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In 2022 the course agenda was updated, a revision of the course papers was done, and the mandatory and optional reading requirements were better explained. The course was attended by 12 students, one of them funded by NORBIS (the Norwegian Bioinformatics Organisation). The Course involved 12 teachers, 5 of whom contributed over Zoom. The co-organiser Denis Thieffry, from ENS/Paris, and student assistant Eirini Tsirvouli, from Oslo, were present for the full two weeks, and teachers Farinas and Zobolas travelled to Trondheim for the course. In addition to being in a classroom for the lectures and project work, the students had one week to prepare for the course and 10 days to submit a final project report on which they were graded (passed/not passed). The report was in the form of an electronic notebook, containing text and a series of executable analyses that were performed in the project. For two of the students, some additional analyses and reporting were requested, and their grading is still pending, but 11 students received the grade ‘passed’. The students were requested to fill in a questionnaire (attached), indicating that for most of them the course was challenging but worked out well and was also relevant for their PhD/MSc project. They furthermore indicated that they would recommend the course to their fellow students.

The course is given every second year, so no course was given in 2023.

Survey among students is attached.

NTNU:TIA:REST

Institutt for biologi

Department of Biology

Engelsk

Mer om vurdering

Each student will submit an individual report, on which they will be assessed.

Anbefalte forkunnskaper

MSc either in biotechnology, biology, biomedicine, computational biology or bioethics

Faglig innhold

The course builds on approaches and technologies that are developed in the NTNU DrugLogics initiative (www.druglogics.eu), based on the logical modelling formalism for predicting the outcome of chemical perturbations (cancer drugs) on cancer cell fate decisions. This approach combines knowledge management, logical model construction and computational simulation with experimental assays and hypothesis testing for pre-clinical (biotechnological) drug development and clinical decision support. The course will exemplify how such approaches can be used in both the biotechnological and biomedical sectors such as pre-clinical drug discovery and repurposing, and clinical development of diagnosis and (combinatorial) treatment of cancer. The course content will focus on: theoretical principles as well as existing tools and resources for logical modeling resources and tools for knowledge management to underpin logical modeling computational biology assisted reasoning for (large scale) hypothesis management by using logical modeling (large scale) hypothesis management for interpretation of biotechnology-/biomedicine experimental data and for design of new experiments fundamental challenges in future biotechnology and biomedicine that require logical modeling for adequate hypothesis management discussion of trajectories for development of modeling-based research infrastructures for future biotechnology and biomedicine including reflections on implications of each of the trajectories for users and stakeholders of these infrastructures

Kursmateriell

The course is based on recent scientific papers, tutorials and videos. Students will receive a weblink to relevant literature and web resources that they should study before the course work starts. The students will use an eNotebook for the project and reporting.

Læringsutbytte

Students will know how to use software tools like GINsim for building logical models and simulating logical model behavior under various experimental conditions. They will know which public resources exist to find information from which to build logical models, and they will know how to use scientific literature to further extend logical models. Students will understand for what purpose logical models can be used, and how logical models can help them in their research. They will be able to apply this knowledge in the design of model-based experimental research. Students will understand Responsible Research and Innovation dimensions of model-based Systems Biology and Systems Medicine approaches, with respect to using and disseminating research results and user-perspectives of trust and confidence in these results.

Læringsformer og aktiviteter

The course consists of 12 lectures and 50 hours of team/project-based learning: Student exercises and project-based learning in multidisciplinary teams applying tools and resources for modeling-based hypothesis generation and management. Day 1: Introductory lectures, introduction to responsible research and innovation and team-based learning-sessions Day 2-5: Combination of lectures/PBL and supervised student group work with tools and resources Day 6-8: Project-work: develop and characterise logical models, and their implications for knowledge discovery Day 9, 10: Project work, preparing presentations, presenting the results.

Logical Modeling for Experimental Design in Biotechnology and Biomedicine

Biologi

Biologi - doktorgrad (ph.d.)

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The course is not given in 2021, the next instance is planned for 2022. In 2021 only a scaled-down version was given, in the form of a 4-day introductory course to logical modeling, without study points. 10 students attended this.
For the 2022 version an additional lecture in logical modeling basics is planned, and also in the application of logical modelling for multiscale simulations. The list of papers supporting the course will be better defined, in sections 'mandatory reading' and 'additional material'. 

Tiltak

Plans for 2022

Report on the BI8040 course: Logical Modelling for Experimental Design in Current and Future Biotechnology and Biomedicine. Based on feedback from the 2020 course, in 2022 an additional lecture in logical modeling basics was given and also some attention to the application of logical modelling for multiscale simulations was added. The list of papers supporting the course was better defined, in the sections 'mandatory reading' and 'additional material'. The 2022 course was sponsored by Norbis (the national research school in bioinformatics, biostatistics and systems biology), and was attended by 13 national and international students (5 PhD, 8 MSc students). The students worked in groups, and overall, they did very well. A course survey (see attached BI8040 2022 assessment.pdf) was performed, indicating the following: -       Good to challenging lecture level.-       Fair to high workload.-       High relevance to PhD/MSc work.-       No specific improvement points, other than maybe the project. Other suggestions for improvement: -       Provide more background material and tutorials for beginners.-       Have group-feedback sessions to better check how groups function.

Most of the students were very happy about the course, as it provided them with a useful skill base.

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The course was assessed by way of a student survey, filled in after the course. Overall the students were happy with the structure and content of the course. Some main points for improvement that the survey provided were: <ul><li>better list the background material for the course: what is expected from the students.</li><li>lectures: some more information about the basic principles of modelling</li></ul>

Each student will submit an individual report, on which they will be assessed. 

Students will know how to use software tools like GINsim for building logical models and simulating logical model behavior under various experimental conditions. They will know which public resources exist to find information from which to build logical models, and they will know how to use scientific literature to further extend logical models. Students will understand for what purpose logical models can be used, and how logical models can help them in their research. They will be able to apply this knowledge in the design of model-based experimental research. Students will understand Responsible Research and Innovation dimensions of model-based Systems Biology and Systems Medicine approaches, with respect to using and disseminating research results and user-perspectives of trust and confidence in these results.  

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Logical Modeling for Experimental Design in Biotechnology and Biomedicine - BI8040