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

1862

Louis Pasteur:Microorganisms responsible for contamination, heating kills microorganisms

van Leeuwenhoek: described single celled organisms

1676 1866

Gregor Mendel:Phenotype determined by inheritable units

1735

Carl Linnaeus:Hierarchical classification of species

1859

Charles Darwin:“The Origin of Species”

1944

Avery, MacLeod, McCarty: DNA is the genetic material

1953

James Watson Francis Crick: solve structure of DNA

Frederick Sanger: Complete sequence of insulin

1955

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

Human genome sequence “completed”, Feb 2001

PubMed abstracts indicate a recent interest in Systems Biology

Human genome completed

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

Huber W, et al., Bioinformatics 2006

Functional genomics using gene knockout libraries for yeast

similar RNAi libraries in other systems

Replacement of yeast ORFs with kanMX gene flanked by unique oligo barcodes- “Yeast Deletion Project Consortium”

Systematic phenotyping

yfg1 yfg2 yfg3

CTAACTC TCGCGCA TCATAATBarcode

(UPTAG):

DeletionStrain:

Growth 6hrsin minimal media

(how many doublings?)

Rich media

…

Harvest and label genomic DNA

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

John Yates labhttp://fields.scripps.edu/mudpit/index.html

Isotope coded affinity tags (ICAT)

Biotin Biotin tagtag

Linker (d0 or d8)Linker (d0 or d8) Thiol specific Thiol specific reactive groupreactive group

ICATICAT ReagentsReagents:: Heavy reagent: d8-ICATHeavy reagent: d8-ICAT ((XX=deuterium)=deuterium)Normal reagent: d0-ICAT (Normal reagent: d0-ICAT (XX=hydrogen)=hydrogen)

S

N N

O

N OO

O N IO OXX

XX

XX

XX

XX

XX

XX

XX

Mass spec based method for measuring relative protein abundances between two samples

Ruedi Aebersoldhttp://www.imsb.ethz.ch/researchgroup/aebersold

Combine and proteolyze(trypsin))

Affinity separation

(avidin)

ICAT-labeled

cysteines

550550 560560 570570 580580m/zm/z

00

100100

200200 400400 600600 800800m/z

00

100100

NHNH22-EACDPLR--EACDPLR-COOHCOOH

Light Heavy

Mixture 2

Mixture 1

Protein quantification & identification via ICAT strategy

Quantitation

ICAT Flash animation:http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/ICAT/ICAT.html

ExampleYeast grown in ethanol vs galactose media were monitored with ICAT

Adh1 vs. Adh2 ratios are shown below…

Comparing mRNA levels to protein levels

Protein-protein interaction data• Physical Interactions

– Yeast two hybrid screens– Affinity purification (mass

spec)– Peptide arrays– Protein-DNA by chIP-chip

• Other measures of ‘association’– Genetic interactions (double

deletion mutants)

– Genomic context (STRING)

Yeast two-hybrid method

Y2H assays interactions in vivo.

Uses property that transcription factors generally have separable transcriptional activation (AD) and DNA binding (DBD) domains.

A functional transcription factor can be created if a separately expressed AD can be made to interact with a DBD.

A protein ‘bait’ B is fused to a DBD and screened against a library of protein “preys”, each fused to a AD.

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

Simon et al., Cell 2001

Lee et al., Science 2002

Mapping transcription factor binding sites

Harbison C., Gordon B., et al. Nature 2004

Dynamic role of transcription factors

Harbison C., Gordon B., et al. Nature 2004

Exercise: Y2H

Construct a protein-protein interaction network for proteins A,B,C,D

Systems biology and emerging properties

Can a biologist fix a radio?

Lazebnik, Cancer Cell, 2002

Building models from parts lists

Protein-DNAinteractions

Gene levels(up/down)

Protein-proteininteractions

Protein levels(present/absent)

Biochemicalreactions

Biochemicallevels

▲ Chromatin IP ▼ DNA microarray

▲ Protein coIP▼ Mass spectrometry

▲noneMetabolic flux ▼

measurements

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

1. 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?). 

2. 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

3. 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.

4. 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

Aebersold R, Mann M., Nature, 2003.

Continuum of modeling approaches

Top-down Bottom-up

Need computational tools able to distill pathways of interest from large molecular interaction databases

(top-down)

Data integration and statistical mining

List of genes implicated in an experiment

• What do we make of such a result?

Jelinsky S & Samson LD,Proc. Natl. Acad. Sci. USAVol. 96, pp. 1486–1491,1999

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

Mapping the phenotypic data to the network

Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

•Systematic phenotyping of 1615 gene knockout strains in yeast•Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents)•Screening against a network of 12,232 protein interactions

Mapping the phenotypic data to the network

Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

Mapping the phenotypic data to the network

Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

Network models can be

predictive

Green nodes represent proteins identified as being required for MMS resistance; gray nodes were not tested as part of the 1615 strains used in this study; blue lines represent protein-protein interactions.

The untested gene deletion strains (ylr423c, hda1, and hpr5) were subsequently tested for MMS sensitivity; all were found to be sensitive (bottom).

Begley TJ, Rosenbach AS, Ideker T, Samson LD. Damage recovery pathways in Saccharomyces cerevisiae revealed by genomic phenotyping and interactome mapping. Mol Cancer Res. 2002 Dec;1(2):103-12.

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