Short Course Programme

The SLAS Europe 2020 Short Course Programme provides additional, pre-conference, in-depth instructions on topics, issues and techniques related to laboratory science and technology community.

Date: Tuesday 2 June

  • Full-Day Courses include lunch and an afternoon coffee break
  • Half-Day Course include an afternoon coffee break
SLAS MemberNon-MemberStudent
Tuesday 2 June: Full-Day Courses
Data Analytics with KNIME€400€500€100
Fundamentals of AI-Driven Closed-Loop Experimentation€400€500€100
Tuesday 2 June: Half-Day Courses
Use of Genetic and Non-Genetic Agents for Target Validation€200€250€100

All registration rates are exclusive of 20% Austrian VAT.  Click here for information on VAT.

Tuesday 2 June: Full-Day Courses | 10:30-17:30

This is a full-day, hands-on course on data wrangling, analysis (statistical tests), visualisation, machine learning and clustering. The data analytics are done using the software KNIME and does not require any programming skills. An hour is dedicated to data that students have brought with them, so that they can go home with the beginning of a solution to their own problem.

Topics Covered

  • Loading data, inspecting for missing data, obtain basic descriptive statistics
  • Slicing through data selecting columns or rows of data (data wrangling)
  • Summarising data per condition (different average and spread measures)
  • Visualising data (bar plots, histograms, violin plots, plate heat maps, etc.)
  • Unsupervised clustering (hierarchical, k-means, t-SNE)
  • Supervised clustering (linear regression, Random Forrest)

The course does not aim to introduce the theory and mathematics of each topic, but how to actually carry out the methods.


Dr. Marc Bickle, Head Technology Development Studio (TDS), Max Planck Institute of Molecular Cell Biology and Genetics

The constant advancement of laboratory automation techniques has greatly increased the speed and scope of experimentation while reducing experiment cost. Even with these advances, many experimental systems are too large to explore exhaustively. The alternative is to collect a feasible subset and use it to train a predictive model for the rest. The question then becomes how to choose this subset. The field of active machine learning provides methods that can be used in a closed-loop experimental system to iteratively choose experiments that are most likely to yield an accurate predictive model. In this course, participants will experience the development of an “automated science” AI-driven process from definition of an experimental space and process goals, to automating data collection, building a predictive model and using the model to choose new experiments. The process will be focused on playing a simple children’s board game (e.g, Battleship) using an integrated system containing a liquid handling robot, a plate reader, a robotic arm and plate management capabilities. It is strongly encouraged that participants in this course have programming experience in Python or Matlab.

Topics Covered

  • Basic assay design
  • Using simulation in design and testing of modeling and active learning components
  • Set up of integrated robotic system for AI-driven experimentation
  • Management of communication between AI and hardware


Joshua D. Kangas, Ph.D., Assistant Teaching Professor, Computational Biology Department, School of Computer Science, Carnegie Mellon University.

Robert F. Murphy, Ph.D., Ray and Stephanie Lane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering, and Machine Learning Head, Computational Biology Department, School of Computer Science, Carnegie Mellon University.

Tuesday 2 June: Half-Day Course | 13:30-17:30

Drug attrition in clinical trials due to inaccurate pre-clinical target validation is a huge problem facing drug discovery. Drug candidates with adequate pharmacokinetics and safety margins still commonly fail, due to lack of efficacy. This suggests that often the ‘wrong’ target has been selected as the focus of the therapeutic programme. Successful target validation will increasingly rely on the use of novel genetic and non-genetic tools including CRISPR and targeted protein degradation to disease relevant models. Combined with computational pathway analytics, testable hypotheses can be generated in an unbiased manner. In this SLAS Short Course, I will show how these technologies are being applied in the field of target validation in industry and academia and I will address how the integration of genome editing, targeted protein degradation and bioinformatic tools is impacting the search for newly validated targets. Finally, I will provide insights on how the use of these new tools in target validation might revolutionise the drug discovery paradigm.

Topics Covered

  • Target validation in drug discovery
  • Use of CRISPR and other genome editing tools for validating the right targets
  • Use of emerging tools in the targeted protein degradation area in target validation
  • Integration of these tools with bioinformatics pipeline
  • High content biology
  • Data analysis


Davide Gianni, PhD., Associate Director Functional Genomics, Discovery Biology, Astrazeneca

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