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.

The schedule is Tuesdays at 11:00 and Fridays at 15:00 via Zoom.

We have finished the first part of the course, about *gene
expression* analysis using *linear models*. Now we will study
*biological networks*. A good overview of the subject is
available on the following paper. Please read it before the next
class.

Albert, Réka. “Network Inference, Analysis, and Modeling in Systems Biology.” The Plant Cell 19, no. 11 (2007): 3327–38. https://doi.org/10.1105/tpc.107.054700.

# Slides used in classes

**Class 1.1: How?.***(Oct 1, 2021).*An introduction and motivation of the course**[Slides]**.**Class 1: Why Systems Biology?.***(Oct 1, 2021).*An introduction and motivation of the course**[Video]**,**[Slides]**.**Class 1.2: Why do we need theory?.***(Oct 1, 2021).*Shall we focus on “how to calculate”? It is better to learn*why*we calculate and*when not to do so*.**[Slides]**.**Class 2: Understanding the problem.***(Oct 5, 2021).*To give a correct answer we first need to understand the question.**[Video]**,**[Slides]**.**Class 3: Simple linear models.***(Oct 8, 2021).*An easy way to get some insight on the data.**[Video]**,**[Slides]**.**Class 4: Practical examples.***(Oct 12, 2021).*Analysis of qPCR results using R.**[Video]**,**[Slides]**.**Class 5: Practical examples in Excel.***(Oct 15, 2021).*Doing linear models on a spreadsheet. Also works on Google Sheets.**[Video]**,**[Slides]**.**Class 6: Sample v/s population.***(Oct 19, 2021).*There is a reality that we cannot see directly. We can only see shadows. How can we get an idea of what reality is?**[Video]**,**[Slides]**.**Class 7: Linear Models for Micro-arrays.***(Oct 22, 2021).*Limma**[Video]**,**[Slides]**.**Class 9: Linear models… again.***(Oct 26, 2021).*More details about contrasts and modelling. Power. Log ratios.**[Video]**,**[Slides]**.**Class 8: Last bits of Linear Models.***(Oct 26, 2021).*Miscellanea of details that have not yet been clearly explained**[Video]**,**[Slides]**.**Class 10: Introduction to Biological networks.***(Nov 5, 2021).*They are everywhere.**[Slides]**.**Class 11: qPCR Normalization.***(Nov 23, 2021).*The 2-delta-delta method.**[Video]**,**[Slides]**.**Class 12: Drawing biological graphs.***(Nov 23, 2021).*A tool for understanding.**[Video]**,**[Slides]**.**Class 13: Matrix Representation of Graphs.***(Dec 3, 2021).*This course seems to deal with matrics and graphs. But, in fact, they are two sides of the same coin.**[Video]**,**[Slides]**.**Class 14: Interaction Graphs.***(Dec 7, 2021).*How to build an adjacency matrix from gene expression data.**[Video]**,**[Slides]**.**Class 15: Practice with Interaction Graphs.***(Dec 14, 2021).*Using limma and graphical lasso to find interaction networks from gene expression data**[Video]**,**[Slides]**.**Class 16: Analyzing Two-color Microarrays.***(Dec 17, 2021).*

**[Video]**,**[Slides]**. + **Class 17: Normalization
in Arrays.** *(Dec 21, 2021).* A practical session.
**[Video]**,**[Slides]**. + **Class 19: Normalization
in RNAseq.** *(Dec 28, 2021).* Last class. **[Slides]**.

# Online material

## Essential

- 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. https://f1000research.com/articles/5-1408/v3. - 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)

## Suggested, but not Essential

- 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