Experimental Design and Data Analysis of Breeding Trials

In this course, students are taught principles of experimental design of trials and statistical analysis of trial data with a special emphasis to linear and generalized linear methods, mixed models, analysis of multi-environment trials using different statistical methods

Program/Minor

Master Plant Breeding

Learning Outcomes

Design Principles: Understand and apply core experimental design concepts—randomization, replication, blocking, controls, and statistical power—in breeding trials; Statistical Models: Link design principles to linear regression and ANOVA, including model assumptions and diagnostics; Experimental Designs: Explain and compare CRD, RCB, incomplete block, row-column, and split-plot designs; Model Selection: Know when to use GLMs or LMMs over traditional models; understand fixed vs. random effects; GLM Applications: Apply logistic, threshold, and loglinear models; specify link and distribution functions appropriately; LMM Applications: Use LMMs to estimate variance components, analyze multi-environment and split-plot trials, and estimate heritability; Genotype-by-Environment Interaction: Analyze GxE using ANOVA, mixed models, Finlay-Wilkinson, AMMI, and GGE biplots; interpret stability and adaptation.

Prerequisites

MAT25303 Advanced Statistics-DL PBR60806 Plant Breeding Skills Cluster (DL) - The R module of this course: knowledge of and experience with R and R Studio is needed. The student is expected to be able to modify existing R scripts and to write short scripts to perform analyses.

Details

Institution: WUR

Period: Period 4

Level: Advanced

EC: 3 ec

Exploratory Data Analysis in R

lant scientists use a wide range of experimental tools. Be it a simple weight measurement or an advanced imaging analysis, every experiment generates data that must be analyzed. Modern approaches for reproducible data analysis are mostly based on writing scripts in (statistical) programming languages. As such, working in the plant sciences and broader life sciences domain requires up-to-date data handling and programming skills.
In this course, students acquire practical skills for exploratory data analysis. Inspired by and working on real-life cases and datasets, students will develop a problem-solving mindset: from data acquisition to data cleaning, from statistical tests to reproducible and responsible reporting. All data analysis tasks will be performed in the R programming language, allowing the students to develop basic programming skills.

Program/Minor

BSc Plant Sciences

Learning Outcomes

Describe basic exploratory data analysis techniques; Write working R code to perform exploratory data analysis: reading, formatting, cleaning, summarizing, elementary statistics and visualizations

Prerequisites

MAT15403 Statistics 2

Details

Institution: WUR

Period: Period 4

Level: Intermediate

EC: 6 ec

Fairness, Accountability, Confidentiality and Transparency in AI

Artificial Intelligence and data science are increasingly being used to power decision making in settings that are highly consequential for people and society. Algorithmic decision making promises great benefits across sectors and domains. At the same time, there are many high-profile stories about the potential harms of algorithms, for individuals, population groups, and society at large. With great power comes great responsibility. What does “responsibility” mean from a technical perspective? What are technical solutions available to those who develop and deploy AI, machine learning and data science to help mitigate the potential risks of algorithms?

The concepts we develop in Fairness, Accountability, Confidentiality, Transparency in Artificial Intelligence (FACT-AI) are aimed at students who have joined or are likely to join the developer community. They should help them to articulate what concepts like “responsibility” mean from a technical perspective, and to make informed decisions when assessing and addressing the potential risks of algorithms.

This course will provide an overview of recent algorithmic approaches to improve fairness, transparency and privacy in AI. We will also discuss how such methods can be combined to improve accountability in algorithmic systems. We will discuss the technical challenges of implementing fairness, transparency and privacy techniques, as well as the challenges of anticipating how effective, in the long-run, such interventions are. We focus on both supervised learning and more recent systems such as LLMs and Agentic systems. Students will also discuss the environmental impacts of AI models and the societal challenges associated with recent generative AI methods.

This course will have a strong practical component: students will be exposed to state-of-the-art approaches and replicate/reproduce/extend a recently published paper in the area.

Program/Minor

Artificial Intelligence (M)

Learning Outcomes

The student is able to explain, motivate and distinguish the main types of algorithmic harm, in general and in terms of concrete AI applications. The student is able to explain the major notions of fairness, accountability, confidentiality, and transparency that have been proposed in the literature, with their strengths and weaknesses. The student is able to describe with state-of-the-art algorithmic approaches to fairness, confidentiality and transparency. The student is able to replicate, evaluate and extend a recently proposed algorithmic approach to improve fairness, transparency or confidentiality in AI. The student is able to evaluate and discuss the environmental impacts of current AI models.

Prerequisites

It is essential that students have taken a Machine Learning course, a Deep Learning course as well as Computer Vision and Natural Language Processing courses. A decent level of proficiency in Python is also essential.

Details

Institution: UvA

Period: Period 3

Level: Master

EC: 6 ec

Food Forward: Innovations that will Feed the World

One of the major current challenges of science and society is to find sustainable solutions that meet the dietary needs of the rapidly growing human population and its increasing standard of living. The United Nations has predicted that the world food demand will rise by 70% by 2050. Current food systems are not sufficient to keep up with this growth, and do not provide appropriate solutions for the diet-related WHO-defined priority diseases that are associated with our ever-increasing standard of living. To provide sustainable and healthy food for the next generations, there is an urgent need to further develop food production. In this course you will learn and discuss how science can feed the world in the coming decades, and become aware that a combined effort of multiple disciplines is required to understand the challenge of future food and think of innovative ways to deal with this challenge. The combination of fundamental sciences and integrated studies on molecular and immunological mechanisms will provide students with a broad perspective on the societal and scientific challenges that await to meet the dietary demands of the future. Students are encouraged to think about how their work contributes to meeting the challenges of future food and will learn to communicate this to a wide audience (both academic and non-academic). Specifically, course content deals with four broad themes, namely: 1) improving crop stress tolerance, 2) alternatives to plant-based food 3) technical innovations in agriculture and 4) food policy and safety. Lectures and assignments will cover these broad themes. For instance, emerging threats to animal, plant and ecosystem health will be covered, but also legislation around food security in The Netherlands, socio-economic aspects of food securityand innovations such as cultured meat (kweekvlees).

Program/Minor

Biologie (BIOL) (B)

Learning Outcomes

This course will provide students with input for their CV on 21st century skills, including interpretation of scientific literature, science communication, English writing and presenting skills, ability to communicate to a broad audience, teamwork and awareness of the relevance of science for addressing the food problem.

Prerequisites

This course requires basic knowledge of molecular and cellular Plant biology. The following courses are recommended: Planten en Micro-organismen (niveau 1), Moleculaire Biologie (niveau 1), Plantenfysiologie en -ontwikkeling (niveau 2). This course can only be followed in the second or third year of the Bachelor.

Details

Institution: UU

Period: Period 4

Level: Advanced

EC: 7.5 ec

Fundamentals of Data Science

Data science is a dynamic and fast-growing interdisciplinary research field that, across science, industry, and government, is altering how people understand the world and make decisions. Not surprisingly, the demand for data science skills is on the rise. This course will cover key principles and tools of data science. In particular, the course will cover the process of acquiring and transforming data; the application of algorithms to learn from data (e.g., classification, regression, clustering); the application of techniques to make decisions based on data; and the role of generative models in data science. This course will cover network data analysis, as well as the social and ethical implications of data science, with a particular emphasis on algorithmic fairness, privacy and explainability. The course will expose students to theory (i.e., machine learning and statistical methods underlying data science) and practice (i.e., use of data science libraries and analysis of real-world datasets).

During the course, students will work on a series of individual exercises and group assignments that will bind together all elements of the data science process. Python will be used for all programming assignments and project. The course will introduce and make use of Jupyter notebooks, Numpy, Matplotlib and Pandas. Auxiliary libraries such as NetworkX, GeoPandas and Seaborn will also be covered.

Program/Minor

Information Studies (M)

Learning Outcomes

Explain the main stages and common challenges in data science projects; Apply (Python) libraries to clean, transform and analyse real-world datasets in an efficient way; Recognise good data visualisation practices and develop effective plots and figures; Describe the mathematical and computational techniques underlying basic supervised and unsupervised learning algorithms, as well as network data analysis and generative modelling; Implement statistical and machine learning methods to recognize patterns in data and judge the settings where each method should be applied; Design a data science project to perform exploratory data analysis and/or answer a research question; Be familiar with the ethical concerns associated with data science and reason about algorithmic fairness, privacy and explainability; Describe how to use data science to perform consequential decisions and understand the challenges of optimal decision-making in dynamic environments; Apply recent generative AI tools to aid data visualization, augmentation and preparation

Prerequisites

Programming experience (Python recommended). General knowledge of probability theory, statistics, and linear algebra

Details

Institution: UvA

Period: Period 1

Level: Master

EC: 6 ec

Fundamentals of Genetics

the Genetic and Molecular Biological Approach to Biology;
single-gene inheritance;
independent assortment of genes;
mapping eukaryote chromosomes by recombination;
gene interaction;
transcription, processing and translation of RNA in Eukaryotes;
regulation of eukaryotic gene expression;
genomes and genomics;
large-scale chromosomal changes;
gene isolation and manipulation;
population genetics;
inheritance of complex traits.

Program/Minor

Bachelor Biology; Bachelor Plant Sciences

Learning Outcomes

Explain the basic genetic and molecular biological processes in eukaryotes: transmission of genetic information, the relationship between genotype and phenotype, the regulation and expression of genes, the generation of variation, the organization of genomes and genes; Summarize the basic genetic methods: how genomes are explored, how populations are studied, how quantitative characters are studied, how genetics is applied in life sciences; Recall tools and methods to study gene expression and to isolate, analyse and manipulate genes; Recall and explain general genetic and molecular biological terms as these are described in the appropriate chapters of the textbook (in other words, to be able to understand and to speak the genetic and molecular biological language at a basic level) Explain and interpret genetic experiments and observations, e.g. to recognize and analyse patterns of inheritance, gene interactions, consequences of chromosomal changes, genetic changes in populations, and quantitative characters; Analyse and judge the use of genetics in societal questions

Prerequisites

Assumed: CBI10306 Cell Biology or CBI10806 Introduction to Cell Biology

Details

Institution: WUR

Period: Period 3

Level: Introductory

EC: 6 ec

Future-Proof Crop Innovation

Life-long learning: 15 modules on topics like Crispr CAS, biotechnology, breeding/biomarkers. Some short, some summer schools

Details

Institution: WUR education for professionals

EC: LLO ec

Gene Regulation

Eukaryotic genomes contain thousands of genes and transposons. To function optimally, genes must be expressed at the right time and place, and transposons inactivated. This course explores the complex mechanisms involved in gene regulation, including DNA sequences, proteins, RNAs, genetic and epigenetic processes, and newly discovered mechanisms like phase separation. Techniques used to study gene regulation are also discussed. Mechanisms are presented in an integrated way across species (yeast, plants, animals), training students to address challenges in health and food production. Covered topics include transcription factors, cis-regulatory elements, chromatin remodeling, DNA methylation, histone modifications and variants, non-coding RNAs, genome organization (loops, domains, TADs, phase separation), and modern genomic techniques (ChIP, sequencing methods, 3C/Hi-C). Recent literature is used throughout.

Program/Minor

Exchange Programme Faculty of Science

Learning Outcomes

The student can discuss various gene regulation mechanisms; The student can discuss the effects of genetic and epigenetic modifications on the development of an organism; The student can explain different techniques for RNA, DNA and chromatin analysis; The student can transfer knowledge gained about gene regulation from lectures and literature orally to fellow students; The student can critically analyze scientific publications in the field of gene regulation; The student develops one's own opinion about science and scientific results; The student can discuss current issues of gene regulation; The student can, together with other students, apply the obtained knowledge to design a project;

Prerequisites

For BMW students, the recommended prior knowledge to take this course is the Molecular Biology, Cell Biology and Molecular Techniques courses from the first semester of year 2 BMW. For Biology students, the courses Cellular Responses and Challenges in Molecular Life Sciences are equivalent. For Psychobiology students, it is recommended that they have completed the Molecular Cell Biology component from Experimentation Year 2. For all students, they should be familiar with concepts such as transcription, translation, promoter, RNA polymerase, transcription factors, nucleosome, etc.

Details

Institution: UvA

Period: Period 3

Level: Advanced

EC: 6 ec

Gene Technology

Recombinant DNA technology has brought about a revolution in our understanding of molecular processes in living organisms. To date, there is no field in experimental biology that has remained untouched by the potential of isolating, analyzing and manipulating genes and organisms. Thus, gene technology provides essential tools in both fundamental and applied medical, industrial, agricultural, environmental and biological research.
In this course we will focus on four major topics, including (i) production of transgenic mammals, (ii) altering gene function using knockouts, RNAi and gene editing, (iii) transgenic mice as human model systems and (iv) production of transgenic crops. In addition to conventional lectures that will outline the major procedures, applications and some research papers, students will be challenged by virtual (computer based) experimental designs on the generation and characterization of transgenic animals and plants.
The practical course involves biomolecular techniques to illustrate two experiments.The first one will deal with the identification, characterization and complementation of mutations in organisms. Basic and more advanced gene technology will be applied such as genomic and plasmid DNA isolation, PCR, golden gate cloning, transformation, restriction/digestion and complementation analysis. The second experiment centers on the generation of site specific mutations and gene editing using CRISPR-Cas technology. These experiments will be supported in a virtual environment.

Program/Minor

Minor Biotechnology; BSc Minor Experimental Plant Sciences; Bachelor Biology; Bachelor Biotechnology; Bachelor Molecular Life Sciences

Learning Outcomes

Explain methods currently used to clone, analyze and edit genes and to introduce foreign genes into animals and plants; Design experimental strategies and interpret experimental data involving gene technology (in silico); Apply basic recombinant DNA techniques; Interpret results from experiments involving recombinant DNA techniques; Incorporate results in a written report; Apply basic regulations when working with genetically modified micro-organisms

Prerequisites

Assumed: Practical Biological Chemistry and Cell Physiology and Genetics or Fundamentals of Genetics and Molecular Biology. Mandatory: Laboratory Safety

Details

Institution: WUR

Period: Period 5, Period 6

Level: Intermediate

EC: 6 ec

Genetic Analysis Trends and Concepts

This advanced course explains the most important genetic concepts to unravel and understand complex biological phenomena. We in detail explain the genetic processes underlying variation and its inheritance and their relevance with respect to evolution. We center our course around three main themes: Mutation, recombination and (epi-)genetic transmission. All three themes are both cause and consequence of evolution.
We expect students to gain a thorough understanding of the concept of a separation between soma and germline and its genetic and evolutionary consequences: Cells in the soma differentiate and change phenotypically because of epigenetic but also genetic processes like for example polyploidization, mitotic recombination and cytoplasmic segregation. Similar processes affect the germline through meiotic recombination, mutation, segregation, ploidy changes and epigenetics. Students will practise using various methods to calculate genetic parameters like linkage and mutation rate. We expect students to integrate this knowledge into a larger understanding of how such mechanisms affect selection (that act on the soma as well as on gametes) and trade-offs in reproduction strategies. Mutations that favor the germline might adversely affect the soma and vice versa.
We illustrate these fundamental concepts in a series of both classical and state-of-the-art experiments, using a wide variety of model- (and well-studied) organisms like Arabidopsis, Aspergillus, Drosophila, Human and E. coli. In addition we discuss a number of classical research papers (like Mendels’ original paper) and current research highlights in contemporary reviews (on topics like mutation, recombination and genetic transmission). Ultimately, students should gain an understanding of the complex interactions that shape the genome and the associated phenotypes.

Program/Minor

BSc Minor Omics: Applications, Technologies & Data (2025) BSc Minor Plant Biotechnology (2025) BSc Minor Plant Breeding (2025) Bachelor Plant Sciences; Master Biology; Master Biotechnology; Master Molecular Life Sciences; Master Plant Biotechnology; Master Plant Sciences

Learning Outcomes

Explain the consequences of the soma-germline differentiation; Calculate the mutation rate, explain how it can be induced, and can evolve; Calculate the recombination rate, and explain how it can evolve; Apply formulas to meiotic and mitotic recombination data (including tetrad analysis) to map genes relative to each other and centromeres; Use and explain genetic- and epigenetic concepts like: epistasis, QTL mapping, complementation, polyteny, X-chromosome inactivation, (endo-)polyploidy, aneuploidy; Using data of offspring phenotypes, infer the allelic and non-allelic interactions of responsible genes; Use and explain evolutionary-genetics concepts like: mutation accumulation, antagonistic pleiotropy, experimental evolution, genetic parasites; Design and perform basic genetic experiments to test predefined hypotheses, using appropriate methods and genetic model systems

Prerequisites

Assumed: GEN11806 Fundamentals of Genetics and Molecular Biology, GEN21306 Introduction to Genetic Analysis or BIC20306 Cell physiology and Genetics, or an equivalent basic genetics course. Mandatory: ZSS06100 Laboratory Safety

Details

Institution: WUR

Period: Period 1, Period 6

Level: Advanced

EC: 6 ec

Genome Bioinformatics

Complex biological systems arise from interactions between molecules, cells, organisms, and their environment. The genome serves as the blueprint for these interactions and is shaped by 4 billion years of evolution. With advances in sequencing technologies, researchers can now collect large-scale genomic datasets from species, populations, and environments. In this course, students explore how such data is analyzed to generate new insights into genome evolution, organization, and function.

The course is structured in three parts. In the first five weeks, students learn about genome structure and the evolutionary processes that leave detectable patterns in genome sequences. They are introduced to the bioinformatic methods that identify these patterns in both prokaryotic and eukaryotic genomes. In the last four weeks, students conduct a small-group research project, applying theoretical and practical skills to analyze large-scale data, evaluate results, and formulate biological hypotheses. They also write and peer-review a research proposal and present findings in a mini-symposium. Work formats include lectures, flipped-classroom discussions, computer practicals, literature reviews, group projects, and written/oral presentations.

Program/Minor

Biology (BIOL) Mathematics and Applications (WSKT) Molecular and Biophysical Life Sciences (MBLS)

Learning Outcomes

Know about major processes in genome evolution and their detectable traces in biological data; Apply and interpret results from state-of-the-art bioinformatics methods on genomic datasets; Formulate hypotheses based on evolutionary patterns and critically assess results; Conduct a literature review, formulate research questions, and design scientific experiments; Execute bioinformatic experiments and present findings both orally and in writing; Understand the scope of bioinformatics research, particularly that conducted at UU; Collaborate effectively in teams on complex scientific tasks; Solve problems using programming, scripting, and critical reasoning; Use technical and analytical skills to process and interpret large biological datasets; Communicate scientific findings clearly through writing and presentations.

Prerequisites

Completion of the level 1 course 'Genomica' (or 'Systemsbiology'); Participation in 'Data Science and Biology' or comparable Unix command line experience; For MBLS students: 'Functional Biology' (MBLS-107) and 'Bioinformatics & Dynamical Modelling' (MBLS-207)

Details

Institution: UU

Period: Period 4

Level: Advanced

EC: 7.5 ec

Genomics and Bioinformatics

Genomics is a new area of research that relates to holistic study of the genome a the gene, transcript, protein and metabolite levels. Due to the large amounts of data involved in this study computer tools are extensively used. During the course we will present basic concepts of genomics and introduce students to the problems of dealing with and understanding these large datasets. We will discuss the basic architecture of the genome and the implications for genome evolution. We will also deal with the structural and functional aspects of genomics from gene through to metabolites. The course will help the students to analyse and understand the links and relationships between genes and their various products. The course will comprise information packages in the form of knowledge clips, recorded tutorial and these will be combined with a large number of exercises where the knowledge provided will be applied to practical bioinformatics problems.

Program/Minor

Master Plant Breeding

Learning Outcomes

Interpret the organization of plant genomes and how they evolved; Explain how different functional genomics levels interacts and depend on each other's; Evaluate the results of genome assembly and annotation; Analyze simple genomics data to retrieve biological information; Execute and present a literature study on a crop genome of choice; Integrate information from genes to pathways to networks at an organism level; Argument the role of genomics for crop improvement

Prerequisites

Basic knowledge of Molecular Biology and Genetics.

Details

Institution: WUR

Period: Period 3

Level: Advanced

EC: 3 ec