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.
What people say about this course:
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 email@example.com.
All quizzes and homework should be sent to firstname.lastname@example.org 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).
2 (Deadline: Friday 1 of March at 9:00).
Write functions to draw a house, a person, a pentagon, and a polygon.
3 (Deadline: Friday 8 of March at 9:00).
We want to draw one or more stick-people. Prepare a function for that.
4 (Deadline: Friday 15 of March at 9:00).
Count rabbits, reverse strands, and find the origin.
5 (Deadline: Friday 22 of March at 9:00).
Algorithm design for half values. Drawing recursive trees.
6 (Deadline: Friday 29 of March at 9:00).
This town is full of rats
- Comment on Homework 5
7 (Deadline: Wednesday 10 of April at
Practice on algorithm design
8 (Deadline: Friday 26 of April at 9:00).
Practice of plots, chaos, and randomness
9 (Deadline: Friday 3 of May at 9:00).
Estimating the frequency of epilepsy and double birthdays.
10 (Deadline: Friday 10 of May at 9:00).
Shall you take the umbrella today?
11 (Deadline: Friday 17 of May at 9:00).
Practice for the exam
Homework 1 (Deadline: Exam day).
More practice for the exam
- Exercises for
Makeup (Deadline: Makeup exam day).
More practice for the makeup exam.
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. (Feb 15, 2019).
Motivation of the course. Programing is as easy as LEGO.[Slides].
Turtle Graphics. (Feb 22, 2019).
From Scratch to R. Computational thinking. Patterns. Loops to repeat, functions to reuse. How to make your own functions.[triangle1.R], [triangle_loop.R], [triangle_loop2.R], [Slides].
Decomposition, Patterns, Abstraction, Algorithms. (Mar 1, 2019).
Functions: a key element of Computational Thinking. Step by Step using RStudio Debugger. Opening the “black box”. Breakpoints, Environment, Traceback.[class3-1.R], [class3-2.R], [class3-3.R], [class3-4.R], [class3-5.R], [Slides].
Analiz, Orüntü, Soyutlama, Algoritmalar. (Mar 1, 2019).
Fonksiyonlar: Sayisal düsünmenin bir temel tasidir.Adim adim RStudio’da hata ayiklayici programi kullanmak.[Slides].
Finding Patterns in Nature and Genetics. (Mar 8, 2019).
Patterns in Nature, Art and Science. Recursive patterns and functions. Exit conditions. if-then-else. Figures from DNA. FASTA files and how to get them. Global and local DNA statistics. GC-content, GC-skew.[class4-1.R], [class4-2.R], [class4-3.R], [Slides].
Cumulative sums. (Mar 15, 2019).
Introduction to Systems Theory.[class5-1.R], [class5-2.R], [Slides].
Systems in Biology and Beyond. (Mar 22, 2019).
We can describe systems as parts and interactions, and simulate their emergent behavior.[class6-1.R], [class6-2.R], [Slides].
Practice on Dynamical Systems. (Mar 29, 2019).
Prepare for the midterm exam.[class7-1.R], [class7-2.R], [class7-3.R], [Slides].
What can we do with systems?. (Apr 19, 2019).
Can we Predict the Future? Deterministic and Non-deterministic Systems. Chaos and randomness.[Slides].
Probabilities. (Apr 26, 2019).
Basic definitions and concepts. How to see the invisible.[Slides].
Sample Average and Population Average. (May 3, 2019).
Law of Large Numbers. Central Limit Theorem.[Slides].
Average, Variance and Standard Deviation. (May 10, 2019).
Normal and Student’s Distributions.[Slides].
Practice for the Exam. (May 17, 2019).
Review of Homework.[Slides].
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
- How to read an R help page
- Getting Started with R
- Free Course: Introduction to R
- Introduction to Data Science
- Book R for Data Science
- Book Data Visualization: A practical introduction by Kieran Healy, Duke University
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.