BT8121 - Transdisciplinary biotechnology - a Digital Life Norway course - 7.5 ECTS

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Registration deadline: 29 August 2024

Credits: 7.5 ECTS

Part 1: 1st - 4th October 2024 at Valberg Slektsgård

Part 2: Project work in October and November 2024, remote work

Part 3: 3rd - 4th December 2024, Place TBD

Course content

This course is for all PhD students and post-doctors 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 with real datasets (see below for choices) with a highly skilled supervisor and an interdisciplinary team. In the plenary sessions, there will be special focus and training on how to work in interdisciplinary research groups, common interdisciplinary challenges and possible solutions, data management, and how to implement the principles of RRI (responsible research and innovation) in your research. Participants with a background in life sciences, biotechnology, bioinformatics, mathematics, or computer science are encouraged to apply.

Structure of the course

The course is composed of 3 teaching blocks:

Part 1: 1st - 4th October 2024 at Valberg Slektsgård

1. An introduction to transdisciplinary ways of working in biotechnology by Trygve Brautaset (Professor, NTNU/Scientific director DLN)
2. An engaging session on concepts of responsible research and innovation with Roger Strand (Professor UiB/NTNU).
3. An introduction to research data management practices and solutions for remote group work by Korbinian Bösl (DLN/ELIXIR Norway).
4. An introduction to the respective projects, group members, and project leaders. Significant time will be dedicated to project work.

Part 2: Project work in October and November 2024, remote work

The groups will collaborate through digital platforms and work on their respective projects. 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 to work with the data and their importance in generating new knowledge. Each group work will consist of one or more tasks linked to modelling and/or analysis of the data.

Part 3: 3rd December 2024 – 4th December 2024, Place TBD

1. Presentations of the work and results from all projects.
2. Sharing experiences on transdisciplinary group work and collaboration
3. Discussions on responsibility and challenges in research data management

Projects

Project 1 - FISHCOMM: Novel mechanisms for cell-to-cell communication in fish

Project leader:  Dhivya Borra Thiyagarajan (UiT)

The FISCHCOMM-project is aiming to study the phenomenon of cell-to-cell communication in salmon fish cells. Cell-to-cell communication might be very important during tissue regeneration and innate immune responses. Different communication tactics in cells include Tunneling Nanotubes (TNTs), Extracellular vesicles (EVs), and Cell fusion (CF). Our current results indicate that exposing the keratocytes to nanoparticles and bacteria forms TNTs. Here we will look at the formation of TNTs and using the imaging data how to measure the TNTs, find the length of TNTs, and look for mitochondrial transfer, which will be the aim of the project. To do this, the student will get the imaging data from cells stressed with bacteria and microparticles obtained from deconvoluted microscopy and QPMI. In addition, students will get to learn how keratocyte cells look and how these cells migrate in the dishes.

Project 2 - DigiSal: Systems biology for adapting salmon breeding and nutrition strategies to modern feedstuffs

Project leader: Jon Olav Vik (NMBU)

Salmon farming in the future must navigate conflicting and shifting demands of sustainability, shifting feed prices, disease, and product quality. The industry needs to develop a flexible, integrated basis of knowledge for rapid response to new challenges. The Digital Salmon will be an ensemble of mathematical descriptions of salmon physiology, combining mathematics, high-dimensional data analysis, computer science and measurement technology with genomics and experimental biology into a concerted whole. See also: The Digital Salmon on YouTube. The participants will investigate the metabolic repertoire of Atlantic salmon fed contrasting diets in a published feed-switch experiment, using a combination of omics data and the constraint-based model SALARECON. This is also a good opportunity to experience FAIR data and model management.

Project 3 - SiD: An interdisciplinary data-driven approach to resolve sigma-factor-specific promoter dependency in bacteria

Project leaders: Rahmi Lale, Essa Ahsan Khan (NTNU)

The interdisciplinary project, SiD, focuses on advancing the understanding of transcriptional regulation in bacteria through a data-driven approach. This innovative project combines high-throughput in vitro transcription technique with in vivo screening, employing DNA and RNA sequencing to precisely map cis-acting DNA sequences for 17 sigma-factors across three bacterial species: Escherichia coli, Pseudomonas putida, and Bacillus subtilis. By harnessing extensive in silico analysis, including statistics and machine learning, we aim to identify sigma-factor-specific cis-binding motifs within bacterial promoters.

In the SiD project we have generated a significant volume of data, both DNA and RNA sequences. Participants will use this data to learn about data collection, exploitation, and management, with the aim of generating new knowledge and fostering innovation. This initiative not only enhances theoretical knowledge but also provides practical skills in handling and analyzing complex datasets, crucial for future scientific advancements.

Project 4 - NMRLipids: Understanding lipid systems at an atomistic level

Project leaders: Aleksei Nesterenko, Markus Miettinen (UiB)

NMRLipids Databank - is a project on structure biology/chemistry where experimental data are matched against atomistic simulations. The well-known PDB project shares the coordinate data of proteins, but lipid structure is dynamic and cannot be described in terms of coordinates. NMRLipids Databank unites data records from solid-state NMR of lamellar phase and X-ray scattering of unilamellar liposomes and match them against atomistic molecular dynamics simulations. Databank is organized as a GitHub project, where dynamic data from processed MD trajectories are stored together with experimental data. Students will learn how to add data to the databank, and how to use API for requesting and analysing content-to-structure relationships over the entire database. Students are also invited to implement FAIR principles for improving the Databank: select and/or develop ontology for describing records, develop metadata standard for describing raw trajectory deposition and raw NMR data deposition.

Practicalities

• 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).
• You will be asked to indicate your preference for a project in the registration form. More projects may be added in which case, we will contact you and give you the option to revise your preference.
• 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 preference). Please note: we will try to assign participants to projects that are not closely connected to their field of study. So if you are a member of one of the offered projects, you will be assigned to a different project in the course.

Questions?

Course coordinator: Anamika Chatterjee, RRI Senior Adviser, Centre for Digital Life Norway.

Practical matters: Rosalie Zwiggelaar, DLN Research school coordinator