Courses

During the course, the emphasis will be on composing and analysing exact models based on specific hypotheses. The results of the analyses offer an understanding of the original biological system. The models studied address fundamental questions from a variety of biological fields, including:

* Multi-level evolution:
- pre-biotic evolution
- eco-evolutionary dynamics and spatial pattern formation
- genome evolution (e.g. interaction between gene regulation and evolution)* Developmental dynamics:
- pattern formations
- morphogenesis and mechanical interactions between cells
- evolution and morphogenesis* Immune system dynamics:
- self/non-self discrimination
- host-pathogen co-evolution* Behaviour:
- self-structuring through local interactions
- interface between learning and evolution
A number of different model formalisms are used, namely:
* (Non-linear) differential/difference equations (ODE and PDE)
* Cellular automata machines
* Individually oriented models
* Evolutionary computation
After completion the course, the student:

1. knows how computational models of dynamical systems can be used to investigate biological processes. (e.g. topics mentioned in 3). In particular;
   • the need of computational models
   • how to formulate computational models
   • how to analyze computational models
   • how to interpret results of computational models
2. knows implicit assumptions of various model formalisms. In particular:
   • ODE and PDE.
   • FSM and CA
   • event based models (e.g. Gillespie)
   • individual (particle) based models
   • evolutionary models
3. knows basic theory derived from computational modeling of
   • network dynamics (e.g. cell cycle, cell differentiation). In particular:
   • spatial pattern formation (e.g. spiral and chaotic waves)
   • multilevel evolution (genome evolution, eco-evolutionary dynamics)
   • multilevel morphogenesis (from genes, to cells to tissues to organism)
4. able to understand current literature using modeling. In particular
   • extracting the bottom line
   • evaluating the explicit and implicit assumptions of the models
   • relating the discussion to the theoretical knowledge gained in 3.

Currently, molecular biology is generating information on the molecular properties of cells and organisms at an incredible pace. For example, we know the complete genome sequence of an enormous and rapidly increasing number of species. Not only do these high-throughput experiments generate a complete view of the genetic information of cells, other techniques measure the level of expression of all genes at the same time or measure all the interactions between all the proteins present in a cell. Bioinformatics is obviously needed for the storage and primary analysis of these huge volumes of biomolecular data. More interestingly, the data uniquely allows bioinformatics to make biological discoveries that were not possible until now. This course introduces the concepts and approaches required to make evolutionary biological discoveries in this genome-scale data and presents examples of interesting pieces of biology that have been discovered using bioinformatics. Topics to be discussed include genome evolution (as opposed to single genes), and the origins of the eukaryotic cell.

The following subjects are discussed in the course:
1. Sequence homology, Protein domains
2. Gene trees and orthology
3. Genome evolution: evolution of the presence of genes, evolution of gene order
4. Formalizations of gene function (e.g. Gene Ontology)
5. Introduction to high-throughput (HTP) techniques such as micro-array, ChIP-on-chip and yeast-2-hybrid
6. Use of these HTP data to study evolution of function
7. Origin of Eukaryotes, endo-symbiosis, explosion of gene duplicates
8. Genome Evolution: Genome duplications

Mass spectrometry is a method of choice in both Proteomics and Structural Biology. A few examples where mass spectrometry is used to advantage: identification of unknown proteins, monitoring and quantification of differences in protein expression between normal and deviant cells, analysis of protein post-translational modifications, receptor-ligand binding, protein folding and noncovalent interactions. In this course the emphasis is on critically deconstructing an article to its main message and to discover its merits and weaknesses. Small groups of students get a publication or publications. This may be a review article on a specific topic, a publication on novel applications of mass spectrometry or on a new method. The group discusses a publication with one of the lecturers. Full course week is required; obligatory attendance on Monday and Friday.            

Many researchers will need to apply statistical analysis in their work. Often, the R statistical language is chosen, since it is well established, free, and has many packages available for different tasks. If you want to be able to use the more powerful features of R, create visually attractive figures with ggplot, write concise and organized code that you can share with others, create automatically generated reports. This course gives you the knowledge to follow one of the subsequent courses of statistical analysis for omics technologies, and linear models with R.

Literature/study material used:
Provided during the course. Students are required to bring a laptop to work online via a web page.

Period: 15 June 2020 - 19 June 2020

Lecturers:
Joep de Ligt, Biomedical genetics/Genetics, 100%
Jeroen de Ridder, Biomedical genetics/Genetics, 10%
Edwin Cuppen, Biomedical genetics/Genetics, 10%
Berend Snel, Theoretical Biology and Bioinformatics, 10%

Invited speakers (differs between years), scheduled speakers listed below:
(Cuppen) Francis Blokzijl, UMC Utrecht, 10% (DNA)
(Veldink) Wouter van Rheenen, UMCU / Rudolf Magnus, 10% (RNA)
(Heck) Maarten Altelaar, Utrecht University, 10% (Protein)
(Verhoeven-Duif) Judith Jans, UMC Utrecht, 10% (Metabolic)
　
Course description:
The correct analysis and integration of omics data has become a major component of biomedical research. The advances in technology have allowed for more sophisticated and unbiased approaches to assess the different omics data types. Large collaborative projects combined with databasing efforts have led to invaluable resources like ENCODE [https://www.encodeproject.org/], Expression Atlas [https://www.ebi.ac.uk/gxa/home], the Human Protein Atlas [http://www.proteinatlas.org/] and KEGG [http://www.genome.jp/kegg/]. These resources can provide valuable insights into your omics data and serve as a validation or quality control set when used appropriately. The challenge is to effectively analyze omics data and these large online resources after performing an experiment or getting clinical results.
For example, when analyzing tumors derived from a set of patients, the question is: how to correctly analyze your OMICs data and leverage public data by comparing these against your own data. The Cancer Genome Atlas alone numbers over 50,000 files from 3 different OMICs types. What are the correct and feasible strategies to utilize these data?
In this course a scientist (active within the respective OMICs field) starts the morning with a lecture, the accompanying scientific article will be available for prior reading. The presenter will introduce a recent study performed within their group and outline the data mining and data integration opportunities and issues they encountered. The lecture is followed by a discussion on how to conduct this research and possible approaches to expand on the current work or solve one of the encountered issues. Topics covered will include mutation analysis, expression profiling, protein abundance and metabolic pathways. In the afternoon students will be tasked with finding a solution to a challenge set by the presenter. Solving such problems can only be done through writing (small) computer programs and integrating relevant data sources.
This course is suitable for students who take an interest in informatics and biomedical application of informatics. The course builds on the skills acquired in introduction programming courses; having completed one of these is a hard prerequisite. Following the "Introduction to Bioinformatics for Molecular Biologists" course is highly recommended.
The goal of this course is to outline current omics analyses methods and the challenges and value of integrating public data in life science research. We will discuss state-of-the-art approaches for tackling these challenges. Students from other disciplines and other universities are invited to attend this course. The topic is suitable for all students in the life sciences dealing with OMICs data.

Literature/study material used:
Lectures, Scientific articles, Course laptop (students can bring their own), Online resources and documentation, Online tutorials, Unix operating system, Online discussion and Q&A platform.

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Studyguide.
Maximum capacity is 20 participants.

Mandatory for students in own Master’s programme:
No.

Optional for students in other GSLS Master’s programme:
Yes, especially CSDB and MCLS students.

Prerequisite knowledge:
Introduction to Python/R/ other programming language.

The course Introduction to Research Data Management gives practical insights on Data Management for scientists. Basic knowledge of relational databases, entity-relationships models, relational models and SQL with MySQL is provided during the course. The programming language used to process data from and to the database is Python.

Proper management of research data is a requirement by funding agencies, publishers or academic institutions. This course provides the technical keys to understand how to model, structure and query data. Benefits of having these skills are numerous: a better insight on how to manage research data and comply with research data management policies, more efficiently store and reuse important data for computational experiments and awareness of the current techniques available to make these tasks easier. The modeling part of the course is focused on communicating the important aspects of datasets to colleagues or an audience via simple models that can be included in posters or other types of publications.

The course is divided into six modules:

1. Research Data Management and Databases
2. Data and Models
3. Starting with MySQL and Workbench
4. Structuring and Querying Data
5. Storing and Processing Data with Python
6. Working with data repositories

Next, more practical insights are given, mainly about:

• Data modeling with E-R and relational schemas
• SQL (mainly DML)
• Working with MySQL and Workbench (modeling)
• Working with publicly available data by modeling, importing and integrating data into relational databases.
• Working with data schemas and public repositories

The final grade consists of:

• Online quizzes (10%), three attempts per quiz. Min. score is 6 per quiz.
• Two minor assignments (20%), No minimum score. There is one opportunity to resubmit one of the two minor assignments to improve its grade.
• A final assignment (70%), Min. score is 5. There is one opportunity to resubmit the final assignment is the grade is less than 6.

Min. final grade to pass the course: 5.5

Literature/study material used:
Course content and material is hosted on https://elearning.ubc.uu.nl

A virtual machine (Ubuntu Linux) containing all the necessary software is available for students.
Alternatively, students may choose to install the required software on their own machine. In that case, they will need a computer environment with:

• Minimum: Python 2.7.9 or Python 3.4.x/3.5.x
• Jupyter (IPython) notebook
• MySQL 5.7.X branch
• MySQL Workbench CE 6.3.X
• Python pandas (http://pandas.pydata.org/)
• Windows users can install WinPython (http://winpython.github.io/) containing all the necessary modules by default

Period (from-till): 22 June - 3 July 2020

Lecturer(s):
Name, faculty/department, participation (%) in course
Dr. Jeroen de Ridder, UMC University, 100%

Extended course description (for Osiris):
Bioinformatics is at the heart of many modern genomics research, and encompasses the application of statistics and computer science to (large-scale) biomolecular datasets. In essence, bioinformatics is about smart ways of extracting knowledge from the enormous amounts of data that can be generated using modern measurement techniques. For instance, it plays an important role in finding the genetic origins of various diseases, such as cancer, diabetes or alzheimer.

In this course we will study some key examples of bioinformatics analyses, i.e. data analytics and computational algorithms, by reading a set of selected papers that present some significant biological conclusions. Instead of the teachers giving lectures about the methodologies, the students are stimulated to read, study and comprehend the available course material. Some lectures will be provided to ensure the basic concepts are clear.

Schedule: The course runs for five days from 9.00 till approximately 17.00. Each day will include two rounds of paper discussions and two lectures that goes into depth with regards to the computational approaches taken. The second week of the course is for proposal writing and peer review of the proposals.

Content:

• Unsupervised learning, Hierarchical and k-means clustering, spectral clustering
• Supervised learning, cross-validation, overtraining, Bayes classifier, Random Forest classifier
• Dimension reduction, PCA, NMF, tSNE
• Hidden Markov Models, Forward Backward algorithm, Viterbi

Literature/study material used:
Provided course materials (slides) will be made available through our online learning platform: elearning.ubc.uu.nl

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Studyguide.
Bioinformatics Profile students will have priority when this course is followed as a part of their profile.
Thereafter, registration is on 'first-come-first-serve' basis until the maximum number of 20 participants is reached.

Mandatory for students in own Master’s programme:
No

Optional for students in other GSLS Master’s programme:
Yes, especially CSDB and MCLS students.

Prerequisite knowledge:
Basic knowledge of Linear Algebra and Statistics.

Microbes are crucial for life on earth and are highly relevant for human life as beneficials, pathogens, food producers, nutrient cycling, agriculture and many other aspects of our daily lives. The genomes of microbes provide a wealth of information on the processes and mechanisms that these organisms use in their environment. For instance on mechanisms that pathogens use to overcome host immunity or antibiotics, or enzymes that fungi use to produce interesting metabolites that can be used in medicine or agriculture.In this course you will learn how to analyse genome data of individual microbes, but also of microbiual communities (metagenomics). The first week will be focused on basic bioinformatic skills (linux, R, bash, and command line tools) and the analysis of bacterial genomes. In the second week the analyses will be on Eukaryotic microbes with a focus on fungal genomes, comparative approaches and expression analysis.The course will have theroretical lectures, but will mainly consist of hands-on bioinformatic practicals. Knowledge on programming is therefore a prerequisite.

Literature/study material used:

Online material and papers.
Mandatory for students in Master’s programme: NO.
Optional for students in other Master’s programmes GS-LS: Yes, all GSLS