This course is an introduction to Computational Thinking. We will use the tools we learned in the previous course and apply them to model and simulate scientific experiments as a way to understand them.
Molecular Biology is going around computing and informatics in these days. Obtaining data is easy and cheap, processing data is hard and expensive to do and learn. A molecular biologist have to be able to understand what he/she produced. Otherwise he/she is a pipetting robot. Informatics and computing is what you have to learn, work harder.
Computing in Molecular Biology course was really hard to understand for us. In first year I failed with FF, then next year I passed with AA. I was able to pass when I understand the purpose of the computational methods, how important they are and how can we use it in molecular biology.
The course content and problems are very educational for beginner students but the main problem is that they have no perspective about computational sciences they have just thinking passing exam and move on. Everything about the course is depends on students behaviour and I think the lecturer makes a great effort for teaching so if students do not want to learn they will lose themselves.
This course was so interesting for me. I really didn’t like computers and I didn’t know anything about course and programing skills. This course was so much useful for me. I could understand your expression and your body language, but it was so fast for me because everything was new and hard for me, so understanding was hard.
Tabi ki bu dersleri seçerken en önemli etken hepimizin bildiği bir gerçek, moleküler biyolojide bilgisayarın önemidir. Yine bildiğimiz gibi, yaptığımız deneyler, elde ettiğimiz veriler , düzgün bir şekilde analiz edilip anlamlı bir çıkarıma dönüştürülmediği sürece hiçbir önem arz etmemektedirler. Şimdi elbette tercih sizlerin ancak bu derslerin bizler için çok önemli olduğunu göz önüne alarak ve tahmin ettiğim üzere okulda dolaşan korkulu senaryoları bir kenara bırakarak, karar vermeniz sizlerin yararınıza olacaktır
This page will be updated during the semester. Please check it regularly.
The forum of the course is at https://groups.google.com/d/forum/iu-cmb. You can also participate writing an email to firstname.lastname@example.org.
All quizzes and homework should be sent to email@example.com before the deadline to get a grade. Please be careful, otherwise you will get a grade zero.
- Homework 1 (Deadline: Friday 22 of February at 9:00).
Here you find the slides that have been used in classes. Notice that usually they are not published immediately, so you better take good notes. We recommend taking notes with pen and paper using the Cornell Method.
Introduction to Scientific Computing. Motivation of the course [Slides]. (Feb 15, 2019).
- Turtle Graphics. From Scratch to R
- Decomposition, Patterns, Abstraction, Algorithms. Functions: a key element of Computational Thinking
- Step by Step into Functions of Functions. Using RStudio Debugger, and something about recursive functions
- Drawing genomic data.
- Genomic data. Working with DNA sequences.
- Local and Global Statistics on DNA. Finding the Origin of Replication
- Cumulative sums. Introduction to Systems Theory.
- Dynamic Systems. Making water is like growing cells.
- Can we Predict the Future?. Deterministic and Non-deterministic Systems. Chaos and randomness.
- Probabilities. Basic definitions and concepts.
- Probability distributions.
- Events. Confidence intervals
- Practical Simulations. Is this variation caused by chance or something else?
- Experiment Design. Simulating to plan a DNA sequencing project
- More Simulations. Defining p-value and Run Length
- Genetic algorithms. Solving hard questions using Nature’s ideas.
- Practicing Genetic algorithms. Solving hard problems step by step
Summary of the course. The big picture. Why we did all of this
By regulation from the Rectory, students need to attend at least 70% of the classes. The attendance book is updated every week and can be seen in Google Sheets.
Some Free Online Resources about R
Polya, G. and Conway, John H. How to Solve It: A New Aspect of Mathematical Method. Princeton Science Library.
Wilson et al. “Best Practices for Scientific Computing.” PLoS Biology 12,1 (2014).
Stefan et al. “The Quantitative Methods Boot Camp: Teaching Quantitative Thinking and Computing Skills to Graduate Students in the Life Sciences”. PLoS Comput. Biol. 11, 1–12 (2015).
Elson D, Chargaff E (1952). On the deoxyribonucleic acid content of sea urchin gametes. Experientia 8 (4): 143–145.
Chargaff E, Lipshitz R, Green C (1952). Composition of the deoxypentose nucleic acids of four genera of sea-urchin. J Biol Chem 195 (1): 155–160.
Roten C-AH, Gamba P, Barblan J-L, Karamata D. Comparative Genometrics (CG): a database dedicated to biometric comparisons of whole genomes. Nucleic Acids Research. 2002;30(1):142-144.
Zeeberg, Barry R, Joseph Riss, David W Kane, Kimberly J Bussey, Edward Uchio, W Marston Linehan, J Carl Barrett, and John N Weinstein. Mistaken Identifiers: Gene Name Errors Can Be Introduced Inadvertently When Using Excel in Bioinformatics. BMC Bioinformatics 5 (2004): 80. doi:10.1186/1471-2105-5-80.