Class 1.1: How?

Systems Biology

Andrés Aravena, PhD

October 01, 2021

Interaction networks

Unfortunately, we do not have time to study all kinds of networks relevant in molecular biology

(at least, not in this course)

We will focus on interaction networks

That is, networks that can be built from gene expression data

Gene expression

In other words we will speak about


We will learn to analyze gene expression, so we can design better experiments and achieve higher impact

Big plan

This course has basically three parts

  • Finding differentially expressed genes
    • evaluating differential expression
    • using pre-normalized data
    • statistical tests
    • linear models
  • Building interaction networks
  • Normalizing gene expression data

Time to choose our schedule

Please fill this doodle

Measuring Gene Expression

More precisely, mRNA concentration

What is the question?

We want to know

  • Which genes are being expressed
  • How much of each gene is being expressed
  • How does expression change
    • In time
    • Under different conditions
    • Between strains/mutants/cell lines

The Big Assumption

Measuring protein concentration is hard

We assume that protein concentration is proportional to mRNA concentration

  • Which genes are being transcribed
  • How much of each gene is being transcribed
  • How does transcription change
    • In time
    • Under different conditions
    • Between strains/mutants/cell lines

How to measure mRNA concentration?


  • qPCR
  • Microarrays
  • RNAseq


If you have primers for each gene

  • specific to each gene
  • thermodynamically stable
  • efficient

Raw data: CT value for each gene/condition
and CT value for calibration reference

Hybridization methods

Southern/Northern/Western blot can detect, but not quantify
(I think so. I’m not a biologist)

Instead, we have macro- and microarrays

Raw data: Light intensity (luminescence) in one or more wave length

This is measured in arbitrary units, and is a number between 0 and 65536
(that is, a 16-bits value)


mRNA is retro-transcribed and fragmented.
Fragments are sequenced. Reads are aligned to reference genome

Raw data: SAM/BAM file with location of each read in the reference genome

Processed data: Number of reads per gene, normalized by gene length

Data source: NCBI GEO

Gene Expression Omnibus

  • Platforms
  • Samples
  • Series
  • Data Set
  • Profile

Relevant Objects in GEO

GEO Platform
Set of probes used in one or more experiment. Type of microarray slide, qPCR primers, including controls.
GEO Samples
a specific result of a single experiment. Raw RNA concentration for each probe in the platform
GEO Series
Set of Samples from a complete experiment. Includes technical and biological replicas

Relevant Objects in GEO

GEO Datasets
Sets of samples from different experiments that can be compared. For example, using the same platform
GEO Profiles
individual gene expression profiles assembled from GEO. Follows a single gene through several conditions

NCBI GEO data structure


Let’s takea look at


Types of files

NCBI standard

  • SOFT
  • MINiML
  • Series Matrix

Industry standard

  • CEL (Affymetrix)
  • GPR
  • SAM/BAM (RNAseq)

Home exercise

These are optional, try at least one.

  1. Learn how to read these files in your computer
    • They are usually compressed
    • Do not use Word
    • If you use Excel, be careful
  2. Learn how to get data from the European Database
  3. Bring your own data

Write a document (in English) explaining your results