Examination arrangement

Course content

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

Learning outcome

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.

Learning methods and activities

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.

Further on evaluation

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

Recommended previous knowledge

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

Course materials

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.