Transdisciplinary life science – a Digital Life Norway course
IMPORTANT INFO: We will update the events if there are cancellations or other changes due to covid-19. For now, you can sign up as usual, but please do not book flights or accommodation until you receive confirmation that the course will be held. You will be notified per e-mail about the courses you have registered for.
+ home university of project leader
Project work in-between plenary meetings
Registration is closed.
This course is for all PhD students and postdoctors who want to learn more about working on transdisciplinary research projects within biotechnology and the life sciences. The main part of the course will be to work directly on a pre-defined research project (see below for choices) with a highly skilled supervisor and a transdisciplinary team. There will be special focus and training on how to work in teams with a common goal, how to implement the principles of RRI (responsible research and innovation) in your research, and data management. Participants with a background in life sciences, biotechnology, bioinformatics, mathematics, or computer science are encouraged to apply, in addition to students from the humanities with an interest in biotechnology.
A course schedule will be provided closer to the start date.
Structure of the course
The course is composed of 3 teaching blocks:
- A start-up plenary (3-4 days) at NTNU, where participants will be introduced to the different scientific projects that constitute the scientific part of the course. The start-up plenary will also cover the essentials on transdisciplinary group work, introduction to remote collaboration and sharing of big data, and introduction to RRI.
- The group projects offered in the course are based on scientific data made available by the group leader and their research group. The students will learn about the data collection, and their importance in generating new knowledge and/or innovation. Each group work will consist of one or more tasks linked to modelling and/or analysis of the data. The group projects are distributed at different Norwegian Universities (in 2020: NTNU, UiB, UiT), and might require that participants travel.
- The final plenary session (2 days) will take place at NTNU, and will contain presentations of the work and results from all scientific projects. There will also be round-table discussion and sharing of experiences on transdisciplinary group work and collaboration.
The start-up plenary will cover:
- Practical information about the course including information about the evaluation method
- Discussions on responsible research and innovation, with Roger Strand (DLN/UiB/NTNU) and Dominique Chu (University of Kent)
- Introduction to data management and tools useful in remote group work, with Korbinial Bösl (Elixir/DLN/UiB)
- Practical lessons on teamwork, based on methods and experience from Experts in Teamwork at NTNU
- Scientific introduction to the projects led by the project leaders (in groups)
Fluctuations in fluorescence imaging for nanoscopy of living and fixed cells
Project leader: Krishna Agarwal, Department of Physics and Technology, UiT – The Arctic University of Norway
Can we see features as small as 50-100 nm using a wide-field fluorescence microscope capable of resolving features as big as 250 nm? Can we image living cells with this super-resolution? This project is about simple technical solutions, namely fluorescence fluctuation based nanoscopy techniques, for these two problems. Natural photokinetic properties of fluorescent molecules result into unique signatures, which can be used for super-resolution without introducing harmful chemicals or intense laser light onto living cells. We will explore four such nanoscopy techniques namely MUSICAL, SRRF, SACD, and ESI using pre-acquired image datasets.
Integrative -omics analysis for improved characterization of breast cancer
Project leaders: Tone Frost Bathen and Guro F. Giskeødegård, Department of Circulation and Medical Imaging, NTNU
Analysis of large biological data sets and integrated analysis of data from different molecular levels are challenging and exciting research areas for the coming years. Combined -omics approaches will provide more comprehensive characterization of disease mechanisms compared to the study of single -omics, and is highly relevant to study complex diseases such as cancer. In this project we will use a big data-approach to combine metabolomics and transcriptomics data from tumors of breast cancer patients with relevant clinical variables describing the patients’ disease. The aim is to identify biological mechanisms related to cancer characteristics and patient diagnosis. Further, patient follow-up data are available and will be used to determine possible prognostic biomarkers for clinical use. Optimization of suitable machine learning techniques will be relevant for solving this challenge
Multiomics data analysis in toxicology
Project leaders: Nello Blaser, Department of Informatics, and Marta Eide, Department of Biological Sciences, University of Bergen
In this project, you will analyze biometric and omics data from a toxicology study. You will apply and evaluate omics data analysis on real data sets, interpret the results and use multiomics techniques to combine multiple datasets. The data will come from an study exploring the effect of exposing juvenile Atlantic cod (Gadus morhua) to three different chemical compounds that are known to bind and activate peroxisome proliferator-activated receptors (PPARs) and affect lipid metabolism in other species. A range of different omics data, including transcriptomics, proteomics and lipidomics are available. The aim of the study is to understand more about the signaling networks leading from PPAR-activation in cod. You will first analyze the omics datasets individually, before combining them to determine if analysis of the joint data provides additional information.
The in silico patient: perspectives for therapy design
Project leaders: Martin Kuiper, Department of Biology, Astrid Lægreid, Department of Clinical and Molecular Medicine, and Rune Nydal, Department of Philosophy and Religious Studies, NTNU
The objective of the project is to develop a computer model of a patient’s tumour that can be used for computer simulations of the effects of drug therapies. Students will be given a generic cancer model that they need to extend based on a set of criteria that take into account specific mutations and cancer cell activities that characterise the tumour. The processing and analysis of tumour data will be done with the software package R. Model extensions will be based on resources that include databases like Signor, IntAct and Reactome. The model simulations will be performed in the Boolean modelling software GINsim. In addition to data analysis, network building and computer simulations, students will have to consider the various ethical and data confidentiality issues that are associated with bringing a computer-model based therapy prediction platform to the clinic. The role of the patient, patient organisations, clinicians, and regulatory bodies should be addressed, and the various hurdles should be identified and approaches to deal with these should be proposed.
Network inference using single cell gene expression data
Project leader: Anagha Joshi, Department of Clinical Science, University of Bergen
In this project, we will tackle the challenge of learning regulatory networks from individual cells in our body using single cell transcriptomic data. Multicellular organisms including humans have same DNA in every cell, nevertheless the cells of every organ need to perform a highly specific function. The key question therefore is to understand how a cell develops the ability to perform divergent functions. Emergence of single cell technologies has provided a unique opportunity to understand this process at a single cell level. Despite the promise, single cell transcriptomic data analysis is full of technical and biological challenges. In this project, we will familiarize ourselves with two main learning goals: reconstruction of regulatory networks and single cell transcriptomic data analysis. We will then apply diverse strategies to infer regulatory networks across different human single cell datasets. We will work in a team to explore different strategies of inferring networks and will evaluate which ones work better. We will aim to summarize the outcome of this project as a small journal article.
Members of Digital Life Norway Research School will be prioritized for this course. The research school also covers all costs related to travel and accommodation for their members (remember to apply for a travel grant).
After the registration deadline for the course, accepted participants will be notified and receive information about which group project you will work on (based on your wishes).
Miscellaneous: Ragnhild I. Vestrum, Coordinator, Centre for Digital Life Norway