Algorithms for Network-based Bioinformatics
We will cover topics such as complex network models to characterise and analyse biological systems, approaches to infer network structures from given biological measurements, strategies of network enhancement through network integration, predictions based on network structures, and graph generation (molecular design). Specifically, the course contains the following topics: Background on molecular data: systems biology, data-driven
Evolutionary Algorithms
In this course we consider a specific subfield of Artificial Intelligence: Evolutionary Algorithms (EAs). These algorithms, sometimes also identified as being part of the class of bio-inspired algorithms, have as a metaphor the concept of natural evolution, i.e., the mechanisms by which, the fittest individuals in a population survive, reproduce, and in doing so, over
Societal Challenges & Innovation Theory
The analysis of innovation processes is much more powerful when using theories of socio-technical change and innovation. Theories of socio-technical change and innovation can be a powerful tool to understand and help solve some of the grand challenges that our society faces. The course focuses on the UN Sustainable Development Goals (SDGs), like SDG 3
Data Science for Plant Breeding and Genetics
One of the major goals for plant breeders is to identify candidate varieties that are well adapted to the set of environmental conditions that are relevant for the agricultural system of interest. To achieve this goal, breeders characterize their genotypes in multi-environment trials or in phenotyping platforms. The ranking of genotypes might change across trials,
Climate Smart Agriculture
Agriculture contributes significantly to global warming through large scale greenhouse gas emissions. At the same time many agriculture systems are vulnerable to climate change and without adaptation global food production could significantly reduce affecting food security. In response to these challenges the concept of climate smart agriculture has been developed. Climate-smart agriculture (CSA) aims to
Plant Breeding
This course introduces students to key principles of plant breeding. Plant breeding is the science-driven creative process of changing the traits of plants in order to develop new plant varieties. Several essential approaches and tools used in the process are discussed: different modes of reproduction, selection methods, including molecular selection methods, the production of hybrid
Plant Biotechnology
During the course, lectures will be given to introduce various aspects of Plant Biotechnology. The first set of lectures outlines the techniques used in Plant Biotechnology, including recent developments such as gene editing, and in the second set, specific examples of application of these techniques and the effects on science and society are provided. Societal
Introduction to Machine Learning (TU Delft)
This course equips students with foundational understanding of key concepts of Machine Learning (ML) and demonstrates how to solve real world problems with ML techinques. It covers the following topis: Learning Theory, Supervised Learning, Unsupervised Learning, and Transfer and Ensemble Learning
Biotic Interactions
This course provides knowledge on recent developments in research on plant-pathogen and plant-insect interactions. This will include the molecular targets and signal transduction pathways involved, and the ecological aspects of biotic interactions in nature and in agriculture. With regard to defense against pathogens, the innate immune response and the gene-for-gene model will be discussed in
Molecular Systems Biology
Nowadays increasing numbers of complete genomic sequences are available and methods have been developed to study system wide gene expression, protein abundances and interactions and metabolite formation. Systems biology integrates the results of the different omics techniques in order to understand how they work together by using dedicated analysis and visualisation techniques (e.g. machine learning,