This course is an introduction to the theoretical tools that are used to understand the emerging behavior of complex biological networks. Systems Biology is a systemic approach to understand the biological phenomena that occurs inside a cell at the molecular level.
Slides used in classes
Most classes are done on a whiteboard, so you need to keep your own paper copy. Some of these classes are later transcribed into webpage and published here. When the class used slides, they are published here.
Example Jupyter code
- Pascal’s Triangle. [Web page]
- Probabilities in R. [Web page]
- Law of Large Numbers. [Web page]
- Testing the hypothesis of finger randomness. [Web page]
- Testing again with a different score function. [Web page]
- Testing two samples. Doing the same test many times. [Web page]
- The lady tasting tea: Using experimental methods to introduce inference statistics. (PDF).
- A lady tasting tea and other applications of Categorical Data Analysis. (PDF).
- You Can Load a Die, But You Cant Bias a Coin. (ResearchGate page).
- A Class Project in Survey Sampling. (ResearchGate page).
- Debunking the P-value with Statistics. (Backyard Brains website).
- The p value and the base rate fallacy. (Statistics Done Wrong website).
- Paired Sample T-Test. (Statistics Solutions website).
- Comparing Two Population Means: Paired Data. (Pennsylvania State University).
- Hypothesis Test: Difference Between Paired Means. (web page)
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (Bioconductor tutorial).
The forum of the course is at https://groups.google.com/d/forum/iu-systems-biology. You can also participate writing an email to email@example.com. Feel free to use it to ask any question or give any answer.
- From genotype to phenotype
- Behavior emerging from networks
- Which genes code for transcription factors?
- Which are their binding sites?
- How do they change gene expression?
- Few experimental results. Expensive
- How can we generalize them?
- Probability theory
- other approaches