Introduction to Systems
Biology
Overview of the day
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Background & Introduction
Network analysis methods
Case studies
Exercises
Why Systems Biology?
…and why now?
Timeline of discovery
van Leeuwenhoek:
described single
celled organisms
Charles Darwin:
“The Origin of
Species”
1676
Gregor Mendel:
Phenotype
determined by
inheritable units
James Watson
Francis Crick:
solve structure
of DNA
1953
1859 1866
1735
Carl Linnaeus:
Hierarchical
classification of
species
1862
Louis Pasteur:
Microorganisms
responsible for
contamination,
heating kills
microorganisms
1944 1955
Avery,
MacLeod,
McCarty:
DNA is the
genetic
material
Frederick
Sanger:
Complete
sequence
of insulin
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:
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–
–
–
–
Genomics
Transcriptomics
Proteomics
Metabolomics
[Anything]omics
High-throughput applications of
microarrays
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•
•
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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
Replacement of
yeast ORFs with
kanMX gene
flanked by unique
oligo barcodes“Yeast Deletion
Project Consortium”
similar RNAi libraries in other systems
Systematic phenotyping
Barcode
CTAACTC
(UPTAG):
Deletion
Strain:
TCGCGCA
TCATAAT
yfg2D
yfg3D
yfg1D
Rich media
…
Growth 6hrs
in minimal media
(how many doublings?)
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 lab
http://fields.scripps.edu/mudpit/index.html
Isotope coded affinity tags (ICAT)
Mass spec based method for measuring relative protein
abundances between two samples
ICAT Reagents: Heavy reagent: d8-ICAT (X=deuterium)
Normal reagent: d0-ICAT (X=hydrogen)
O
N
N
O
XX
N
S
Biotin
tag
XX
O
O
O
XX
O
XX
Linker (d0 or d8)
Ruedi Aebersold
http://www.imsb.ethz.ch/researchgroup/aebersold
N
I
Thiol specific
reactive group
Protein quantification & identification
via ICAT strategy
100
Mixture 1
Light
0
550
560
Heavy
570
580
m/z
ICATlabeled
cysteines
Quantitation
100
NH2-EACDPLR-COOH
Mixture 2
Combine and
proteolyze
(trypsin)
Affinity
separation
(avidin)
0
200
ICAT Flash animation:
http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/ICAT/ICAT.html
400
600
m/z
800
Example
Yeast 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
Lee et al., Science 2002
Simon et al., Cell 2001
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-DNA
interactions
▲ Chromatin IP
▼ DNA microarray
Gene levels
(up/down)
Protein-protein
interactions
▲ Protein coIP
▼ Mass spectrometry
Protein levels
(present/absent)
Biochemical
reactions
▲none
Metabolic flux ▼
measurements
Biochemical
levels
Mathematical abstraction of
biochemistry
Metabolic models
“Genome scale” metabolic models
• Genes
• Metabolites
– Cytosolic
– Mitochondrial
– Extracellular
708
584
559
164
121
• Reactions
– Cytosolic
– Mitochondrial
– Exchange fluxes
1175
702
124
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, pathwayspecific model and with the global network of proteinprotein, 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
Data integration and
statistical mining
Need computational
tools able to distill
pathways of interest
from large molecular
interaction databases
(top-down)
List of genes implicated in an experiment
• What do we make of such a result?
Jelinsky S & Samson LD,
Proc. Natl. Acad. Sci. USA
Vol. 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
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mRNA expression data
Protein modifications
Protein levels
Growth phenotype
Dynamics over time
Mapping the phenotypic data to the network
•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
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.
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 topdown modeling
• Metabolic models, built bottom-up, are being
refined with genomic information
• Data – Model – Predictions – Data: cycle as a
Systems Biology theme