Markers in Quantitative Genetics and Plant Breeding (online)

In this course, the students will be made familiar with the use of molecular markers in genetic research and plant breeding, the estimation of genetic distance based on marker genotype frequencies in different types of segregating populations, the construction of linkage maps, concepts and applications of quantitative genetics, the analysis of quantitative trait loci (QTLs) and the discovery and application of markers in research and for selection in breeding programs, both for qualitative and quantitative traits.

Program/Minor

Learning Outcomes

Understand and apply genetic markers, including SNPs, in segregating populations for genetic mapping, QTL analysis, marker-assisted selection, and bulked-segregant or selective genotyping; Construct and interpret genetic and physical maps using segregation data and specialised software, and distinguish between linkage and physical distance; Comprehend inheritance of qualitative and quantitative traits, including monogenic and polygenic traits, and their implications for plant breeding; Understand genetic models involving additivity, dominance, overdominance, and epistasis, and their role in heterosis, inbreeding depression, and hybrid vs. pure line strategies; Calculate and interpret midparent values, additive and dominance effects, dominance ratio, and genetic distances from trait means in different breeding generations; Estimate additive and dominance variance, environmental variance, and narrow- and broad-sense heritability, understanding their dependency on population, environment, and design; Use quantitative genetic models and statistical methods to analyse variance components and compare breeding strategies based on genetic parameters; Calculate and interpret selection differential, response to selection, correlated response, and apply the breeder’s equation for direct and indirect selection; Perform QTL analyses and compare mapping methods such as single marker analysis, interval mapping, composite interval mapping, and contrast these with GWAS; Understand GWAS methodology, including LD, population structure correction, Manhattan and QQ plots, and apply genomic selection methods statistically and practically; communicate results through scientific poster presentations and experiment design.

Prerequisites

Genetics (DL); Principles of Plant Breeding (DL); Advanced Statistics (DL) Plant Breeding Design Cluster (online) The part on R and R Studio, students are expected to be able to write and modify short R scripts to carry out statistical/genetics analyses.

Details

Institution: WUR

Period: Period 6

Level: Advanced

EC: 4 ec

Master Level Computational Biology

During the course, the emphasis will be on composing and analysing exact computational models based on specific biological hypotheses. These models are used to gain insight into the dynamics of biological systems across various fields. Topics include multi-level evolution, such as pre-biotic evolution, eco-evolutionary dynamics, spatial pattern formation, and genome evolution—for instance, the interaction between gene regulation and evolutionary processes. Developmental dynamics are also addressed, focusing on pattern formation, morphogenesis, mechanical interactions between cells, and the evolutionary basis of morphogenetic processes. The course further explores immune system dynamics, including self/non-self discrimination and host-pathogen co-evolution, as well as behavioural dynamics, such as self-structuring through local interactions and the interface between learning and evolution.

To analyse these systems, students will use a range of modelling formalisms. These include (non-linear) differential and difference equations (ODE and PDE), cellular automata, individually oriented (particle-based) models, and evolutionary computation. Through these tools, students will learn how computational models of dynamical systems can be formulated, analysed, and interpreted to investigate biological processes. The course also prepares students to critically read and interpret current scientific literature that uses modelling, helping them to extract key findings, assess underlying assumptions, and connect insights to the theoretical frameworks discussed during the course.

Program/Minor

Learning Outcomes

Understand how computational models can be used to study biological processes; Explain the purpose, formulation, analysis, and interpretation of computational models; Recognize assumptions in model formalisms such as ODE, PDE, FSM, CA, event-based, individual-based, and evolutionary models; Understand theory derived from modelling of network dynamics, spatial patterning, evolution, and morphogenesis; Interpret scientific literature using modelling by extracting conclusions, evaluating assumptions, and linking to course theory.

Prerequisites

Modeling requires some knowledge, contact the coordinator. Course open for Bioinformatics and Biocomplexity students.

Details

Institution: UU

Period: Period 3

Level: Master

EC: 7.5 ec

Microbial Genomics

Microbes (bacteria, archaea and microbial eukaryotes, including fungi) are key members of our biosphere and are highly relevant for human life as mutualists and pathogens and as crucial drivers of 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. Furthermore, microbes are commonly used as model systems to research molecules and molecular processes and to uncover how organisms function, develop, and interact with their environment.

In this course you will learn how to analyse genome data of individual microbes, but also of microbial communities (metagenomics). The first part of the course will be focused on basic bioinformatic skills (linux, bash, and command line tools) and the analysis of bacterial genomes. The second part will be focused on the analyses of eukaryotic microbes with a focus on fungal genomes, such as comparative approaches and gene expression analysis. The course will includetheoretical lectures, but will mainly consist of hands-on bioinformatic practicals. Therefore, interest to work with a computer is required (and desirable!).

Program/Minor

Learning Outcomes

Understand the importance of (meta)genomics in microbial biology; Know and explain differences in prokaryotic and eukaryotic genome structure; Explain the power and usefulness of comparative genome analyses; Navigate a computer file system using the command line; Design automated workflows with a series of sequential commands and examine the results; Identify, visualize, and interpret between and within species diversity; Evaluate the quality of genome sequencing data; Explain the impact of genome sequencing and assembly strategies as well as the genome structure on the quality of prokaryotic and eukaryotic genome sequences; Develop a suitable workflow to analyse (re-)sequencing data and interpret the results; Perform gene predictions from a genome sequence and understand its challenges and limitations; Map transcriptome data, determine gene expression values, and perform differential gene expression analysis; Perform functional annotation of predicted protein sequences and interpret the results; Design and execute comparative genomics analyses and interpret the results; Hypothesize on the causes of phenotypes associated with the different genomic datasets.

Prerequisites

Assumed: Knowledge about genome biologyand genome sequencing

Details

Institution: UU

Period: Period 2

Level: Master

EC: 4.5 ec

Modeling and Data Analysis in Complex Networks

Big Data is mostly obtained from features of components and the interactions between components in large complex systems. Examples are (1) end user features and interactions in both online and real-world social networks like Twitter, LinkedIn (2) data from content sharing platforms such as YouTube (3) physiological data of the brain and (4) stock prices etc. in economic systems. Such a dataset is networked in nature i.e. the data of the system components or interactions are (cor)related to each other.

This course introduces the basic methodologies to analyze, model, interpret and predict such Networked Data that enable us to further intervene or optimise the system, combining advances from network science, modeling of dynamic processes and statistical physics, beyond machine learning algorithms. These methods will be applied to diverse real-world datasets obtained from e.g. Facebook, LinkedIn, YouTube, the brain etc.

Program/Minor

Learning Outcomes

Construct a network based on the dataset, characterize and model the network in order to e.g. detect patterns and anomalies; Model the data via dynamic processes (e.g. viral spreading) on networks thus decode the underlying governing mechanisms of e.g. information/error/behavior contagion; Predict e.g. the popularity of a product, news, disease, computer virus; Control the contagion process such as maximize the information prevalence and market share; Obtain an overview of the Msc/Phd projects on the frontiers of networked data analysis.

Prerequisites

The assignment and final project require basic programming skill.

Details

Institution: TU Delft

Period: Period 1

Level: Master

EC: 5 ec

Modeling Life

Have you ever wondered how cells “know” which genes to express at a given time and place, or how a plant seed “decides” when is the best time to germinate? Or would you like to know what the stripes of a zebra, the fingers on your hand, and the patterning of vegetation in dry ecosystems have in common? The aim of this course is to learn how computational models can be used to help address these questions, and how to use models for questions where experimentation is not possible.

Living systems like those mentioned above are made up of many different interacting elements, from genes, to cells, tissues, organs, organisms, and ecosystems. In this course on Modeling Life you will learn to use computational models to understand how these interactions together produce self-organizing mechanisms, from the gene regulatory networks processing information and making cell fate decisions, to how cell-to-cell communication enables the regular distribution of tissues (i.e., fingers) and organs (e.g. lateral roots in plants), how mechanical processes shape tissues in morphogenesis, and finally, how evolution shapes these and many other processes. Additionally, you will learn how to create models, which model type to pick and how to judge the quality of the model.

This course will consist of lectures (HC) covering various modelling approaches to study life at different levels accompanied by computer practicals (WC) where students will get hands-on experience on running computational models of the development or the evolution of animals, plants, and microbial systems. The students will learn how to formulate a computational model to address specific questions (algorithmic thinking), how to analyze the output of the model, and how to question a model based on the underlying assumptions. The students will learn to use modern tools like AI assisted programming to develop their models.

Program/Minor

Learning Outcomes

The course aims to provide students with an introductory understanding of computational modeling to study living systems, and their large range of applications.

Prerequisites

Basic knowledge on the analysis of differential equations, and some experience with R or python programming.

Details

Institution: UU

Period: Period 2

Level: Master

EC: 7.5 ec

Modelling and Problem Solving

The course covers major (combinatorial) solving approaches, namely constraint programming, integer programming, boolean satisfiability, dynamic programming and decision diagrams, and local search. The course focuses on modelling practical problems, expressing them in the surveyed paradigms; algorithms for solving such problems are covered in the follow-up “Constraint Solving” course. The topic of genetic algorithms is reserved for the course “Evolutionary Algorithms”. Lastly, the course discusses conducting proper empirical evaluations.

Program/Minor

Learning Outcomes

Comprehensively explain main modelling paradigms such as (Max)SAT, CP, (M)(I)LP, DP and LS; Motivate a choice for an appropriate modelling paradigm; Model real-world problems in a suitable modelling paradigm; Conduct proper empirical evaluation of developed models and related algorithms.

Prerequisites

Required: bachelor courses on algorithm design, programming; Recommended: bachelor courses on artificial intelligence, complexity theory, graph theory, (logical) reasoning,

Details

Institution: TU Delft

Period: Period 2

Level: Master

EC: 5 ec

Modelling biological systems

Biological systems are arguably the most complex systems science tries to understand. We make observations, and try to obtain useful insight in the systems. This requires hypotheses; ideas of how we believe the world around us is structured and operates. Models translate our hypotheses into concrete predictions about our objects of study that can be tested by targeted experiments.

The more complex the systems we study, the more crucial the use of models. The SARS-CoV2 outbreak in 2019 is an example of a highly complex biological system. Models of how this virus spreads are crucial because they provide predictions that in turn can be used to take appropriate measures. Without models, we would not be able to focus the measures to combat this virus. There is a key role for one of the strongest tools in our scientific toolkit: mathematics. Mathematics are able to capture complexity in a form that makes it manageable. Mathematical models allow a full exploration of the consequences of our hypotheses and provide quantitative, and therefore precisely testable, predictions. Nowadays, this modelling toolkit is vastly extended by the wide availability of computers, which allow us to implement and test mathematical models, or even directly simulate the behaviour of complex systems.

In this course we attempt to convey the fascination for biological systems and introduce the tools to model them.

Program/Minor

Learning Outcomes

Identify the key components of a relatively complex biological system; Describe the interrelationships between the key components of a complex biological system; Formulate a mathematical description of the system; Analyse the system of governing equations to make predictions about the biological system; Write a simple computer program in Python to simulate the simplified biological system; Interpret the predictions and simulations of biological systems

Prerequisites

Modelling biological systems builds on first year biology courses, especially ecology, mathematics, statistics and physics. This basic knowledge is needed to adequately handle the models in the current course.

Details

Institution: WUR

Period: Period 2

Level: Intermediate

EC: 6 ec

Modelling Functional Diversity in Crop Production

The last century, agriculture has been characterised by control and specialization: monocrops being grown with intensive use of fertilizers and pesticides. But environmental concerns, disease and pest resistance to pesticides and finite availability of resources call for a change in strategy. Diversity both in terms of utilizing different crop species and in combining different ecology-based management practices may provide an alternative. Yet, the mechanisms that drive the relationship between species diversity and functioning ecosystem functioning remain poorly understood. Also, we have limited knowledge on how these relationships can be used in combination with diversity in crop management to create productive, resource-efficient and resilient crop systems. This course focusses on this knowledge gap.
The first part of the course deals with what we can learn from natural systems, and analyzes the mechanisms that drive relationship between diversity and ecosystem functioning. The focus will be on resource sharing between neighbouring plants. The second part addresses how this knowledge about effects of species diversity can be utilized in agricultural systems, looking at resource sharing between different species in a mixed cropping situation. Finally, the third part focuses on diversity in management options in the suppression of weed growth, taking into account the interaction between crop and weed plants and how this plays a role in integrated weed management (weed control through a combination of methods extending beyond herbicide use).

To determine to which extent diversity in crop species and crop management can be functional, computer simulation models have become an indispensable tool for researchers in this field. This course therefore has a heavy modelling component, as all three parts outlined above will contain modelling practicals in addition to measurements on plants in the greenhouse. Simulation models will be used for the analysis of plant-plant interaction and the consequences of management decisions. Therefore, during the course, you will be taught the principles of both functional-structural plant modelling using the modelling platform GroIMP, as well as mechanistic modelling of crop-weed interactions, using the modelling platform FST.

Program/Minor

Learning Outcomes

Explain how the evolution of plant traits relates to competition for resources between individual plants; Explain how plants interact and adapt their competitiveness depending on the identity of neighbouring plants; Explain how community ecological processes can determine population species composition over time; Explain why improvements in crop performance and resource use efficiency associated to species diversity can result from interactions between species and competition for resources; Explain how the quantitative characterization of intra and interspecific competition among plant species relates to a descriptive model on yield loss due to weeds; Explain the principle and suitability of a range of strategies aiming a reduced use of herbicides for weed control; Explain how two advanced crop modelling techniques can help analysing the role of plant competition in crop performance; Translate theoretical concepts into concrete scientific questions, hypotheses and meaningful experiments; Integrate knowledge on plant interactions using plant growth modelling, and interpret model output on plant interactions and competition; Communicate the contents of a scientific study in a simulated conference setting using a clear and attractive poster and to present this poster with condensed information; Analyse how diversity in weed management measures, including crop diversification, contributes to a sustainable management of weed populations, using weed demographic modelling;

Prerequisites

Assumed: General knowledge on Crop Ecology and Agronomy Mandatory: Laboratory Safety

Details

Institution: WUR

Period: Period 4

Level: Advanced

EC: 6 ec

Moleculaire Celbiologie & Immunologie

Tijdens dit vak zullen de basisprincipes van moleculaire regulaties in menselijke, of dierlijke cellen behandeld worden, met een speciale focus op cellulaire functies/interacties tijdens immuunreacties.

De basisprincipes van de regulatie van celgroei, cel specificatie en de communicatie in en tussen cellen wordt behandeld. Er wordt een basisintroductie van het immuunsysteem gegeven en er zullen een aantal activatiemechanismes besproken worden. De focus komt te liggen op de soorten cellen die een rol spelen in het immuunsysteem en de ziektes van het immuunsysteem. Ook wordt de rol van het immuunsysteem in het voorkomen en behandelen van ziektes uitgelegd.

In de vorm van een Onderzoekspracticum analyseert de student het effect van diverse stressoren op de regulatie van lysosomale biogenese.

Program/Minor

Learning Outcomes

Het begrijpen van de basale principes van de regulatie van celgroei en cel specificatie; Het begrijpen van de basale principes van communicatie in en tussen cellen; Het begrijpen van de 'architectuur' van het immuunsysteem; Het begrijpen van de rol van het immuunsysteem in een aantal ziektes en behandelmethodes; Kritisch analyseren van de meest recente vakliteratuur uitgaande van de eigen verkregen resultaten; Kunnen beoordelen of de geobserveerde effecten van de gebruikte stressoren zijn zoals verwacht, of verrassend zijn.

Details

Institution: TU Delft

Period: Period 1

Level: Intermediate

EC: 8 ec

Moleculaire Celbiologie en Genetica

In deze inleidende cursus biologie worden de algemene principes behandeld die ten grondslag liggen aan het leven op aarde. Alle biologische organismen zijn opgebouwd uit cellen. Cellen zijn de kleinste functionele eenheid van leven en hun eigenschappen worden bepaald door fysische en chemische wetmatigheden. Tussen cellen en de omgeving bestaat een wisselwerking. Cellen nemen signalen en voedingsstoffen/energie op uit hun omgeving en zetten die om in specifieke producten en gedragingen die nodig zijn voor overleving, functioneren en celdeling. De structuur van de cel en zijn functies worden bepaald door eiwitten, gecodeerd in het DNA, wat een informatiedrager is die van generatie tot generatie wordt doorgegeven. In het tweede deel van de cursus worden de structuur en functie van het genetisch materiaal op moleculair niveau en het effect van mutaties op de functie van genen behandeld. Ook kijk je dan hoe genetisch materiaal in de cel wordt opgeslagen en over dochtercellen verdeeld. Daarnaast krijg je een inleiding in de moderne moleculaire genetica.

Program/Minor

Learning Outcomes

De structuur van biomoleculen, de algemene processen in de cel en de opbouw van biologische membranen beschrijven; De intracellulaire compartimenten van de cel en de bijbehorende functies benoemen; De verschillen en overeenkomsten van celademhaling en fotosynthese benoemen; Hoe signalen van buitenaf aan de cel worden doorgegeven; Beschrijven/uitleggen op welke manier de celcyclus wordt gereguleerd; De overeenkomsten en verschillen tussen mitose en meiose uitleggen; De principes van DNA-replicatie en transcriptie uitleggen; De principes van RNA-processing en translatie en de rol van tRNA beschrijven; De processen replicatie, transcriptie en translatie bij prokaryote en eukaryote organismen vergelijken; De werking van virussen beschrijven; Eenvoudige moleculaire celbiologische technieken begrijpen; De opgedane kennis toepassen; Daarnaast werk je aan een academisch-kritische houding ontwikkeld met betrekking tot de positieve dan wel negatieve maatschappelijke consequenties van biologische kennis en onderzoek.

Prerequisites

Mandatory: Basic vwo-knowledge of Biology and Chemistry

Details

Institution: UU

Period: Period 3

Level: Introductory

EC: 7.5 ec

Molecular and Cellular Biophysics

De combinatie van de vakken Moleculaire Celbiologie, Life Sciences en Biochemie geven een complete introductie van de werking van de levensprocessen in de cel. Studenten maken kennis met de moleculaire wereld en leren een moleculaire manier van denken aan. In de Moleculaire Celbiologie nemen we het functioneren van de kleinste levende eenheid, de cel, als uitgangspunt. Tijdens de colleges worden de overeenkomsten en verschillen tussen prokaryotische en eukaryotische cellen besproken. De functie en opbouw van verschillende organellen komen uitgebreid aan bod, evenals de mechanismen waarmee eiwitten op hun juiste bestemming in de cel terechtkomen. Vervolgens worden de verschillende fasen van de celcyclus en celdeling behandeld, inclusief de wijze waarop genetische informatie wordt doorgegeven aan de volgende generatie en de mogelijke gevolgen van fouten in deze processen. Tot slot wordt op moleculair niveau besproken hoe genetische informatie vanuit het DNA via RNA, wordt omgezet tot eiwitten. Het onderwijs wordt verzorgd in de vorm van hoorcolleges. De stof wordt geoefend via online quizzes en werkcolleges

Program/Minor

Learning Outcomes

De student kan basiskennis van cellen, organellen, celomgeving, celcyclus, celdeling en celgedrag van functionerende cellen reproduceren; De student kan basiskennis van de principes van replicatie en DNA structuur reproduceren; De student kan basiskennis van genexpressie en transcriptie regulatie reproduceren; De student kan basiskennis van eiwit productie (translatie), vouwing, modificatie, transport binnen de cel, secretie en afbraak reproduceren; De student kan namen en werking van de meest belangrijke enzymen beschrijven; De student kan verklaren wat de consequenties zijn van hypothetische mutaties in de cel; De student kan zelf onderlinge verbanden leggen tussen de diverse processen die behandeld worden; De student kan zelfstandig leren met een engelstalig leerboek; De student kan verschillende moleculaire processen herkennen in voorbeelden van experimentele data uit engelstalige vakliteratuur

Details

Institution: TU Delft

Period: Period 4

Level: Introductory

EC: 5 ec

Molecular Cell Biology

De combinatie van de vakken Moleculaire Celbiologie, Life Sciences en Biochemie geven een complete introductie van de werking van de levensprocessen in de cel. Studenten maken kennis met de moleculaire wereld en leren een moleculaire manier van denken aan. In de Moleculaire Celbiologie nemen we het functioneren van de kleinste levende eenheid, de cel, als uitgangspunt. Tijdens de colleges worden de overeenkomsten en verschillen tussen prokaryotische en eukaryotische cellen besproken. De functie en opbouw van verschillende organellen komen uitgebreid aan bod, evenals de mechanismen waarmee eiwitten op hun juiste bestemming in de cel terechtkomen. Vervolgens worden de verschillende fasen van de celcyclus en celdeling behandeld, inclusief de wijze waarop genetische informatie wordt doorgegeven aan de volgende generatie en de mogelijke gevolgen van fouten in deze processen. Tot slot wordt op moleculair niveau besproken hoe genetische informatie vanuit het DNA via RNA, wordt omgezet tot eiwitten.

Program/Minor

Learning Outcomes

De student kan basiskennis van cellen, organellen, celomgeving, celcyclus, celdeling en celgedrag van functionerende cellen reproduceren; De student kan basiskennis van de principes van replicatie en DNA structuur reproduceren; De student kan basiskennis van genexpressie en transcriptie regulatie reproduceren; De student kan basiskennis van eiwit productie (translatie), vouwing, modificatie, transport binnen de cel, secretie en afbraak reproduceren; De student kan namen en werking van de meest belangrijke enzymen beschrijven; De student kan verklaren wat de consequenties zijn van hypothetische mutaties in de cel; De student kan zelf onderlinge verbanden leggen tussen de diverse processen die behandeld worden; De student kan zelfstandig leren met een engelstalig leerboek; De student kan verschillende moleculaire processen herkennen in voorbeelden van experimentele data uit engelstalige vakliteratuur

Details

Institution: TU Delft

Period: Period 1

Level: Introductory

EC: 5 ec