Computational Systems Biology:
An Introduction
Eytan Ruppin, 2012
השראה: קוראים לי ריי קורצווייל ואני •אחיה לנצח
• הוא לימד מחשב להלחין מוזיקה, 17בגיל המציא את הסורק, ובעשורים 27בגיל
הבאים הפך למיליונר בזכות מאות פטנטים וחזה את מהפכות האינטרנט
, נביא ההייטק 60והסלולר. עכשיו, בגיל 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
DNA RNA protein
transcription translation
The hard disk
One program
Its output
:// . . / / / / .http www ornl gov hgmis publicat tko index htm
DNA Pre-mRNA
protein
transcription translation
Mature
mRNA
splicing
Gene expression
DNA Pre-mRNA
protein
transcription translation
Mature
mRNA
splicing
Gene expression
Gene
Transcription factors (TFs) control transcription by binding to specific DNA sequence motifs.
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
DNA RNA proteinGenome: Sequencing technologies
Transcriptome: Microarrays
Proteome: Various assays
Protein-protein interaction (PPI): yeast two-hybrid
Protein-DNA (transcriptional) interactions: chip-on-chip
Genetic interactions
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:
1062 TFs, X genes 1149 interactions
S. cerevisiae
▲ Chromatin IP ▲ Microarrays
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
4389 proteins 14319 interactions
S. cerevisiae
Metabolic Networks• Nodes – metabolites; Edges – biochemical
reactions• Reflect the cell’s metabolic circuitry• Derived through:
1062 metabolites 1149 reactions
S. cerevisiae
▲ Biochemistry knowledge▲ Metabolic flux measurements
Systems Biology: Network States
There are many sources of information
about biological networks
Biological networks operate in thecrowded intra‐cellular environment
4. How do we model the complex biological processes encoded in these networks?
30
Modeling the Network Function
Kinetic models
Approx. kinetics
•A dynamic system with differential equations•Requires unknown data on kinetic constants and concentrations
Topological analysis
•Topological analysis•Degree distribution, motifs, functional modules
Constraint-based analysis
•Constraint-based modeling
•Boolean and discrete models, bayesian models, linear models, etc
Conventional functional models
Metabolic
PPI
Signaling
Regulatory
Abstraction level
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