Trondheim Logical Modelling Course BI8040
Logical Modeling for Experimental Design in Current and Future Biotechnology and Biomedicine
The course builds on approaches and technologies that have been developed within the NTNU DrugLogics initiative (www.druglogics.eu), which use the logical modelling formalism for predicting the outcome of chemical perturbations (cancer drugs) on cancer cell fate decisions, and on inflammation-based diseases. 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 and other diseases. The students will practice the new acquired skills in a small team-based project.
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
The PhD-version of the course (BI8040) gives 7,5 ECTS study points. Teaching and course material: The course will start August 20 and end August 31. We will provide a web link to relevant literature and web resources that the course will be based on. Some of the papers will constitute mandatory reading before the course starts.. The course will consist 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. Some ideas for modelling projects will be provided. In addition, students may want to design their own project based on other literature or their PhD project results. These project ideas should be sent to the teaching staff prior to the course. Students will present their ideas for projects on Day 3.
Day 1: Introductory lectures, introduction to responsible research and innovation and team-based learning-sessions
Day 2-5: Combination of lectures/TBL and supervised student group work with tools and resources
Day 6-9: Project-work: develop and characterise logical models, and their implications for knowledge discovery
Day 10: Presentations. The students will use an eNotebook for the project and reporting.
Students write their individual reports after the course finishes, deadline for submitting the report: 11 September.
Martin Kuiper, Professor Systems Biology, IBI, NTNU
Denis Thieffry, Professor Systems Biology, Department of Biology, IBENS, Paris, France, and Guest Researcher at the Systems Biology group of IBI, NTNU.
Astrid Lægreid, Professor Functional Genomics, IKOM, NTNU
Rune Nydal, Ass. Professor ethics of technology, IFR, NTNU
Åsmund Flobak, postdoc in Astrid Lægreid’s Functional Genomics group, NTNU
Aurélien Naldi, postdoc in Denis Thieffry’s computational systems biology team, IBENS
Laurence Calzone, Research engineer, Computational Systems Biology of Cancer, Institut Curie, Paris
Anna Niarakis, Associate Professor Computational Systems Biology, at Univ Evry, University of Paris-Saclay
Some additional guest lecturers will participate. The language of lectures and instructions is English.
Application Procedure (deadline 10 August 2018):
Applications, including a motivation letter and a CV should be sent by email to email@example.com. There is a maximum of 25 students, deadline for application is August 10. Students should have an MSc either in biotechnology, biology, biomedicine, computational biology or bioethics.
- Digital Life Norway Research School
More information about the course program, teaching and material can be downloaded here: Digital Life Norway Research School Course Program. Further Information: For questions about the course, contact Martin Kuiper: firstname.lastname@example.org. Questions related to travel grants from Digital Life Norway Research School should be directed towards Liv Eggset Falkenberg: email@example.com.