Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and management
Provides a number of clearly described, detailed `recipes´ for analysis of systems data
Supplies a number of alternative approaches to modeling similar types of data
Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and management
Provides a number of clearly described, detailed `recipes´ for analysis of systems data
Supplies a number of alternative approaches to modeling similar types of data
Includes supplementary material: sn.pub/extras
Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (``systems´´) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1–3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5–15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent and wider scale.
Part I. Network Components1. Identification of cis-Regulatory Elements in Gene Co-Expression Networks Using A-GLAMLeonardo Mariño-Ramirez, Kannan Tharakaraman, Oliver Bodenreider, John Spouge, and David Landsman2. Structure-Based ab initio Prediction of Tranillegalscription Factor Binding SitesL. Angela Liu and Joel S. Bader3. Inferring Protein-Protein Interactions from Multiple Protein Domain CombinationsSimon P. Kanaan, Chengbang Huang, Stefan Wuchty, Danny Z. Chen, and Jesus Izaguirre4. Prediction of Protein-Protein Interactions: A Study of the Co-Evolution ModelItai Sharon, Jason V. Davis, and Golan Yona5. Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and IssuesEric Franzosa, Bolan Linghu, and Yu Xia6. Prediction and Integration of Regulatory and Protein-Protein InteractionsDuangdao Wichadakul, Jason McDermott, and Ram Samudrala7. Detecting Hierarchical Modulary in Biological NetworksErzsébet Ravasz ReganPart II. Network Inference8. Methods to Reconstruct and Compare Tranillegalscriptional Regulatory NetworksM. Madan Babu, Benjamin Lang, and L. Aravind9. Learning Global Models of Tranillegalscriptional Regulatory Networks from DataAviv Madar and Richard Bonneau10. Inferring Molecular Interactions Pathways from eQTL DataImran Rashid, Jason McDermott, and Ram Samudrala11. Methods for the Inference of Biological Pathways and NetworksRoger E. Bumgarner and Ka Yee YeungPart III. Network Dynamics12. Exploring Pathways from Gene Co-Expression to Network DynamicsHuai Li, Yu Sun, and Ming Zhan13. Network DynamicsHerbert M. Sauro14. Kinetic Modeling of Biological SystemsHaluk Resat, Linda Petzold, and Michel F. Pettigrew15. Guidance for DataCollection and Computational Modeling of Regulatory NetworksAdam Christopher Palmer and Keith Edward ShearwinPart IV. Function and Evolutionary Systems Biology16. A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene StructureLiran Carmel, Igor B. Rogozin, Yuri I. Wolf, and Eugene V. Koonin17. Enzyme Function Prediction with Interpretable ModelsUmar Syed and Golan Yona18. Using Evolutionary Information to Find Specificity Determining and Co-Evolving ResiduesGrigory Kolesov and Leonid A. Mirny19. Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics Jake Y. Chen, Eunseog Youn, and Sean D. Mooney20. Effects of Functional Bias on Supervised Learning of a Gene Network ModelInsuk Lee and Edward M. MarcottePart V. Computational Infrastructure for Systems Biology21. Comparing Algorithms for Clustering of Expression Data: How to Assess Gene ClustersGolan Yona, William Dirks, and Shafquat Rahman22. The Bioverse API and Web ApplicationMichal Guerquin, Jason McDermott, Zach Frazier, and Ram Samudrala23. Computational Representation of Biological SystemsZach Frazier, Jason McDermott, Michal Guerquin, and Ram Samudrala24. Biological Network Inference and Analysis using SEBINI and CABINRonald Taylor and Mudita Singhal
The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In Computational Systems Biology, expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism. The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed deillegalscription and implementation advice that is crucial for getting optimal results.
Comprehensive and up-to-date, Computational Systems Biology serves to motivate and inspire all those who wish to develop a complete deillegalscription of a biological system.
From the reviews:"The new volume of the Humana Press `Methods in Molecular Biology´ series, entitled `Computational Systems Biology,´ consists of 25 chapters authored by 57 specialists in the field. ... This book contains a wide collection of methods that ... gives a broad review of this fascinating and quickly developing field. ... for a computational biologist, it is a fascinating read, a broad and comprehensive resource on the current methods and approaches.” (Borys Wróbel, Acta Biochimica Polonica, Vol. 56, December, 2009)
This volume sees expert investigators contribute chapters which bring together biological data and computational/mathematical models of the data to aid researchers create a system that provides both predictive and mechanistic information for a model organism.
Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (''systems'') involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1-3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5-15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent andwider scale.
Network Components.- Identification of cis-Regulatory Elements in Gene Co-expression Networks Using A-GLAM.- Structure-Based Ab Initio Prediction of Tranillegalscription Factor-Binding Sites.- Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations.- Prediction of Protein-Protein Interactions: A Study of the Co-evolution Model.- Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and Issues.- Prediction and Integration of Regulatory and Protein-Protein Interactions.- Detecting Hierarchical Modularity in Biological Networks.- Network Inference.- Methods to Reconstruct and Compare Tranillegalscriptional Regulatory Networks.- Learning Global Models of Tranillegalscriptional Regulatory Networks from Data.- Inferring Molecular Interactions Pathways from eQTL Data.- Methods for the Inference of Biological Pathways and Networks.- Network Dynamics.- Exploring Pathways from Gene Co-expression to Network Dynamics.- Network Dynamics.- Kinetic Modeling of Biological Systems.- Guidance for Data Collection and Computational Modelling of Regulatory Networks.- Function and Evolutionary Systems Biology.- A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure.- Enzyme Function Prediction with Interpretable Models.- Using Evolutionary Information to Find Specificity-Determining and Co-evolving Residues.- Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics.- Effects of Functional Bias on Supervised Learning of a Gene Network Model.- Computational Infrastructure for Systems Biology.- Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters.- The Bioverse API and Web Application.- Computational Representation of Biological Systems.- Biological NetworkInference and Analysis Using SEBINI and CABIN.
From the reviews: "The new volume of the Humana Press 'Methods in Molecular Biology' series, entitled 'Computational Systems Biology,' consists of 25 chapters authored by 57 specialists in the field. ... This book contains a wide collection of methods that ... gives a broad review of this fascinating and quickly developing field. ... for a computational biologist, it is a fascinating read, a broad and comprehensive resource on the current methods and approaches." (Borys Wróbel, Acta Biochimica Polonica, Vol. 56, December, 2009)
Inhaltsverzeichnis
Part I. Network Components
1. Identification of cis-Regulatory Elements in Gene Co-Expression Networks Using A-GLAM
Leonardo Mariño-Ramirez, Kannan Tharakaraman, Oliver Bodenreider, John Spouge, and David Landsman
2. Structure-Based ab initio Prediction of Tranillegalscription Factor Binding Sites
L. Angela Liu and Joel S. Bader
3. Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations
Simon P. Kanaan, Chengbang Huang, Stefan Wuchty, Danny Z. Chen, and Jesus Izaguirre
4. Prediction of Protein-Protein Interactions: A Study of the Co-Evolution Model
Itai Sharon, Jason V. Davis, and Golan Yona
5. Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and Issues
Eric Franzosa, Bolan Linghu, and Yu Xia
6. Prediction and Integration of Regulatory and Protein-Protein Interactions
Duangdao Wichadakul, Jason McDermott, and Ram Samudrala
7. Detecting Hierarchical Modulary in Biological Networks
Erzsébet Ravasz Regan
Part II. Network Inference
8. Methods to Reconstruct and Compare Tranillegalscriptional Regulatory Networks
M. Madan Babu, Benjamin Lang, and L. Aravind
9. Learning Global Models of Tranillegalscriptional Regulatory Networks from Data
Aviv Madar and Richard Bonneau
10. Inferring Molecular Interactions Pathways from eQTL Data
Imran Rashid, Jason McDermott, and Ram Samudrala
11. Methods for the Inference of Biological Pathways and Networks
Roger E. Bumgarner and Ka Yee Yeung
Part III. Network Dynamics
12. Exploring Pathways from Gene Co-Expression to Network Dynamics
Huai Li, Yu Sun, and Ming Zhan
13. Network Dynamics
Herbert M. Sauro
14. Kinetic Modeling of Biological Systems
Haluk Resat, Linda Petzold, and Michel F. Pettigrew
15. Guidance for DataCollection and Computational Modeling of Regulatory Networks
Adam Christopher Palmer and Keith Edward Shearwin
Part IV. Function and Evolutionary Systems Biology
16. A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure
Liran Carmel, Igor B. Rogozin, Yuri I. Wolf, and Eugene V. Koonin
17. Enzyme Function Prediction with Interpretable Models
Umar Syed and Golan Yona
18. Using Evolutionary Information to Find Specificity Determining and Co-Evolving Residues
Grigory Kolesov and Leonid A. Mirny
19. Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics
Jake Y. Chen, Eunseog Youn, and Sean D. Mooney
20. Effects of Functional Bias on Supervised Learning of a Gene Network Model
Insuk Lee and Edward M. Marcotte
Part V. Computational Infrastructure for Systems Biology
21. Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters
Golan Yona, William Dirks, and Shafquat Rahman
22. The Bioverse API and Web Application
Michal Guerquin, Jason McDermott, Zach Frazier, and Ram Samudrala
23. Computational Representation of Biological Systems
Zach Frazier, Jason McDermott, Michal Guerquin, and Ram Samudrala
24. Biological Network Inference and Analysis using SEBINI and CABIN
Ronald Taylor and Mudita Singhal
Klappentext
Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (¿¿systems¿¿) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1¿3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5¿15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent andwider scale.
Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and management
rn
Provides a number of clearly described, detailed 'recipes' for analysis of systems data
rn
Supplies a number of alternative approaches to modeling similar types of data
rn
Includes supplementary material: sn.pub/extras
rn
