Introduction to Systems Biology

Introduction to Systems Biology

Overview of the day

• Background & Introduction

• Network analysis methods

• Case studies

• Exercises

Why Systems Biology?

• …and why now?

Timeline of discovery

Frederick Sanger

• In 1975, he developed the chain termination method of DNA sequencing, also known as the Dideoxy termination method or the Sanger method. Two years later he used his technique to successfully sequence the genome of the Phage Φ-X174; the first fully sequenced genome. This earned him a Nobel Prize in Chemistry (1980) (his second)

• Sanger earned his first Nobel prize in Chemistry (1958) for determining the complete amino acid sequence of insulin in 1955. Concluded that insulin had a precise amino acid sequence.

The genomic era

PubMed abstracts indicate a recent interest in Systems Biology

Functional genomics

• Study of Genomes is called “Genomics”

• Genomics led to Functional Genomics which aims to characterize and determine the function of biomolecules (mainly proteins), often by the use of high-throughput technologies.

• Today, people talk about:

• Genomics
• Transcriptomics
• Proteomics
• Metabolomics
• [Anything]omics

High-throughput applications of microarrays

• Gene expression

• De novo DNA sequencing (short)

• DNA re-sequencing (relative to reference)

• SNP analysis

• Competitive growth assays

• ChIP-chip (interaction data)

• Array CGH

• Whole genome tiling arrays

Tiling microarrays

Functional genomics using gene knockout libraries for yeast

Systematic phenotyping

Systematic phenotyping with a barcode array (Ron Davis and others)

• These oligo barcodes are also spotted on a DNA microarray

• Growth time in minimal media:

• Red: 0 hours
• Green: 6 hours

Mass spectrometry

• Peptide identification

• Relative peptide levels

• Protein-protein interactions (complexes)

• Post-translational modifications

• Many many technologies

MudPIT (Multidimensional Protein Identification Technology)

• MudPIT describes the process of digesting, separating, and identifying the components of samples consisting of thousands of proteins.

• Separates peptides by 2D liquid chromatography (cation-exchange followed by reversed phase liquid chromotography)

• LC interfaced directly with the ion source (microelectrospray) of a mass spectrometer

Example

Comparing mRNA levels to protein levels

Protein-protein interaction data

Yeast two-hybrid method

Issues with Y2H

• Strengths

• High sensitivity (transient & permanent PPIs)
• Takes place in vivo
• Independent of endogenous expression

• Weaknesses: False positive interactions

• Auto-activation
• ‘sticky’ prey
• Detects “possible interactions” that may not take place under real physiological conditions
• May identify indirect interactions (A-C-B)

• Weaknesses: False negatives interactions

• Similar studies often reveal very different sets of interacting proteins (i.e. False negatives)
• May miss PPIs that require other factors to be present (e.g. ligands, proteins, PTMs)

Protein-DNA interactions: ChIP-chip

Mapping transcription factor binding sites

Dynamic role of transcription factors

Exercise: Y2H

Systems biology and emerging properties

Can a biologist fix a radio?

Building models from parts lists

Mathematical abstraction of biochemistry

Metabolic models

“Genome scale” metabolic models

• Genes 708

• Metabolites 584

• Cytosolic 559
• Mitochondrial 164
• Extracellular 121

• Reactions 1175

• Cytosolic 702
• Mitochondrial 124
• Exchange fluxes 349
• Forster et al. Genome Research 2003.

One framework for Systems Biology

• The components. Discover all of the genes in the genome and the subset of genes, proteins, and other small molecules constituting the pathway of interest. If possible, define an initial model of the molecular interactions governing pathway function (how?). 

• Pathway perturbation. Perturb each pathway component through a series of genetic or environmental manipulations. Detect and quantify the corresponding global cellular response to each perturbation.

One framework for Systems Biology

• Model Reconciliation. Integrate the observed mRNA and protein responses with the current, pathway-specific model and with the global network of protein-protein, protein-DNA, and other known physical interactions.

• Model verification/expansion. Formulate new hypotheses to explain observations not predicted by the model. Design additional perturbation experiments to test these and iteratively repeat steps (2), (3), and (4).

From model to experiment and back again

Systems biology paradigm

Data integration and statistical mining

List of genes implicated in an experiment

• What do we make of such a result?

Types of information to integrate

• Data that determine the network

• (nodes and edges)

• protein-protein
• protein-DNA, etc…

• Data that determine the state of the system

• mRNA expression data
• Protein modifications
• Protein levels
• Growth phenotype
• Dynamics over time

Network models can be predictive

Summary

• Systems biology can be either top-down or bottom-up

• We are now in the post genomic era (don’t ignore that)

• Systematic measurements of all transcripts, proteins, and protein interactions enable top-down modeling

• Metabolic models, built bottom-up, are being refined with genomic information

• Data – Model – Predictions – Data: cycle as a Systems Biology theme