Crop Systems Biology Narrowing the gaps between crop modelling and genetics 1st Edition by Xinyou Yin, Paul Struik 3319205625 9783319205625

Crop Systems Biology: Narrowing the gaps between crop modelling and genetics 1st Edition by Xinyou Yin, Paul C. Struik – Ebook PDF Instant Download/DeliveryISBN: 3319205625, 9783319205625 

Full download Crop Systems Biology: Narrowing the gaps between crop modelling and genetics 1st Edition after payment.

Product details:

ISBN-10 :   3319205625 

ISBN-13 :  9783319205625

Author:  Xinyou Yin, Paul C. Struik 

The sequencing of genomes has been completed for an increasing number of crop species, and researchers have now succeeded in isolating and characterising many important QTLs/genes. High expectations from genomics, however, are waving back toward the recognition that crop physiology is also important for realistic improvement of crop productivity. Complex processes and networks along various hierarchical levels of crop growth and development can be thoroughly understood with the help of their mathematical description – modelling. The further practical application of these understandings also requires quantitative predictions. In order to better support design, engineering and breeding for new crops and cultivars for improving agricultural production under global warming and climate change, there is an increasing call for an interdisciplinary research approach, which combines modern genetics and genomics, traditional physiology and biochemistry, and advanced bioinformatics and modelling. Such an interdisciplinary approach has been practised in various research groups for many years. However, it does not seem to be fully covered in the format of book publications. We want to initiate a book project on crop systems biology – narrowing the gaps between genotypes and phenotypes and the gaps between crop modelling and genetics/genomics, for publication in 2013/2014. The book will be meant for those scientists and graduate students from fundamental plant biology and applied crop science who are interested in bridging the gap between these two fields. We have invited a group of scientists (who have very good track records in publishing excellent papers in this field or in a closely related area) to contribute chapters to this new book, and they have agreed to do so.​

Crop Systems Biology: Narrowing the gaps between crop modelling and genetics 1st Table of contents:

Chapter 1: Challenges in Integrating Genetic Control in Plant and Crop Models
1.1 Introduction
1.2 Fundamentals in Quantitative Genetics
1.2.1 Quantitative Traits Controlled by Numerous Genes
1.2.2 Principles and Methods of QTL Cartography
1.2.3 QTL Genetic Parameters
1.3 Integration of Genetic Control in Crop Models
1.3.1 Levels of Integration
1.3.2 QTL-based Modelling
1.3.3 Gene-based Modelling
1.4 Modelling Cellular Networks
1.4.1 Modelling and Analyzing Gene Regulatory Networks
1.4.1.1 Qualitative Models of Gene Regulatory Networks
1.4.2 Modelling and Analyzing Metabolic Networks
1.4.2.1 Steady-state Models of Metabolic Networks
1.4.2.2 Dynamical Models of Metabolic Networks
1.5 Integrating Cellular Networks into Plant Models
1.5.1 Model Simplification
1.5.1.1 Structural Analysis
1.5.1.2 Time Scale Analysis
1.5.1.3 Metabolic Control Analysis
1.5.2 Coupling Among Scales
1.6 Conclusions, Open Issues and Perspectives
Appendix 1: Qualitative Modelling
Logical and Boolean Model
Piece-wise Linear (PL) Model
Petri Net Model
Appendix 2: Constraint-based Models
Metabolic Pathway Analysis
Flux Balance Analysis and Related Techniques
References
Chapter 2: Simulating Genotype-Phenotype Interaction Using Extended Functional-Structural Plant Mode
2.1 Introduction and Overview: Using Morphogenetic Models in Cereal Breeding
2.2 Functional-Structural Plant Models of Cereals
2.2.1 A New Functional-Structural Plant Model for Rice
2.3 Coupling the Functional-Structural Plant Model of Rice with Quantitative Genetic Information
2.4 RiceBreeder: A Tool for Visual Virtual Breeding
2.5 Current Pitfalls and Problems
2.6 How Can Virtual Breeding Become a Useful Tool in the Near Future?
References
Chapter 3: Modelling of Genotype by Environment Interaction and Prediction of Complex Traits acro
3.1 Introduction
3.2 Statistical Models to Predict Phenotypic Performance
3.2.1 Statistical Models to Predict Performance of Unobserved Genotypes in Observed Environments
3.2.2 Statistical Models to Predict Performance of Observed Genotypes in New Environments
3.2.3 Statistical Models to Predict Performance of Unobserved Genotypes in New Environments
3.3 Crop Growth Models to Predict Genotypic Performance
3.4 Further Considerations
3.4.1 Classification of Environments
3.4.2 Population Structure
3.4.3 Next Generation Sequencing
3.4.4 High-Throughput Phenotyping to Input to Models for Phenotype Prediction
3.5 Concluding Remarks
References
Chapter 4: Process-Based Simulation Models Are Essential Tools for Virtual Profiling and Design of
4.1 Introduction
4.2 What Are Process-Based Simulation Models for Fruit and Root Systems?
4.2.1 Virtual Fruit Model
4.2.2 Root System Architecture Model
4.3 Virtual Profiling on Roots and Fruits
4.3.1 Roots
4.3.2 Fruit
4.4 Ideotype Design
4.4.1 Sensitivity Analysis, a Key-Step Before Designing Ideotypes
4.4.2 Multi-objective Optimisation Algorithms to Design Ideoypes
4.5 Conclusion
References
Chapter 5: Heuristic Exploration of Theoretical Margins for Improving Adaptation of Rice through
5.1 Introduction
5.1.1 Integrative Modeling of the Genetic and Environmental Control of Complex Traits
5.1.2 Mechanisms of Early Vigor and Their Interaction with Drought
5.1.3 Modeling Early Vigor and Drought Interactions
5.2 Materials and Methods
5.2.1 Ecomeristem Model
5.2.2 Model Calibration on Rice Diversity Panel
5.2.3 Simulation Experiments
5.2.4 Statistical Analyses
5.3 Results
5.3.1 Predicted Shoot Dry Matter under Different Water Regimes
5.3.2 Evaluation of Vigor and Drought Response by Cluster of Response Type
5.3.3 Principal Component Analysis for Model Parameter Values, Vigor and Drought Sensitivity
5.4 Discussion
5.4.1 Critical Appreciation of the Conceptual Approach
5.4.2 Experimental Design versus Type of Drought Resistance and Vigor
5.4.3 Theoretical Scope for Improving Vigor
5.4.4 Theoretical Scope for Improving Drought Resistance
5.4.5 Perspectives for Crop Modeling Support to Breeders and Geneticists
5.5 Conclusions
References
Chapter 6: Limited-Transpiration Trait for Increased Yield for Water-Limited Soybean: From Model
6.1 Introduction
6.2 Initial Model Exploration of Limited Transpiration Trait
6.3 Studies of Limited-Transpiration Phenotype
6.4 Studies of Limited-Transpiration Genotype
6.4.1 Cell, Cell Wall and Development
6.4.2 Secondary, Hormone and Lipid Metabolism
6.4.3 RNA Regulation of Transcription
6.4.4 Protein, Signaling and Transport
6.4.5 Stress Genes
6.5 Application of Limited-Transpiration Trait
6.5.1 Breeding Progress
6.5.2 Model Assessment of Benefit in the USA
6.6 Conclusions
References
Chapter 7: Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and 
7.1 Introduction
7.2 Genotype-to-Phenotype (G-to-P) Modelling
7.3 Whole Plant Ecophysiology and Modelling
7.4 Enhancing Breeding Efficiency
7.4.1 Phenotyping for Drought Adaptation: Water Capture by Root Systems
7.4.2 Phenotypic Prediction: Evaluating G × M × E Effects on Drought Adaptation
7.5 Implications
References
Chapter 8: Crop Modeling Approaches for Predicting Phenotype of Grain Legumes with Linkage to Ge
8.1 Introduction
8.2 Are the Crop Models Ready?
8.3 Is Genetics Advanced Enough? Relationship of Phenotype to Genotype
8.3.1 From Genes to DNA
8.3.2 Genes and QTLs
8.3.3 Analyzing G × E with Statistical Tools
8.4 Present Crop Models Are Responsive to Weather, Soils, and Management Effects
8.5 Genes, Processes, and Emergent Outcomes in Crop Models
8.6 Early Efforts to Link Genetics to Crop Models
8.7 Connecting Genes in Process-Oriented Crop Models
8.8 Genotype Specific Parameters in the CROPGRO Model
8.9 Modeling Genotype by Environment by Management (G × E × M) Effects
8.10 Using Crop Models to Evaluate Benefits of Single Genes/Traits and in Combination
8.11 Phenotyping: Needed to Make the Connection to Genes
8.11.1 What Is a Phenotype?
8.11.2 Example of Phenotyping of Dry Bean Recombinants in a Field-Oriented Project
8.11.3 Inverse Modeling Tools for Optimizing Genetic Parameters from Phenotyping Data
8.12 Linking Genes to GSPs in CROPGRO Legume Models
8.12.1 Example of Gene-Linked Model for CROPGRO-Soybean
8.12.2 Example of Gene-Linked Model for CROPGRO-­Common Bean
8.13 Conclusions
References
Chapter 9: Modelling QTL-Trait-Crop Relationships: Past Experiences and Future Prospects
9.1 Introduction
9.2 Complementarity of Crop Modelling and Genetic Mapping
9.3 Roles of Modelling in Assisting Genetic Analysis and Breeding
9.3.1 Models Can Support Phenotyping for the QTL Mapping
9.3.2 Models Can Dissect Complex Traits into Physiological Components
9.3.3 Models Can Integrate and Project Single Organ Level Genetic Variation to Crop Level
9.3.4 Ecophysiological Model May Help to Resolve Genetic Complexities
9.3.5 QTL-Based Modelling Can Quantify Constraints in Breeding
9.3.6 QTL-Based Modelling Can Assist Marker-Assisted Selection
9.4 Past Experiences in Integrating Ecophysiological Modelling and Genetic Mapping
9.4.1 Models Generally Perform Better in Simulating Phenotypic Differences Caused by Environmental
9.4.2 Some Model-Input Parameters Do Not Contribute to the Model in Explaining Differences among
9.4.3 Some Model-Input Parameters Are Hard to Measure for the Whole Population, and Heritability
9.4.4 The Percentage of Phenotypic Variation of a Complex Trait Accounted for by the QTL-Based
9.4.5 Despite the Outnumbering of QTL for Model-Input Parameters Relative to Those for the Com
9.4.6 Number of QTL Identified for Model-Input Parameters Based on a Bi-parental Population Is L
9.4.7 Medium-Size Population Is the Best Option That Combines Feasibility and Robustness in Integ
9.5 Future Prospects
9.5.1 Understanding Physiological Basis of QTL and Genetic Variation
9.5.2 Broadening Genetic Background of the Mapping Population
9.5.2.1 Use of Multiple Mapping Population
9.5.2.2 Genome-Wide Association Study
9.6 Concluding Remarks
References
Chapter 10: Crop Systems Biology: Where Are We and Where to Go?
10.1 Why Crop Systems Biology?
10.2 Roadmap to Crop Systems Biology; Where Are We?
10.3 Roadmap to Crop Systems Biology; Where to Go?
10.4 Crop Systems Biology Needs Cross-Discipline Efforts in Concert

People also search for Crop Systems Biology: Narrowing the gaps between crop modelling and genetics 1st:

crop system definition
    
what is crop in biology
    
cropping system example
    
define crop system
    
crop biology definition

Tags: Systems Biology, Narrowing, modelling, genetics, Xinyou Yin, Paul Struik