based Protein Function Prediction Using Integrated Protein-Protein Interaction Data

Lei Shi; Young-Rae Cho; Aidong Zhang 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009

A major challenge in the post-genomic era is to determine protein function on a proteomic scale. There are only less than half of the actual functional annotations available for a typical proteome. The recent high-throughput bio- techniques have provided us large-scale protein-protein interaction data, and many studies have shown that function prediction from protein-protein interaction data is a promising way ...


A dynamic Bayesian framwork to learn temporal gene interactions using external knowledge

Umut Ağyüz; Şenol İşçi; Cengizhan Öztürk; Ahmet Ademoğlu; Hasan H. Otu 2013 8th International Symposium on Health Informatics and Bioinformatics, 2013

One of the main problems in systems biology is learning gene interaction networks from experimental data. This turns out to be a challenging task as the experimental data is sparse and noisy, and network learning algorithms are computationally intense. Bayesian Networks (BN) have become a popular choice for learning such networks as BNs avoid overfitting and are robust to noise. ...


Consensus in Continuous-Time Multiagent Systems Under Discontinuous Nonlinear Protocols

Bo Liu; Wenlian Lu; Tianping Chen IEEE Transactions on Neural Networks and Learning Systems, 2015

In this paper, we provide a theoretical analysis for nonlinear discontinuous consensus protocols in networks of multiagents over weighted directed graphs. By integrating the analytic tools from nonsmooth stability analysis and graph theory, we investigate networks with both fixed topology and randomly switching topology. For networks with a fixed topology, we provide a sufficient and necessary condition for asymptotic consensus, ...


New Descriptors of Evolutionary Information for Accurate Prediction of DNA and RNA-Binding Residues in Protein Sequences

Liangjiang Wang; Caiyan Huang 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009

Evolutionary information in terms of position-specific scoring matrix (PSSM) has been often used to construct classifiers for biological sequence analyses. However, PSSM is rather designed for PSI-BLAST searches, and it may not contain all the evolutionary information for modeling specific sequence patterns. In this study, several new descriptors of evolutionary information have been developed and evaluated for sequence-based prediction of ...


Parameter-dependent Lyapunov functional for stability of time-delay systems with polytopic-type uncertainties

Yong He; Min Wu; Jin-Hua She; Guo-Ping Liu IEEE Transactions on Automatic Control, 2004

This note concerns the problem of the robust stability of a linear system with a time-varying delay and polytopic-type uncertainties. In order to construct a parameter-dependent Lyapunov functional for the system, we first devised a new method of dealing with a time-delay system without uncertainties. In this method, the derivative terms of the state, which is in the derivative of ...


More eLearning Resources

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IEEE-USA E-Books

  • Models and Reality

    Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.

  • Systems Biology Thought Experiments in Human Genetics Using Artificial Life and Grammatical Evolution

    A goal of systems biology and human genetics is to understand how DNA sequence variations impact human health through a hierarchy of biochemical, metabolic, and physiological systems. We present here a proof-of-principle study that demonstrates how artificial life in the form of agent-based simulation can be used to generate hypothetical systems biology models that are consistent with pre-defined genetic models of disease susceptibility. Here, an evolutionary computing strategy called grammatical evolution is utilized to discover artificial life models. The goal of these studies is to perform thought experiments about the nature of complex biological systems that are consistent with genetic models of disease susceptibility. It is anticipated that the utility of this approach will be the generation of biological hypotheses that can then be tested using experimental systems.

  • A Software Tools for Biological Modeling

    This chapter contains section titled: A.1 Genetic Network Analyzer: GNA, A.2 Gene Interaction Network Simulator: GINsim, A.3 Discrete Dynamics Lab: DDLab, A.4 Cellerator, A.5 Sigmoid, A.6 Metatool, A.7 FluxAnalyzer, A.8 ScrumPy, A.9 Jarnac, A.10 Gepasi, A.11 MesoRD, A.12 Ingeneue, A.13 XPPAUT, A.14 BioSens, A.15 JigCell, A.16 Oscill8, A.17 Madonna, A.18 Systems Biology Workbench

  • References

    Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.

  • The Challenges of Systems Biology

    This chapter contains sections titled: 1 Genomics, Gene Expression, and Next- Generation Sequencing, 2 Metabolic Network Reconstruction, 3 Computational Models of Gene Translation, 4 Reverse Engineering of Cellular Networks, 5 Outlook, Note, References

  • Computational Modeling

    Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.

  • Contributors

    Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.

  • Analyzing Gene Expression Imaging Data in Developmental Biology

    This chapter describes the application of data-intensive methods to the automatic identification and annotation of gene expression patterns in the mouse embryo. The first section of the chapter introduces ideas behind modern computational and systems biology, how the explosion of data in the postgenomic world has led to new possibilities and even greater challenges. The second section talks about the particular computational biology problem and describes annotating images of gene expression with the right anatomical terms in depth. An automated solution based on data-intensive methods is discussed the third section. The final section looks ahead to the biological significance and systems biology application of these approaches and also describes a large-scale challenge and possible series of experiments with a novel data-intensive computational architecture.

  • Modeling Approaches

    Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. This comprehensive introduction and overview of system modeling in biology makes the relevant background material from all pertinent fields accessible to researchers with different backgrounds.The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. This book fills that gap. It is the first to provide the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.

  • Multilevel Modeling in Systems Biology: From Cells to Whole Organs

    This chapter contains section titled: 14.1 Introduction: The Philosophy of Multilevel Simulation, 14.2 Cellular Models of the Heart, 14.3 Connecting to Ion Pumps and Calcium Cycling, 14.4 Linking to the Genetic Level, 14.5 Linking to Biochemistry: Counterintuitive Predictions, 14.6 Linking to Pharmacology: Assessing and Predicting Drug Actions, 14.7 Linking to Tissues and Organs, 14.8 Coronary Circulation, 14.9 The Future: From Genome to Proteome to Physiome, Acknowledgments



Standards related to Systems Biology

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Jobs related to Systems Biology

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