Download Gene Expression Analysis, DNA Chips and Genetic Networks

Computational Systems Biology:
An Introduction
Eytan Ruppin, 2012
‫השראה‪ :‬קוראים לי ריי קורצווייל ואני אחיה •‬
‫לנצח‬
‫בגיל ‪ 17‬הוא לימד מחשב להלחין מוזיקה‪• ,‬‬
‫בגיל ‪ 27‬המציא את הסורק‪ ,‬ובעשורים הבאים‬
‫הפך למיליונר בזכות מאות פטנטים וחזה את‬
‫מהפכות האינטרנט והסלולר‪ .‬עכשיו‪ ,‬בגיל‬
‫‪ ,60‬נביא ההייטק ריי קורצווייל גילה שאנחנו‬
‫בדרך לחיי נצח‬
1. Molecular biology – a (very)
quick recap..
The Cell
• Basic unit of life.
• Carries complete
characteristics of the species.
• All cells store hereditary
information in DNA.
• All cells transform DNA to
proteins, which determine
cell’s structure and function.
• Two classes: eukaryotes
(with nucleus) and
prokaryotes (without).
http://regentsprep.org/Regents/biology/units/organization/cell.gif
http://www.ornl.gov/hgmis/publicat/tko/index.htm
DNA
The hard
disk
RNA
One
program
transcription
protein
Its output
translation
Gene expression
DNA
PremRNA
transcription
Mature
mRNA
splicing
protein
translation
Gene expression
DNA
PremRNA
transcription
Mature
mRNA
splicing
protein
translation
Transcription factors (TFs) control
transcription by binding to specific DNA
sequence motifs.
Gene
The Human Genome: numbers
•
•
•
•
23 pairs of chromosomes
~3,200,000,000 bases
~25,000 genes
Gene length: 1000-3000 bases,
spanning 30-40,000 bases
• ~1,000,000 protein variants
Model Organisms
• Eukaryotes; increasing complexity
• Easy to store, manipulate.
Budding yeast
• 1 cell
• 6K genes
Nematode worm
• 959 cells
• 19K genes
Fruit fly
• vertebrate
• 14K genes
mouse
• mammal
• 30K genes
High-throughput measurement
Protein-protein
Protein-DNA (transcriptional) interaction (PPI):
yeast two-hybrid
Genetic interactions interactions: chip-on-chip
DNA
RNA
Genome:
Transcriptome:
Sequencing technologies Microarrays
protein
Proteome:
Various assays
2. Systems Biology
The Reductionist Approach to
Biological Research
• Explanations of things ought to be continually reduced to the
very simplest entities
• Identifying individual genes, proteins and cells, and studying
their specific functions
The Reductionist Approach to Biological
Research (20th century biology)
Explanations of things ought to be continually reduced to the very simplest
entities
Can this approach explain the
behavior of a complex system?
Building models from parts
lists
•High throughput technologies signal the end of reductionism in biology
Why Build Models?
(Jay Bailey, 1998)
• 1. To organize disparate information into a
coherent whole
• 2. To think (and calculate) logically about
what components and interactions are
important in a complex system.
• 3. To discover new strategies
• 4. To make important corrections to the
conventional wisdom
• 5. To understand the essential qualitative
features
• "One is neither too scrupulous and
sincere, nor too subjected to nature;
but one is more or less master of his
model, and especially of his means of
expression"
When one is a master of his
own model..
So what is Systems Biology?
• The study of the mechanisms underlying complex
biological processes as integrated systems of
many interacting components.
– collection of large sets of experimental data
– proposal of mathematical models that might account for
at least some significant aspects of this data set
– accurate computer solution of the mathematical
equations to obtain numerical predictions,
– assessment of the quality of the model by comparing
numerical simulations with the experimental data.
• First described in 1999 by Leroy Hood – Director
of the Institute for Systems Biology
What’s it good for?
• Basic Science/”Understanding Life”
• Predicting Phenotype from Genotype
• Understanding/Predicting
–
–
–
–
Metabolism
Cellular signal trasduction
Cell-Cell Communication
Pathogenicity/Toxicity
• Biology in silico..
Virtual life..
PubMed abstracts indicate a growing
interest in Systems Biology
Human genome completed
3. Biological Networks
From genomics to genetic circuits
• The coordinated action of multiple gene products can
be viewed as a network
Transcriptional Regulatory
Network
• Nodes – transcription factors (TFs) and genes;
• Edges – directed from transcription factor to the
genes it regulates
• Reflect the cell’s genetic regulatory circuitry
• Derived through:
▲ Chromatin IP
▲ Microarrays
S. cerevisiae
1062 TFs, X genes
1149 interactions
Protein-Protein Interaction
(PPI) Networks
•
•
•
•
Nodes – proteins;
Edges – interactions
Reflect the cell’s machinery and signlaing pathways.
High-throughput experiments:
▲ Protein coIP
▲ Yeast two-hybrid
S. cerevisiae
4389 proteins
14319 interactions
Metabolic Networks
• Nodes – metabolites; Edges – biochemical reactions
• Reflect the cell’s metabolic circuitry
• Derived through:
▲ Biochemistry knowledge
▲ Metabolic flux measurements
S. cerevisiae
1062 metabolites
1149 reactions
Systems Biology: Network States
There are many sources of information
about biological networks
Biological networks operate in the
crowded intra‐cellular environment
4. How do we model the
complex biological processes
encoded in these networks?
Modeling the Network Function
•A dynamic system with differential equations
•Requires unknown data on kinetic constants and
concentrations
Kinetic models
Approx. kinetics
Abstraction Signaling Metabolic
level
Constraint-based
analysis
•Constraint-based modeling
•Boolean and discrete models, bayesian models,
linear models, etc
Conventional
functional models
Topological
analysis
Regulatory
•Topological analysis
•Degree distribution, motifs, functional modules
PPI
30
Types of models
•
•
•
•
•
Data models – reconstruction
Topological – structure of networks
Steady state – linear algebra
Dynamic states – ODEs
Thermal fluctuations – noise,
stochastic ODEs
• Sensitivity – MCA, etc
Interim Summary
•
•
•
•
•
The genotype‐phenotype relationship is
fundamental in biology
Systems biology promises to make this
relationship mechanistic
The core paradigm is a four step process –
Components‐>networks‐>in silico models‐>phenotype
Network reconstruction is foundational to the
field and a common denominator
Models are built to describe steady states
(capabilities) and dynamics states
• And now to Monty Python something
completely different..
• The Future as seen at Present
New upcoming Data
• The revolution in genome sequencing technologies
• Which leads also to new gene expression
technologies
• microRNA chips
• Large scale protein abundance data
• Large scale metabolomics data
• Completing the identification of cellular networks
• Large scale individual cell measurements in high
temporal resolution
Research Questions &
Challenges – I. Basic Science
• The riddle of embryonic development
• How are cells regulated?
• The riddle of `junk’ DNA and the hidden world of
mRNA
• How and to what extent does the normal cellular
genotype determine the phenotype? – Epigenetics..
• The emergent properties of tissues and organs
• Evolutionary systems biology – the search for
LUCA, the origins of multi-cellularity, the ascent
of man..
Research Questions &
Challenges – II. Applications
• Charting the pathophysiology of human
diseases
• Identifying new drugs and combinations of
drugs
• Stems cell research and tissue and organ
replacement
• Whats can microrganisms do for you?
• Metagenomics and the art of sailing..
• Personalized Medicine
Some potential computational
avenues
• Genome association & CN studies
• New approaches for modeling
integrated cellular functions – in silico
cellular biology
• Models of tissues and systems
• “Interfacing” with the community and
the literature..
My lab: Don’t ask what Sysbio can do
for you, ask what you can do for Sysbio

• Studying cancer metabolism and predicting and
testing new anti-cancer therapies
• Computational methods for predicting biomarkers
and disease diagnosis
• Searching for new antibiotics that are resistant to
resistance…
• Building and studying the gut metabolome
• The evolution of human brains..
• Metabolism of stem cells, Alzheimer’s disease,
diabetes.
• Fighting aging and extending human lifespan