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 ...

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