Systems Biology is a systemic approach to understand the biological phenomena that occurs inside a cell at the molecular level. This course is an introduction to the theoretical tools that are used to understand the emerging behavior of complex biological networks.

This page will be updated during the semester. Please check it regularly. It was last updated on November 23 at 14:02.

# Attendance

By regulation from the Rectory, students need to attend at least 70%
of the classes. If you cannot attend, you **must** deliver
**all** homework *on time*. Late submissions will
not be accepted.

The attendance book is updated every week and can be seen in Google Sheets.

# Slides used in classes

**Class 1: Why Systems Biology?.***(Oct 5, 2023).***[Slides]**.

Introduction and motivation to the course**Class 2: Understanding the problem.***(Oct 12, 2023).***[Slides]**.

To give a correct answer we first need to understand the question.**Class 3: Simple linear models.***(Oct 19, 2023).***[Slides]**.

An easy way to get some insight on the data.**Class 4: qPCR.***(Oct 26, 2023).***[Slides]**.

Analysis of qPCR results using R.**Class 5: Practice with microarrays.***(Nov 2, 2023).***[Slides]**.

Analysis of microarray results using R and GEO.**Class 6: ANOVA.***(Nov 9, 2023).***[Slides]**.

Analysis of microarray results using R and GEO.**Class 7: Multiple tests.***(Nov 16, 2023).***[Slides]**.

The Birthday paradox.**Class 7a: Why do we need theory?.***(Nov 16, 2023).***[Slides]**.

Shall we focus on “how to calculate”? It is better to learn*why*we calculate and*when not to do so*.**Class 8: Essential calculus.***(Nov 23, 2023).***[Slides]**.

A superpower.**Class 8: Enter the matrix.***(Nov 23, 2023).***[Slides]**.

A superpower.

# Homework

**Homework 2***(Deadline: Thursday 2 of November at 15:00).*

Calculate p-values on simulated data. ANOVA and t Student test.

# Online material

- Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, Ritchie ME
(2018). “RNA-seq analysis is easy as 1-2-3 with limma, Glimma
and edgeR.”
*F1000Research*, 5, 1408. - A guide to creating design matrices for gene expression
experiments doi:10.18129/B9.bioc.RNAseq123
- R package that supports the F1000Research workflow article on RNA-seq analysis using limma, Glimma and edgeR by Law et al. (2016).

- The Art of Linear Algebra – Graphic Notes on “Linear Algebra for Everyone.” (PDF), (GitHub source), (Blog Entry).
- Hypothesis Test: Difference Between Paired Means. (web page)

- The
*p*value and the base rate fallacy. (Statistics Done Wrong website). - 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). - Paired Sample T-Test. (Statistics Solutions website).
- Comparing Two Population Means: Paired Data. (Pennsylvania State University).
- RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (Bioconductor tutorial).

# Bibliography

- No Bullshit guide to Linear Algebra by Ivan Savov (Website), (PDF extract).
- OpenIntro Statistics (Fourth Edition) by David Diez, Mine
Çetinkaya-Rundelm, and Christopher D. Barr (Free PDF
available)
*(there are other good free books in the same website)*.

# Contact

The forum of the course is at https://groups.google.com/d/forum/iu-systems-biology. You can also participate writing an email to iu-systems-biology@googlegroups.com. Feel free to use it to ask any question or give any answer>.

# Topics to be discussed

- Gene expression analysis
- qPCR, micro arrays, RNA-seq
- Public databases

- Statistical analysis of differential expression
- Normalization
- 2-delta-delta
- RMA
- TMM

- Network inference
- Gaussian graphs

- Causality