Plant System Biology
Code: 43867 ECTS Credits: 6| Degree | Type | Year | Semester |
|---|---|---|---|
| 4316231 Plant Biology, Genomics and Biotechnology | OT | 0 | 2 |
Contact
- Name:
- Ana Martin Hernandez
- Email:
- anamontserrat.martin@uab.cat
Use of Languages
- Principal working language:
- english (eng)
Teachers
- Ana Martin Hernandez
External teachers
- Elena Monte
- Iban Eduardo
- Jae-Seong Yang
- Juan José López Moya
- Maria Lois
- Martí Bernardo
Prerequisites
Basic knowledge of Genetics
Objectives and Contextualisation
To provide students with a comprehensive and current view of the techniques, fundamentals and applications of Plant Genomics and introduce systems biology of plants. The specific objectives include understanding the following aspects: the diversity and complexity of plant genomes, the techniques commonly used in genomics, transcriptomics, proteomics and metabolomics studies and applications to the genetic improvement of crop plants. Use of mathematics for predictive modeling through integration of different omics data.
Competences
- Apply knowledge of plant molecular genetics in different scientific and industrial areas.
- Communicate and justify conclusions clearly and unambiguously to both specialised and non-specialised audiences.
- Develop critical reasoning in the area of study and in relation to the scientific and business environment.
- Identify and use Bio-Computer Science tools to be applied to the genetic, evolutionary and functional study of plants.
- Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- Synthesize, and analyze alternatives and debate critically.
- Use and manage bibliographical information and computer resources in the area of study.
- Use scientific terminology to argue the results of research and present them in English both orally and in writing in an international environment.
Learning Outcomes
- Apply mathematical methods of analysis and predictive modelling through the integration of different types of omic experimental data.
- Communicate and justify conclusions clearly and unambiguously to both specialised and non-specialised audiences.
- Develop critical reasoning in the area of study and in relation to the scientific and business environment.
- Distinguish and apply the methods and techniques used normally in studies of genomics, phenomics, transcriptomics, proteomics and metabolomics.
- Integrate knowledge and use it to make judgements in complex situations, with incomplete information, while keeping in mind social and ethical responsibilities.
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- Synthesize, and analyze alternatives and debate critically.
- Use and manage bibliographical information and computer resources in the area of study.
- Use scientific terminology to argue the results of research and present them in English both orally and in writing in an international environment.
Content
Systems Biology: Concepts, methodology, and case studies using multiple omics.
The case study will be the emergence of a new disease affecting and killing all tomato varieties. The students will make a trip through all the -omics to unveil the cause and search for a scientific solution feasible for application in crop plant breeding.
Specifically,
We will use practical applications of methods and techniques in plant phenomics and genomics, including the use of molecular markers in breeding. Importance of QTL in this problem.
Analysis and application of data arising from genomics and transcriptomics studies to narrow down the problem.
Analysis and application of data arising from proteomics, interactomics, and metabolomics studies to find a solution to the problem.
Integrative analysis of the case study applied, including Computational modelling, to crop plant breeding.
Methodology
Lectures and Expert talks
Problems and case studies
Preparation of reports
Personal study
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Activities
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures and Expert Talks | 11 | 0.44 | 1, 5 |
| Problems and case studies | 24 | 0.96 | 3, 4, 6, 9 |
| Type: Supervised | |||
| Preparation of reports | 30 | 1.2 | 2, 7, 9 |
| Type: Autonomous | |||
| Personal study | 84 | 3.36 | 8 |
Assessment
Continuous evaluation 10%
Report 60%
Final Quiz 30%
Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Continuous evaluation of students' participation | 10% | 0 | 0 | 1, 3, 4, 5, 6 |
| Final quiz | 30% | 1 | 0.04 | 6 |
| Reports | 60% | 0 | 0 | 2, 7, 8, 9 |
Bibliography
Yunbi Xu Molecular Plant Breeding. CAB International Oxfordshire, UK disponible online a Biblioteca UAB :http://www.cabi.org/cabebooks/FullTextPDF/2010/20103101750.pdf
Fredericks DN, & Relman DA (1996). Sequence-based identification of microbial pathogens: a reconsideration of Koch’s postulates. Clinical microbiology reviews, 9 (1), 18-33
Li et al. 2014 A Review of Imaging Techniques for Plant Phenotyping. Sensors, 14, 20078-20111.
Großkinsky et al, 2015. Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. Journal of Experimental Botany, 66: 5429-5440.
Collard et al. 2005. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica (2005) 142: 169–196
Tanksley and McCouch 1997. Seed Banks and Molecular Maps: Unlocking Genetic Potential from the Wild. Science 277: 1063-1066.
Serra et al. 2016. Marker-assisted introgression (MAI) of almond genes into the peach background: a fast method to mine and integrate novel variation from exotic sources in long intergeneration species. Tree Genetics & Genomes 12: 96.
Fei Chen, Yunfeng Song, Xiaojiang Li, Junhao Chen, Lan Mo, Xingtan Zhang, Zhenguo Lin and Liangsheng Zhang (2019). Genome sequences of horticultural plants: past,
present, and future. Horticulture Research 6:112.
Anne Pfeiffer, Hui Shi, James M. Tepperman, Yu Zhang, and Peter H. Quail. (2014)Combinatorial Complexity in a Transcriptionally Centered Signaling Hub in Arabidopsis. Molecular Plant 7, 1598–1618.
Lee, C.-R., Park, Y.-H., Min, H., Kim, Y.-R., and Seok, Y.-J. (2019). Determination of protein phosphorylation by polyacrylamide gel electrophoresis. J. Microbiol. 57, 93–100. doi:10.1007/s12275-019-9021-y.
Schopper, S., Kahraman, A., Leuenberger, P., Feng, Y.,Piazza, I., Müller, O., et al. (2017). Measuring protein structural changes on a proteome-wide scale using limited proteolysis-coupled mass spectrometry. Nat. Protoc. 12, 2391. doi:10.1038/nprot.2017.100 https://www.nature.com/articles/nprot.2017.100#supplementary-information.
Mateus, A., Määttä, T. A., and Savitski, M. M. (2016). Thermal proteome profiling: unbiased assessment of protein state through heat-induced stability changes. Proteome Sci. 15, 13. doi:10.1186/s12953-017-0122-4.
For a tutorial on Computational Biology see: https://www.bioconductor.org/packages/release/bioc/vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf and for an applied study with real data to reveal novel molecular mechanisms see here http://msb.embopress.org/content/10/12/767. For the publication first presenting CellNOptR see http://msb.embopress.org/content/5/1/331
Jun Hong, Litao Yang, Dabing Zhang and Jianxin Shi. (2016). Plant Metabolomics: An Indispensable System Biology Tool for Plant Science.Int. J. Mol. Sci., 17, 767.
Saleh Alseekh and Alisdair R. Fernie. (2018). Metabolomics 20 years on: what have we learned and what hurdles remain? The Plant Journal 94, 933–942.
Perez de Souza, L., Alseekh, S., Naake, T., & Fernie, A. (2019). Mass spectrometry-based untargeted plant metabolomics. Current Protocols in Plant Biology, 4, e20100.
Software
Section 1 Introductión: 3 hours
Section 2 Phenomics: 3 hours
Section 3 Genetics: 3 hours
Section 4 Genomics 3 hours
Sections 5 Transcriptomics 3 hours
Section 6 Computing Biology. Modeling 3 hours
Section 7 Proteomics 3 hours
Section 8 Computing Biology. Modeling 3 hours
Section 9 Metabolomics 3 hours
Section 10 Computing Biology. Modeling 3 hours
Section 11 Resolution of the problem and questions. 3 hours