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Elements of Computational Systems Biology.

By: Contributor(s): Series: Wiley Series in Bioinformatics SerPublisher: Hoboken : John Wiley & Sons, Incorporated, 2010Copyright date: ©2010Edition: 1st edDescription: 1 online resource (435 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780470556740
Subject(s): Genre/Form: Additional physical formats: Print version:: Elements of Computational Systems BiologyDDC classification:
  • 570.285
LOC classification:
  • QH324.2 -- .L63 2010eb
Online resources:
Contents:
Intro -- ELEMENTS OF COMPUTATIONAL SYSTEMS BIOLOGY -- CONTENTS -- PREFACE -- CONTRIBUTORS -- PART I OVERVIEW -- 1 Advances in Computational Systems Biology -- 1.1 Introduction -- 1.2 Multiscale Computational Modeling -- 1.3 Proteomics -- 1.4 Computational Systems Biology and Aging -- 1.5 Computational Systems Biology in Drug Design -- 1.6 Software Tools for Systems Biology -- 1.7 Conclusion -- References -- PART II BIOLOGICAL NETWORK MODELING -- 2 Models in Systems Biology: The Parameter Problem and the Meanings of Robustness -- 2.1 Introduction -- 2.2 Models as Dynamical Systems -- 2.2.1 Continuous Models -- 2.2.2 Discrete Models -- 2.3 The Parameter Problem -- 2.3.1 Parameterphobia -- 2.3.2 Measuring and Calculating -- 2.3.3 Counter Fitting -- 2.3.4 Beyond Fitting -- 2.4 The Landscapes of Dynamics -- 2.4.1 Qualitative Dynamics -- 2.4.2 Steady State Attractors of ODE Models -- 2.5 The Meanings of Robustness -- 2.5.1 Parameter Biology -- 2.5.2 Robustness to Initial Conditions -- 2.5.3 Robustness in Reality -- 2.5.4 Structural Stability -- 2.5.5 Classifying Robustness -- 2.6 Conclusion -- References -- 3 In Silico Analysis of Combined Therapeutics Strategy for Heart Failure -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 Model Construction and Validation -- 3.2.2 Classification of Different Heart Failure Cases -- 3.2.3 Simulation Protocol -- 3.3 Results -- 3.3.1 β-Adrenergic Receptor Antagonists -- 3.3.2 β-Adrenergic Receptor Kinase Inhibitor -- 3.3.3 Phosphodiesterase Inhibitor -- 3.3.4 Combined Therapies -- 3.4 Discussion -- Acknowledgment -- 3A.1 Appendix -- 3A.1.1 Model Validation -- 3A.1.2 The Mathematical Model Used for Simulations -- References -- 4 Rule-Based Modeling and Model Refinement -- 4.1 Kappa, Briefly -- 4.2 Refinement, Practically -- 4.2.1 A Simple Cascade -- 4.2.2 Another Cascade -- 4.2.3 The SSA Convention.
4.2.4 A Less Obvious Refinement -- 4.3 Rule-Based Modeling -- 4.3.1 Notation -- 4.3.2 Objects and Arrows -- 4.3.3 Extensions -- 4.3.4 Actions and Rules -- 4.3.5 Events and Probabilities -- 4.4 Refinement, Theoretically -- 4.4.1 Growth Policies -- 4.4.2 Simple Growth Policies -- 4.4.3 Neutral Refinements -- 4.4.4 Example Concluded -- 4.4.5 Growth Policies, Concretely -- 4.4.6 A Weakly Homogeneous Refinement -- 4.4.7 Nonhomogeneous Growth Policies -- 4.5 Conclusion -- References -- 5 A (Natural) Computing Perspective on Cellular Processes -- 5.1 Natural Computing and Computational Biology -- 5.2 Membrane Computing -- 5.3 Formal Languages Preliminaries -- 5.4 Membrane Operations with Peripheral Proteins -- 5.5 Membrane Systems with Peripheral Proteins -- 5.5.1 Dynamics of the System -- 5.5.2 Reachability in Membrane Systems -- 5.6 Cell Cycle and Breast Tumor Growth Control -- 5.6.1 Cell Cycle Progression Inhibition in G1/S -- 5.6.2 Cell-Cycle Progression Inhibition in G2/M -- References -- 6 Simulating Filament Dynamics in Cellular Systems -- 6.1 Introduction -- 6.2 Background: The Roles of Filaments within Cells -- 6.2.1 The Actin Network -- 6.2.2 Intermediate Filaments -- 6.2.3 Microtubules -- 6.3 Examples of Filament Simulations -- 6.3.1 Actin-Based Motility in Listeria -- 6.3.2 Kinetochore Positioning in Budding Yeast -- 6.3.3 Spindle Positioning in Caenorhabditis Elegans Embryos -- 6.3.4 Other Examples -- 6.4 Overview of Filament Simulation -- 6.5 Changing Filament Length -- 6.5.1 Resegmenting Filament -- 6.6 Forces on Filaments -- 6.6.1 Brownian Forces -- 6.6.2 Straightening Force -- 6.6.3 Forces from Motor Complexes -- 6.7 Imposing Constraints -- 6.7.1 Motivation -- 6.7.2 Derivation -- 6.7.3 Implementation -- 6.7.4 State Equation -- 6.8 Solver -- 6.9 Conclusion -- References -- PART III BIOLOGICAL NETWORK INFERENCE.
7 Reconstruction of Biological Networks by Supervised Machine Learning Approaches -- 7.1 Introduction -- 7.2 Graph Reconstruction as a Pattern Recognition Problem -- 7.2.1 Problem Formalization -- 7.2.2 Pattern Recognition -- 7.2.3 Graph Inference as a Pattern Recognition Problem -- 7.2.4 Graph Inference with Local Models -- 7.2.5 Graph Inference with Global Models -- 7.2.6 Remarks -- 7.3 Examples -- 7.3.1 Reconstruction of a Metabolic Network -- 7.3.2 Reconstruction of a PPI Network -- 7.3.3 Reconstruction of Gene Regulatory Networks -- 7.4 Discussion -- References -- 8 Supervised Inference of Metabolic Networks from the Integration of Genomic Data and Chemical Information -- 8.1 Introduction -- 8.2 Materials -- 8.2.1 Metabolic Network -- 8.2.2 Genomic Data -- 8.2.3 Chemical Information -- 8.2.4 Kernel Representation -- 8.3 Supervised Network Inference with Metric Learning -- 8.3.1 Formalism of the Problem -- 8.3.2 From Metric Learning to Graph Inference -- 8.4 Algorithms for Supervised Network Inference -- 8.4.1 Kernel Canonical Correlation Analysis (KCCA) -- 8.4.2 Distance Metric Learning (DML) -- 8.4.3 Kernel Matrix Regression (KMR) -- 8.4.4 Penalized Kernel Matrix Regression (PKMR) -- 8.4.5 Relationship with Kernel Matrix Completion and em-algorithm -- 8.5 Data Integration -- 8.5.1 Genomic Data Integration -- 8.5.2 Chemical Compatibility Network -- 8.5.3 Incorporating Chemical Constraint -- 8.6 Experiments -- 8.7 Discussion and Conclusion -- References -- 9 Integrating Abduction and Induction in Biological Inference Using CF-Induction -- 9.1 Introduction -- 9.2 Logical Modeling of Metabolic Flux Dynamics -- 9.2.1 Metabolic Pathways -- 9.2.2 Regulation of Enzymatic Activities -- 9.3 CF-induction -- 9.3.1 Inductive Logic Programming -- 9.3.2 Abduction and Induction in CF-induction -- 9.4 Experiments -- 9.4.1 A Simple Pathway.
9.4.2 A Metabolic Pathway of Pyruvate -- 9.5 Related Work -- 9.6 Conclusion and Future Work -- Acknowledgments -- References -- 10 Analysis and Control of Deterministic and Probabilistic Boolean Networks -- 10.1 Introduction -- 10.2 Boolean Network -- 10.3 Identification of Attractors -- 10.3.1 Definition of BN-ATTRACTOR -- 10.3.2 Basic Recursive Algorithm -- 10.3.3 On the Worst Case Time Complexity of BN-ATTRACTOR -- 10.4 Control of Boolean Network -- 10.4.1 Definition of BN-CONTROL -- 10.4.2 Dynamic Programming Algorithms for BN-CONTROL -- 10.4.3 NP-hardness Results on BN-CONTROL -- 10.5 Probabilistic Boolean Network -- 10.6 Computation of Steady States of PBN -- 10.6.1 Exact Computation of PBN-STEADY -- 10.6.2 Approximate Computation of PBN-STEADY -- 10.7 Control of Probabilistic Boolean Networks -- 10.7.1 Dynamic Programming Algorithm for PBN-CONTROL -- 10.7.2 Variants of PBN-CONTROL -- 10.8 Conclusion -- Acknowledgments -- References -- 11 Probabilistic Methods and Rate Heterogeneity -- 11.1 Introduction to Probabilistic Methods -- 11.2 Sequence Evolution is Described Using Markov Chains -- 11.2.1 Estimating Pairwise Distances -- 11.2.2 Calculating the Likelihood of a Tree -- 11.2.3 Extending the Basic Model -- 11.3 Among-site Rate Variation -- 11.4 Distribution of Rates Across Sites -- 11.4.1 The Gamma Distribution -- 11.4.2 Numerical Approximation of the Continuous Gamma Distribution -- 11.4.3 Alternative Rate Distributions -- 11.5 Site-specific Rate Estimation -- 11.6 Tree Reconstruction Using Among-site Rate Variation Models -- 11.7 Dependencies of Evolutionary Rates Among Sites -- 11.8 Related Works -- References -- PART IV GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY -- 12 From DNA Motifs to Gene Networks: A Review of Physical Interaction Models -- 12.1 Introduction -- 12.2 Fundamentals of Gene Transcription.
12.2.1 Physical Basis of Transcription Regulation and Representation of DNA Patterns -- 12.2.2 High-Throughput Data: Microarrays, Deep Sequencing, ChIP-chip, and ChIP-seq -- 12.3 Physical Interaction Algorithms -- 12.3.1 Basic Definitions -- 12.3.2 Problem Formulation -- 12.3.3 Clustering-Based Approaches -- 12.3.4 Sequence- or ChIP-Based Regression Methods -- 12.3.5 Network Component Analysis Methods -- 12.3.6 Factor Analysis Methods -- 12.4 Conclusion -- 12.4.1 Future Prospects and Challenges -- Acknowledgments -- References -- 13 The Impact of Whole Genome In Silico Screening for Nuclear Receptor-Binding Sites in Systems Biology -- 13.1 Introduction -- 13.2 Nuclear Receptors -- 13.2.1 NRs as a Link between Nutrition Sensing and Inflammation Prevention -- 13.2.2 NRs and System Biology -- 13.3 The PPAR Subfamily -- 13.3.1 Global Datasets that Identify a Central Role for PPARs in Disease Progression -- 13.3.2 PPAR Response Elements -- 13.4 Methods for in Silico Screening of Transcription Factor-Binding Sites -- 13.5 Binding Dataset of PPREs and the Classifier Method -- 13.6 Clustering of Known PPAR Target Genes -- 13.6.1 A Look at PPREs in their Genomic Context: Putative Target Genes and Binding Modules -- 13.7 Conclusion -- Acknowledgments -- References -- 14 Environmental and Physiological Insights from Microbial Genome Sequences -- 14.1 Some Background, Motivation, and Open Questions -- 14.2 A First Statistical Glimpse to Genomic Sequences -- 14.3 An Automatic Detection of Codon Bias in Genes -- 14.4 Genomic Signatures and a Space of Genomes for Genome Comparison -- 14.5 Study of Metabolic Networks Through Sequence Analysis and Transcriptomic Data -- 14.6 From Genome Sequences to Genome Synthesis: Minimal Gene Sets and Essential Genes -- 14.7 A Chromosomal Organization of Essential Genes.
14.8 Viral Adaptation to Microbial Hosts and Viral Essential Genes.
Summary: Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible toSummary: undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.
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Intro -- ELEMENTS OF COMPUTATIONAL SYSTEMS BIOLOGY -- CONTENTS -- PREFACE -- CONTRIBUTORS -- PART I OVERVIEW -- 1 Advances in Computational Systems Biology -- 1.1 Introduction -- 1.2 Multiscale Computational Modeling -- 1.3 Proteomics -- 1.4 Computational Systems Biology and Aging -- 1.5 Computational Systems Biology in Drug Design -- 1.6 Software Tools for Systems Biology -- 1.7 Conclusion -- References -- PART II BIOLOGICAL NETWORK MODELING -- 2 Models in Systems Biology: The Parameter Problem and the Meanings of Robustness -- 2.1 Introduction -- 2.2 Models as Dynamical Systems -- 2.2.1 Continuous Models -- 2.2.2 Discrete Models -- 2.3 The Parameter Problem -- 2.3.1 Parameterphobia -- 2.3.2 Measuring and Calculating -- 2.3.3 Counter Fitting -- 2.3.4 Beyond Fitting -- 2.4 The Landscapes of Dynamics -- 2.4.1 Qualitative Dynamics -- 2.4.2 Steady State Attractors of ODE Models -- 2.5 The Meanings of Robustness -- 2.5.1 Parameter Biology -- 2.5.2 Robustness to Initial Conditions -- 2.5.3 Robustness in Reality -- 2.5.4 Structural Stability -- 2.5.5 Classifying Robustness -- 2.6 Conclusion -- References -- 3 In Silico Analysis of Combined Therapeutics Strategy for Heart Failure -- 3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 Model Construction and Validation -- 3.2.2 Classification of Different Heart Failure Cases -- 3.2.3 Simulation Protocol -- 3.3 Results -- 3.3.1 β-Adrenergic Receptor Antagonists -- 3.3.2 β-Adrenergic Receptor Kinase Inhibitor -- 3.3.3 Phosphodiesterase Inhibitor -- 3.3.4 Combined Therapies -- 3.4 Discussion -- Acknowledgment -- 3A.1 Appendix -- 3A.1.1 Model Validation -- 3A.1.2 The Mathematical Model Used for Simulations -- References -- 4 Rule-Based Modeling and Model Refinement -- 4.1 Kappa, Briefly -- 4.2 Refinement, Practically -- 4.2.1 A Simple Cascade -- 4.2.2 Another Cascade -- 4.2.3 The SSA Convention.

4.2.4 A Less Obvious Refinement -- 4.3 Rule-Based Modeling -- 4.3.1 Notation -- 4.3.2 Objects and Arrows -- 4.3.3 Extensions -- 4.3.4 Actions and Rules -- 4.3.5 Events and Probabilities -- 4.4 Refinement, Theoretically -- 4.4.1 Growth Policies -- 4.4.2 Simple Growth Policies -- 4.4.3 Neutral Refinements -- 4.4.4 Example Concluded -- 4.4.5 Growth Policies, Concretely -- 4.4.6 A Weakly Homogeneous Refinement -- 4.4.7 Nonhomogeneous Growth Policies -- 4.5 Conclusion -- References -- 5 A (Natural) Computing Perspective on Cellular Processes -- 5.1 Natural Computing and Computational Biology -- 5.2 Membrane Computing -- 5.3 Formal Languages Preliminaries -- 5.4 Membrane Operations with Peripheral Proteins -- 5.5 Membrane Systems with Peripheral Proteins -- 5.5.1 Dynamics of the System -- 5.5.2 Reachability in Membrane Systems -- 5.6 Cell Cycle and Breast Tumor Growth Control -- 5.6.1 Cell Cycle Progression Inhibition in G1/S -- 5.6.2 Cell-Cycle Progression Inhibition in G2/M -- References -- 6 Simulating Filament Dynamics in Cellular Systems -- 6.1 Introduction -- 6.2 Background: The Roles of Filaments within Cells -- 6.2.1 The Actin Network -- 6.2.2 Intermediate Filaments -- 6.2.3 Microtubules -- 6.3 Examples of Filament Simulations -- 6.3.1 Actin-Based Motility in Listeria -- 6.3.2 Kinetochore Positioning in Budding Yeast -- 6.3.3 Spindle Positioning in Caenorhabditis Elegans Embryos -- 6.3.4 Other Examples -- 6.4 Overview of Filament Simulation -- 6.5 Changing Filament Length -- 6.5.1 Resegmenting Filament -- 6.6 Forces on Filaments -- 6.6.1 Brownian Forces -- 6.6.2 Straightening Force -- 6.6.3 Forces from Motor Complexes -- 6.7 Imposing Constraints -- 6.7.1 Motivation -- 6.7.2 Derivation -- 6.7.3 Implementation -- 6.7.4 State Equation -- 6.8 Solver -- 6.9 Conclusion -- References -- PART III BIOLOGICAL NETWORK INFERENCE.

7 Reconstruction of Biological Networks by Supervised Machine Learning Approaches -- 7.1 Introduction -- 7.2 Graph Reconstruction as a Pattern Recognition Problem -- 7.2.1 Problem Formalization -- 7.2.2 Pattern Recognition -- 7.2.3 Graph Inference as a Pattern Recognition Problem -- 7.2.4 Graph Inference with Local Models -- 7.2.5 Graph Inference with Global Models -- 7.2.6 Remarks -- 7.3 Examples -- 7.3.1 Reconstruction of a Metabolic Network -- 7.3.2 Reconstruction of a PPI Network -- 7.3.3 Reconstruction of Gene Regulatory Networks -- 7.4 Discussion -- References -- 8 Supervised Inference of Metabolic Networks from the Integration of Genomic Data and Chemical Information -- 8.1 Introduction -- 8.2 Materials -- 8.2.1 Metabolic Network -- 8.2.2 Genomic Data -- 8.2.3 Chemical Information -- 8.2.4 Kernel Representation -- 8.3 Supervised Network Inference with Metric Learning -- 8.3.1 Formalism of the Problem -- 8.3.2 From Metric Learning to Graph Inference -- 8.4 Algorithms for Supervised Network Inference -- 8.4.1 Kernel Canonical Correlation Analysis (KCCA) -- 8.4.2 Distance Metric Learning (DML) -- 8.4.3 Kernel Matrix Regression (KMR) -- 8.4.4 Penalized Kernel Matrix Regression (PKMR) -- 8.4.5 Relationship with Kernel Matrix Completion and em-algorithm -- 8.5 Data Integration -- 8.5.1 Genomic Data Integration -- 8.5.2 Chemical Compatibility Network -- 8.5.3 Incorporating Chemical Constraint -- 8.6 Experiments -- 8.7 Discussion and Conclusion -- References -- 9 Integrating Abduction and Induction in Biological Inference Using CF-Induction -- 9.1 Introduction -- 9.2 Logical Modeling of Metabolic Flux Dynamics -- 9.2.1 Metabolic Pathways -- 9.2.2 Regulation of Enzymatic Activities -- 9.3 CF-induction -- 9.3.1 Inductive Logic Programming -- 9.3.2 Abduction and Induction in CF-induction -- 9.4 Experiments -- 9.4.1 A Simple Pathway.

9.4.2 A Metabolic Pathway of Pyruvate -- 9.5 Related Work -- 9.6 Conclusion and Future Work -- Acknowledgments -- References -- 10 Analysis and Control of Deterministic and Probabilistic Boolean Networks -- 10.1 Introduction -- 10.2 Boolean Network -- 10.3 Identification of Attractors -- 10.3.1 Definition of BN-ATTRACTOR -- 10.3.2 Basic Recursive Algorithm -- 10.3.3 On the Worst Case Time Complexity of BN-ATTRACTOR -- 10.4 Control of Boolean Network -- 10.4.1 Definition of BN-CONTROL -- 10.4.2 Dynamic Programming Algorithms for BN-CONTROL -- 10.4.3 NP-hardness Results on BN-CONTROL -- 10.5 Probabilistic Boolean Network -- 10.6 Computation of Steady States of PBN -- 10.6.1 Exact Computation of PBN-STEADY -- 10.6.2 Approximate Computation of PBN-STEADY -- 10.7 Control of Probabilistic Boolean Networks -- 10.7.1 Dynamic Programming Algorithm for PBN-CONTROL -- 10.7.2 Variants of PBN-CONTROL -- 10.8 Conclusion -- Acknowledgments -- References -- 11 Probabilistic Methods and Rate Heterogeneity -- 11.1 Introduction to Probabilistic Methods -- 11.2 Sequence Evolution is Described Using Markov Chains -- 11.2.1 Estimating Pairwise Distances -- 11.2.2 Calculating the Likelihood of a Tree -- 11.2.3 Extending the Basic Model -- 11.3 Among-site Rate Variation -- 11.4 Distribution of Rates Across Sites -- 11.4.1 The Gamma Distribution -- 11.4.2 Numerical Approximation of the Continuous Gamma Distribution -- 11.4.3 Alternative Rate Distributions -- 11.5 Site-specific Rate Estimation -- 11.6 Tree Reconstruction Using Among-site Rate Variation Models -- 11.7 Dependencies of Evolutionary Rates Among Sites -- 11.8 Related Works -- References -- PART IV GENOMICS AND COMPUTATIONAL SYSTEMS BIOLOGY -- 12 From DNA Motifs to Gene Networks: A Review of Physical Interaction Models -- 12.1 Introduction -- 12.2 Fundamentals of Gene Transcription.

12.2.1 Physical Basis of Transcription Regulation and Representation of DNA Patterns -- 12.2.2 High-Throughput Data: Microarrays, Deep Sequencing, ChIP-chip, and ChIP-seq -- 12.3 Physical Interaction Algorithms -- 12.3.1 Basic Definitions -- 12.3.2 Problem Formulation -- 12.3.3 Clustering-Based Approaches -- 12.3.4 Sequence- or ChIP-Based Regression Methods -- 12.3.5 Network Component Analysis Methods -- 12.3.6 Factor Analysis Methods -- 12.4 Conclusion -- 12.4.1 Future Prospects and Challenges -- Acknowledgments -- References -- 13 The Impact of Whole Genome In Silico Screening for Nuclear Receptor-Binding Sites in Systems Biology -- 13.1 Introduction -- 13.2 Nuclear Receptors -- 13.2.1 NRs as a Link between Nutrition Sensing and Inflammation Prevention -- 13.2.2 NRs and System Biology -- 13.3 The PPAR Subfamily -- 13.3.1 Global Datasets that Identify a Central Role for PPARs in Disease Progression -- 13.3.2 PPAR Response Elements -- 13.4 Methods for in Silico Screening of Transcription Factor-Binding Sites -- 13.5 Binding Dataset of PPREs and the Classifier Method -- 13.6 Clustering of Known PPAR Target Genes -- 13.6.1 A Look at PPREs in their Genomic Context: Putative Target Genes and Binding Modules -- 13.7 Conclusion -- Acknowledgments -- References -- 14 Environmental and Physiological Insights from Microbial Genome Sequences -- 14.1 Some Background, Motivation, and Open Questions -- 14.2 A First Statistical Glimpse to Genomic Sequences -- 14.3 An Automatic Detection of Codon Bias in Genes -- 14.4 Genomic Signatures and a Space of Genomes for Genome Comparison -- 14.5 Study of Metabolic Networks Through Sequence Analysis and Transcriptomic Data -- 14.6 From Genome Sequences to Genome Synthesis: Minimal Gene Sets and Essential Genes -- 14.7 A Chromosomal Organization of Essential Genes.

14.8 Viral Adaptation to Microbial Hosts and Viral Essential Genes.

Groundbreaking, long-ranging research in this emergent field that enables solutions to complex biological problems Computational systems biology is an emerging discipline that is evolving quickly due to recent advances in biology such as genome sequencing, high-throughput technologies, and the recent development of sophisticated computational methodologies. Elements of Computational Systems Biology is a comprehensive reference covering the computational frameworks and techniques needed to help research scientists and professionals in computer science, biology, chemistry, pharmaceutical science, and physics solve complex biological problems. Written by leading experts in the field, this practical resource gives detailed descriptions of core subjects, including biological network modeling, analysis, and inference; presents a measured introduction to foundational topics like genomics; and describes state-of-the-art software tools for systems biology. Offers a coordinated integrated systems view of defining and applying computational and mathematical tools and methods to solving problems in systems biology Chapters provide a multidisciplinary approach and range from analysis, modeling, prediction, reasoning, inference, and exploration of biological systems to the implications of computational systems biology on drug design and medicine Helps reduce the gap between mathematics and biology by presenting chapters on mathematical models of biological systems Establishes solutions in computer science, biology, chemistry, and physics by presenting an in-depth description of computational methodologies for systems biology Elements of Computational Systems Biology is intended for academic/industry researchers and scientists in computer science, biology, mathematics, chemistry, physics, biotechnology, and pharmaceutical science. It is also accessible to

undergraduate and graduate students in machine learning, data mining, bioinformatics, computational biology, and systems biology courses.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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