eLearning

Control and coordination in biochemical networks - Introduction to the special section on systems biology

O. Wolkenhauer; B. K. Ghosh; Kwang-Hyun Cho IEEE Control Systems, 2004

First Page of the Article 

Interaction Motifs in Regulatory Networks and Structural Robustness

Adrien Elena; Jacques Demongeot 2008 International Conference on Complex, Intelligent and Software Intensive Systems, 2008

If we consider the regulatory networks used in systems biology, we observe certain regularities in their architecture, namely the occurrence of interaction motifs, i.e. of oriented sub-graphs relating their elements with the same interaction scheme. We propose to describe some of these universal motifs and to study their main dynamical properties especially concerning their robustness (called also structural stability), i.e. ...

Biomolecular implementation of linear I/O systems

K. Oishi; E. Klavins IET Systems Biology, 2011

Linear I/O systems are a fundamental tool in systems theory, and have been used to design complex circuits and control systems in a variety of settings. Here we present a principled design method for implementing arbitrary linear I/O systems with biochemical reactions. This method relies on two levels of abstraction: first, an implementation of linear I/O systems using idealised chemical ...

Feature - Circuits and systems for high-throughput biology

C. Nardini; L. Benini; G. De Micheli IEEE Circuits and Systems Magazine, 2006

High-throughput biological data acquisition and processing technologies have shifted the focus of biological research from the realm of traditional experimental science (wet biology) to that of information science (in silico biology). Powerful computation and communication means can be applied to the very large amount of apparently incoherent data coming from biomedical research. Part of the experiments and tests that we ...

Slowed propagation across the compacta-trabeculata interface: A consequence of fiber and sheet anisotropy

Stephen H. Gilbert; Alan P. Benson; Richard D. Walton; Olivier Bernus 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011

Transmural myocardial activation is influenced by myocardial structure, including structural differences between the compacta (Cta) and the trabeculata (Tta), although this has not been fully explained. Hearts from rats were Langendorff perfused, stained with DI-4-ANEPPS, the apex was cut off and fluorescence acquired from the exposed short-axis surface. The hearts were stimulated at 160 ms cycle length at the anterior, ...

IEEE-USA E-Books

• 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.

• 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.

• Index

  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.

• General Concepts

  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

• 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

• The Role of Modeling in Systems Biology

  This chapter contains sections titled: 1.1 Philosophical Overview, 1.2 Historical Context, 1.3 The Purposes and Implications of Modeling, 1.4 Different Kinds of Models, 1.5 Sensitivity Analysis1.6 Concluding Remarks, Acknowledgments

• 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.

• 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.

• On Modules and Modularity

  This chapter contains sections titled: 3.1 Introduction, 3.2 The Concept of Modules in Other Biological Disciplines, 3.3 The Concept of Modularity in Systems Biology, 3.4 Definition of Modules for Dynamic Networks, 3.5 Conclusions