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The recommendations from the 2022 edition were:<ul><li>Assign students to groups based on a random principle, to avoid adverse grouping affects. This was implemented in 2023, by sorting the students by name, alphabetically.</li><li>Do the grouping with the classroom, to avoid problems with students who have signed up but do not take the class in the end. This was also done, with satisfying results.</li><li>There was a request for better student assistant support. Given the financial constraints this was not possible. At times it was challenging to provide all groups the necessary support.</li></ul> In 2023, because the network building was deemed challenging we provided the student groups with a ready-made network to make sure that network analysis could be done on a well-constructed network. This worked well, in practice.

The course is designed as a Project-based learning / Active learning course, The students work together in small teams (max 5) to engage in building a biological system representation in the network editor Cytoscape.Most students experience this as very valuable and claim that they have learned a lot. There was one group in which the students had issues in working together in an efficient way, and that showed in the results. All groups consider the course as highly challenging, with a high workload, but fitting the learning objectives. This is documented in the survey, which is held at the end of the course (see uploaded BI3019 Course assessment 2023.pdf). In addition, at 1/3 of the course, a reference group meeting was held (see 'reference group feedback'). The work is graded based on a report, which is structured as a scientific publication (between 40 and 50 pages long). Overall most students did very well, but a minority scored a C or lower.

A survey was held after the course; the results are:<ul><li>The Theme Introductions could be improved</li><li>The learning outcome was very good</li><li>The project workload is high</li><li>The project complexity is challenging</li><li>Overall the course themes are challenging</li><li>The course had relevance for most MSc students</li><li>Whether the course needs more attention on various aspects can be assessed by scoring 'definitely'. This is only true for:</li><li>analysis methods</li><li>Cytoscape and Cytoscape Apps</li><li>Celldesigner</li><li>Standards</li><li>The project</li></ul>These observations have been reported every year, and despite various updates, the course remains 'challenging'. This is in fact precisely where the course needs to be.

NTNU:TIA:REST

Institutt for biologi

Department of Biology

Engelsk

Mer om vurdering

The course follows and active learning approach, in which students will work and learn together in small teams. Because the effectiveness of this study form depends on teams with sufficient critical mass there is an overall requirement to participate in at least 80% of the team sessions (28 sessions in total). Participation will be monitored through signing off on attendance sheets. Students who comply with the mandatory attendance will be graded on their individual report.In case of fail a revised version of the report can be submitted for a new assessment.In case you want to improve the grade for your report you will have to follow the course again and submit a new report.Course and Report language: English.

Anbefalte forkunnskaper

Molecular Biology, molecular cell biology, biotechnology. Basic knowledge of statistics will be required.

Faglig innhold

Systems Biology requires a multidisciplinary skill base, and in order to perform research using systems biology approaches one should be familiar with a wide variety of systems biology related concepts originating from (molecular) biology, computational data integration, network-based analysis, mathematics, control theory and knowledge bases. The course therefore provides the student with a broad overview of the main concepts necessary to understand and use systems biology approaches in research. It introduces the students to the history of systems biology, the relationship between systems biology and high throughput genomics; the variety of data types and databases that are available for building systems networks; the importance of data standards and the use of specific data formats; he importance of ontologies for description and analysis of biological data; the most important databases and software tools available on the web; knowledge resources, portals  organism specific resources; text mining and knowledge management; data integration approaches; graphs and networks; pathway-based analysis; the Cytoscape platform.

Kursmateriell

Scientific review papers, software tool manuals and tutorials, total of 500 pages.

Læringsutbytte

After completing the course the student will be able to:<ul><li>Describe the reasons why systems biology was proposed and introduced as a new research approach, the main theories and concepts that it is founded on, and explain why these concepts have been important to establish it as an integrative biological research approach.</li><li>Explain the fundamentals of graph-based, pathway-based, and computational model-based analysis and simulation. The student will be able to explain the value of text mining for information retrieval, and motivate the use of ontologies for data description and analysis, the use of standards, knowledge and data exchange formats for analysing experimental results, as well as the use of pathway-based analysis of omics data for setting up network-based analysis methods.</li></ul>After completing the course the student will be able to:<ul><li>Use a range of standards, resources and tools available on the internet and combine them into approaches appropriate for their biological research, and be able to use curated information resources, and annotate and add new information to a network from literature curated by the student</li><li>Find relevant background information related to a specific biological system or domain that they are working with.</li><li>Skillfully apply network-based Systems Biology knowledge integration and analysis methods for the interpretation of new experimental results.</li><li>Use a variety of software tools on the web to analyse experimental results/omics data in the context of available network and pathway knowledge, and use the results for network building.</li><li>Exploit the different functions of the Cytoscape platform and more than a dozen of its main Applications, in setting up the different steps of building a biological network.</li><li>Develop a comprehensive network-based systems biology approach for knowledge discovery to address specific biological questions, and use their knowledge to develop new research using systems approaches and design integrated analysis methods for interpreting their own results.</li></ul>After completing the course the student will be able to:<ul><li>Contribute to innovative systems biology approaches in new biological research fields, and collaborate well in multidisciplinary projects.</li><li>Report on systems biology studies and research findings and contribute to discussions about the applicability of new integration and analysis approaches in such projects.</li><li>Find relevant systems biology-related research papers, and be able to use the findings in order to consider and design innovative new systems biology approaches for new applications in this field.</li></ul>

Læringsformer og aktiviteter

The course follows an Active Learning approach in which students collaborate in small groups to discuss and work with tasks and questions, based on teacher guidance. The material for the course comes from recent scientific review papers describing systems biology concepts, tools, approaches and resources.A substantial part of the course is devoted to project-based learning in PC classes, in which the students will practice the use of tools, resources and data integration approaches to build a biological network, analyse it and use network-based approaches for integrated data and systems analysis. Using a gene list resembling an experimental outcome they build a network retrieving information from the web about the parts (genes, proteins) of biological networks, and use the Cytoscape platform for basic graph-based or pathway-based analysis of experimental data.The course will be relevant for molecular (cell) biology students and students from biotechnology and chemical engineering who are interested in systems biology.

Mandatory attendance

Systems Biology: Resources, standards and tools

Biomedisinsk teknikk

Biofysikk og medisinsk teknologi

Biofysikk

Bioteknologi/Molekylærgenetikk

Kjemisk prosessteknologi

Bioinformatikk

Biokjemi

Biologi

Bioteknologi

Kjemi

Biology (Master's Programme)

Environmental Toxicology and Chemistry (Master's Programme)

Industriell kjemi og bioteknologi - masterstudium (5-årig)

Biologi - doktorgrad (ph.d.)

Biofysikk - doktorgrad (ph.d.)

Bioteknologi - doktorgrad (ph.d.)

Kjemi - doktorgrad (ph.d.)

Kjemisk prosessteknologi - doktorgrad (ph.d.)

Materialteknologi - doktorgrad (ph.d.)

Bioteknologi (5-årig) - masterstudium

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Every year the students are asked to provide feedback both through a reference group, for early changes to the course, and through a survey at the end of the course, to refine the course for the next year. The changes suggested in 2019 related the clarity of the project objectives and timelines, the  workload, which was deemed too high, and to the challenges: the specific computational and analytical tasks that the students needed to to in the project. Because of this, the challenges were better organised and presented on a timeline, at the beginning of the project introduction. In general, more time was spent in the first lectures to clarify the purpose and content of the project. Also, an additional lecture was added to clarify the use of functional genomics data in the project analysis steps.

Tiltak

Course improvements based on Student Feedback

Report on the BI3019 course. In 2022, 30 students attended the course, 29 delivered a report. Course improvements based on Student Feedback:Student feedback is collected through a reference group and through a survey. The reference group feedback allows to add improvements early on during the course, the survey provides clarity about how the course was perceived overall, and it is used to plan improvements for the next issue of the course.  Relative to 2021, several modifications were introduced:<ol><li>The student groups were composed during the first class. Because of this, several groups of students who already knew each other were allowed to collaborate.</li><li>The amount of mandatory network building was lowered, to allow for more focus on analysis.</li></ol> Main reference group feedback for the 2022 course (attachment: Reference Group feedback 2022.pdf):<ul><li>Technical issues with tools can be dealt with better with a student assistant present in the class.</li><li>Not all students have the same background, some are 3rd year, should there be minimum requirements for prior courses?</li><li>It would help if the students would better understand the meaning of the challenges. Maybe this could be defined by way of ‘learning objectives’.</li></ul> In 2022 there was one student assistant less in the course, because of IBI’s financial constraints. Although within one course session all groups could still be assisted well, the increases waiting time led to some frustration among some students. The issue with the background that students should have, was also flagged. However, this problem was exacerbated by the fact that in one group, all students were in their third year. The question for more guidance about the connection between the project challenges and the learning objectives was taken up immediately by supplying the students with a detailed description of how these two are linked. Course improvements based on the survey (attachments: Course assessment 2022.pdf, BI3019 Course Survey.pdf). The following points stood out:<ul><li>The workload is high</li><li>The project is challenging</li><li>The course themes are challenging</li><li>Some more attention might be needed on systems biology background, the different themes, omics data sources, analysis methods, the project itself, simulation tools.</li></ul> As many of these points have already been taken into account earlier, observing these issues again may have to do with the fact that students for the first time were allowed to choose their own group. Maybe related to this, several groups turned out to be really struggling. Spreading the students randomly may be a better insurance for equally strong groups, where the students are better able to educate each other while working on the project. Nevertheless, the work on network building will be further reduced in 2023, so that more time is available for analysis and reporting.

Of the 30 students who started the course, 29 were able to submit a report. The average length of these reports was around 45 pages, written in the style of a scientific publication. From the material and methods section it was clear that most of the students had obtained a good understanding of the tools and resources available for doing systems biology analyses of experimental results. From the results and discussions it became clear that also a majority of students was able to synthesize interrelationships between different observations and use these to develop some level of systems understanding of the project's results.

As every year, the students were requested to submit a survey about their views on the course (see attach and above).

After completing the course the student will be able to:<ul><li>Describe the reasons why systems biology was proposed and introduced as a new research approach, the main theories and concepts that it is founded on, and explain why these concepts have been important to establish it as an integrative biological research approach.</li><li>Explain the fundamentals of graph-based, pathway-based, and computational model-based analysis and simulation. The student will be able to explain the value of text mining for information retrieval, and motivate the use of ontologies for data description and analysis, the use of standards, knowledge and data exchange formats for analysing experimental results, as well as the use of pathway-based analysis of omics data for setting up network-based analysis methods.</li></ul><ul></ul>After completing the course the student will be able to:<ul><li>Use a range of standards, resources and tools available on the internet and combine them into approaches appropriate for their biological research, and be able to use curated information resources, and annotate and add new information to a network from literature curated by the student</li><li>Find relevant background information related to a specific biological system or domain that they are working with.</li><li>Skillfully apply network-based Systems Biology knowledge integration and analysis methods for the interpretation of new experimental results.</li><li>Use a variety of software tools on the web to analyse experimental results/omics data in the context of available network and pathway knowledge, and use the results for network building.</li><li>Exploit the different functions of the Cytoscape platform and more than a dozen of its main Applications, in setting up the different steps of building a biological network.</li><li>Develop a comprehensive network-based systems biology approach for knowledge discovery to address specific biological questions, and use their knowledge to develop new research using systems approaches and design integrated analysis methods for interpreting their own results.</li></ul>After completing the course the student will be able to:<ul><li>Contribute to innovative systems biology approaches in new biological research fields, and collaborate well in multidisciplinary projects.</li><li>Report on systems biology studies and research findings and contribute to discussions about the applicability of new integration and analysis approaches in such projects.</li><li>Find relevant systems biology-related research papers, and be able to use the findings in order to consider and design innovative new systems biology approaches for new applications in this field.</li></ul>

Medical Technology (Master's Programme)

Kjemi - masterstudium

Industriell kjemi og bioteknologi - masterstudium (2-årig)

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Systems Biology requires a multidisciplinary skill base, and in order to perform research using systems biology approaches one should be familiar with a wide variety of systems biology related concepts originating from (molecular) biology, computational data integration, network-based analysis, mathematics, control theory and knowledge bases. The course therefore provides the student with a broad overview of the main concepts necessary to understand and use systems biology approaches in research. It introduces the students to the history of systems biology, the relationship between systems biology and high throughput genomics; the variety of data types and databases that are available for building systems networks; the importance of data standards and the use of specific data formats; he importance of ontologies for description and analysis of biological data; the most important databases and software tools available on the web; knowledge resources, portals  organism specific resources; text mining and knowledge management; data integration approaches; graphs and networks; pathway-based analysis; the Cytoscape platform.

After completing the course the student will be able to:<ul><li>Describe the reasons why systems biology was proposed and introduced as a new research approach, the main theories and concepts that it is founded on, and explain why these concepts have been important to establish it as an integrative biological research approach.</li><li>Explain the fundamentals of graph-based, pathway-based, and computational model-based analysis and simulation. The student will be able to explain the value of text mining for information retrieval, and motivate the use of ontologies for data description and analysis, the use of standards, knowledge and data exchange formats for analysing experimental results, as well as the use of pathway-based analysis of omics data for setting up network-based analysis methods.</li></ul>Ferdigheter:<ul><li>After completing the course the student will be able to:</li><li>Use a range of standards, resources and tools available on the internet and combine them into approaches appropriate for their biological research, and be able to use curated information resources, and annotate and add new information to a network from literature curated by the student</li><li>Find relevant background information related to a specific biological system or domain that they are working with.</li><li>Skillfully apply network-based Systems Biology knowledge integration and analysis methods for the interpretation of new experimental results.</li><li>Use a variety of software tools on the web to analyse experimental results/omics data in the context of available network and pathway knowledge, and use the results for network building.</li><li>Exploit the different functions of the Cytoscape platform and more than a dozen of its main Applications, in setting up the different steps of building a biological network.</li><li>Develop a comprehensive network-based systems biology approach for knowledge discovery to address specific biological questions, and use their knowledge to develop new research using systems approaches and design integrated analysis methods for interpreting their own results.</li></ul>Generell Kompetanse:After completing the course the student will be able to:<ul><li>Contribute to innovative systems biology approaches in new biological research fields, and collaborate well in multidisciplinary projects.</li><li>Report on systems biology studies and research findings and contribute to discussions about the applicability of new integration and analysis approaches in such projects.</li><li>Find relevant systems biology-related research papers, and be able to use the findings in order to consider and design innovative new systems biology approaches for new applications in this field.</li></ul>

The course follows and active learning approach, in which students will work and learn together in small teams. Because the effectiveness of this study form depends on teams with sufficient critical mass there is an overall requirement to participate in at least 80% of the team sessions (28 sessions in total). Participation will be monitored through signing off on attendance sheets. Students who comply with the mandatory attendance will be graded on their individual report.In case of fail a revised version of the report can be submitted for a new assessment.In case you want to improve the grade for your report you will have to follow the course again and submit a new report.Course and Report language: English.

Molecular Biology, molecular cell biology, biotechnology. Basic knowledge of statistics will be required. 

The course follows and active learning approach, in which students will work and learn together in small teams. Because the effectiveness of this study form depends on teams with sufficient critical mass there is an overall requirement to participate in at least 80% of the classes. Students who comply with this will be graded on their individual report. Course and Report language: English.   


 
Systems Biology requires a multidisciplinary skill base, and in order to perform research using systems biology approaches one should be familiar with a wide variety of systems biology related concepts originating from (molecular) biology, computational data integration, network-based analysis, mathematics, control theory and knowledge bases. The course therefore provides the student with a broad overview of the main concepts necessary to understand and use systems biology approaches in research. It introduces the students to the history of systems biology, the relationship between systems biology and high throughput genomics; the variety of data types and databases that are available for building systems networks; the importance of data standards and the use of specific data formats; he importance of ontologies for description and analysis of biological data; the most important databases and software tools available on the web; knowledge resources, portals  organism specific resources; text mining and knowledge management; data integration approaches; graphs and networks; pathway-based analysis; the Cytoscape platform. 

The course follows an Active Learning approach in which students collaborate in small groups to discuss and work with tasks and questions, based on teacher guidance. The material for the course comes from recent scientific review papers describing systems biology concepts, tools, approaches and resources. A substantial part of the course is devoted to project-based learning in PC classes, in which the students will practice the use of tools, resources and data integration approaches to build a biological network, analyse it and use network-based approaches for integrated data and systems analysis. Using a gene list resembling an experimental outcome they build a network retrieving information from the web about the parts (genes, proteins) of biological networks, and use the Cytoscape platform for basic graph-based or pathway-based analysis of experimental data. The course will be relevant for molecular (cell) biology students and students from biotechnology and chemical engineering who are interested in systems biology.

 

Kunnskap:
After completing the course the student will be able to:
 Describe the reasons why systems biology was proposed and introduced as a new research approach, the main theories and concepts that it is founded on, and explain why these concepts have been important to establish it as an integrative biological research approach.
 Explain the fundamentals of graph-based, pathway-based, and computational model-based analysis and simulation. The student will be able to explain the value of text mining for information retrieval, and motivate the use of ontologies for data description and analysis, the use of standards, knowledge and data exchange formats for analysing experimental results, as well as the use of pathway-based analysis of omics data for setting up network-based analysis methods.

Ferdigheter:
After completing the course the student will be able to:
 Use a range of standards, resources and tools available on the internet and combine them into approaches appropriate for their biological research, and be able to use curated information resources, and annotate and add new information to a network from literature curated by the student
 Find relevant background information related to a specific biological system or domain that they are working with. 
 Skillfully apply network-based Systems Biology knowledge integration and analysis methods for the interpretation of new experimental results.
 Use a variety of software tools on the web to analyse experimental results/omics data in the context of available network and pathway knowledge, and use the results for network building. 
 Exploit the different functions of the Cytoscape platform and more than a dozen of its main Applications, in setting up the different steps of building a biological network. 
 Develop a comprehensive network-based systems biology approach for knowledge discovery to address specific biological questions, and use their knowledge to develop new research using systems approaches and design integrated analysis methods for interpreting their own results.

Generell Kompetanse:
After completing the course the student will be able to:
 Contribute to innovative systems biology approaches in new biological research fields, and collaborate well in multidisciplinary projects. 
 Report on systems biology studies and research findings and contribute to discussions about the applicability of new integration and analysis approaches in such projects. 
 Find relevant systems biology-related research papers, and be able to use the findings in order to consider and design innovative new systems biology approaches for new applications in this field.



Kjemi - dr.grad

Cellebiologi for medisinsk/teknisk personell - masterstudium

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Systems Biology: Resources, standards and tools - BI3019