Online courses

The course is a special topics class for biology students in their late undergraduates/graduate studies emphasizing hands-on learning of different computational methods in virus ecology and evolution. As part of the course, students will learn Python programming. The ultimate goal of the course is to seed interest and make students capable of pursuing computational methods in (virus) ecology and evolution in their further studies/careers.

UiB students have basic math background from a single semester-course (MAT101), covering introduction to single-variable functions (polynomial, trigonometric and exponential functions), limits, single-variable calculus, simple differential equations, basic linear algebra, and construction of simple mathematical models. UiB students will also have completed a single semester-course in introduction to programming (INF100).

Module 1 – Biological background & Introduction to computational methods - Virus ecology and evolution

Module 2 – Programming in Python - Introduction to Unix and Python programming  & Reproducibility in data science and discussion of modeling approaches

Module 3 – Differential equation-based modeling - Introduction to differential equation-based modeling

Module 4 – Individual-based modeling - Introduction to individual-based modeling of virus - host communities

Module 5 – Bioinformatics - Introduction to bioinformatics and phylogenetic trees

Module 1 – Broad understanding of ecological and evolutionary significance of viruses and of computational methods in the field.

• • • Students will know about different roles that viruses play in ecological and evolutionary contexts, including their effects on cycling of matter in microbial food webs and factors driving evolution in virus-host systems
    • Students will learn how to manage reproducibility in data science and understand the use of different modeling techniques in (viral) ecology and evolution, specifically differential equation-based vs individual-based modeling

Module 2 – Capability to program in Python

• • • Familiarity with a Linux-based operating system
    • Proficiency in the implementation of Python programs of significant size and complexity using core and third-party libraries and data types

Module 3 – Understand dynamic modeling and data – model coupling

• • • Knowledge how to solve a simple population growth model (Malthusian growth / logistic) analytically and with numerical integration
    • Visualization of solutions to population growth models in a Python environment, compare model solutions with laboratory growth data (e.g. for algae growing in batch culture) and manually test which parameters fit the data well.
    • Understand the concept of coupling in the context of a microbial ecosystem, by simulating predator-prey (virus-host) interactions in a Python environment, and comparing model solutions with laboratory and environmental data

Module 4 – Design of individual-based ecological/evolutionary models

• • • Students will be able to construct and run individual-based evolutionary algorithms into the field of virus ecology and evolution using Python

Module 5 – Familiarity with building bioinformatics pipelines and construction of phylogenetic trees

• • • Students will learn how to put together a (simple) bioinformatics pipeline in the form of a python script and know the basic principles on how to make a phylogenetic tree based on genomic data. They will furthermore have basic understanding of the main types of algorithms for reconstruction of phylogenetic trees (based on molecular data) and will be able to interpret a phylogenetic tree (in various contexts)

• Time frame
  • Course material will be made available to students online in March 2023 and course final will be (as planned physical seminar) in June 2023
• Workload
  • The course encompasses roughly 220 hours of workload in total including lecture time, tutorials, Q&A etc. Spread over 15 weeks between March and June, weekly workload should be around 15 hours. This will give students who pass 10 ETCS credits.
• Teaching platform
  • We will use Canvas as teaching platform, where students can work through modules independently. UiB external teachers as well as students will be able to use the UiB Canvas by using an external link to log-on.
• Course material
  • Each module will have recorded lectures/instruction videos, background literature if applicable and sets of tutorials made available through Canvas, as well as online discussion sessions and/or chat platforms for more direct interactions.
• Evaluation

• Completed tutorials, students are free to work in groups.
• Final group project in topic of either module 3, module 4, or module 5; Proposal for final project must be submitted before midterm and final project has to be presented orally at the end of the semester (June 2023).
• Pass/Fail grading

• Class size
  • Minimum 2 students, maximum 12 students, UiB students prioritized