Systems Biology

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Systems Biology

• Micro 343
• David Wishart Rm. Ath 3-41
• david.wishart_at_ualberta.ca

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Objectives

• To gain some familiarity with systems biology and
  how it relates to proteins, proteomics, genomics
  and bioinformatics holistic view
• Understanding that systems biology is a mix of
  molecular biology, physiology, simulation
  (computing) and math
• In 2010 Systems biology Biology

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Genomics, Proteomics Systems Biology
Genomics
Proteomics
Systems Biology
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What is Systems Biology?

• Systems Biology - The study of the mechanisms
  underlying complex biological processes as
  integrated systems of many interacting
  components. Systems biology involves (1)
  collection of large sets of experimental data (2)
  proposal of mathematical models that might
  account for at least some significant aspects of
  this data set, (3) accurate computer solution of
  the mathematical equations to obtain numerical
  predictions, and (4) assessment of the quality of
  the model by comparing numerical simulations with
  the experimental data.
• First described in 1999 by Leroy Hood

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System Biology
Lee Hood director of the Institute for System
Biology
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Institute for System Biology
http//www.systemsbiology.org/
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Systems Biology
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Different Ways of Viewing the World

• Physicists
• Chemists
• Biologists

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Building Blocks in Physics
The Particle Spectrum
115 Elements
28 Elementary Particles
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Building Blocks in Chemistry
10,750 compounds in KEGG
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Building Blocks in Biology
107 species
1012 cells/ organism
105 proteins/species
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A Matter of Scale
Physics Chemistry Biology
28 Particles
10,750 Chemicals
1 trillion trillion Components
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Scientific Simulation

• Gives scientists an opportunity to see whats too
  small to be seen
• Gives temporal and spatial meaning to
  hard-to-understand processes
• Creates a reality that is closer to our own
  macroscopic experience
• Allows scientists to predict, model and conduct
  experiments that are beyond current capabilities
  on a computer

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Our Seeing Limits (and Limitations)
Free 5 5000
500,000
1 m 1 x 10-3 m 1x10-6 m
1x10-9 m
Live, moving Live, moving Fixed,
stained Fixed, stained
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Our Seeing Limits (and Limitations)
5,000,000 500,000,000
1x10-10 m 1x10-12 m
Extracted, crystallized
Atomized, vaporized
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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Powers of 10
http//micro.magnet.fsu.edu/primer/java/scienceopt
icsu/powersof10/
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The Language of Physics
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The Language of Chemistry
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The Language of Biology

• The EGF receptor binds epidermal growth factor
  which triggers the phosphorylation of PLC-gamma
  followed by the binding and subsequent
  phosphorylation of Grb2 and SOS which leads to
  the formation of a Raf1-MEK complex which, in
  turn, leads to a p21ras auto-phosphorylation
  cascade. The complex then phosphorylates a MAP
  kinase whichis transported to the nucleus via a
  nuclear transport signal which triggers the
  transcription of c-Fos, c-Myc and c-Jun which
  upon release in the rough ER are transported to

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The Language of Biology
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A Fundamental Difference

• What happens if I drop this ball?
• Physics -- predictive
• What happens if I mix this acid with that base?
• Chemistry -- predictive
• What happens if this TGF receptor is
  phosphorylated?
• Biology -- observational

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Stamp Collecting vs. Stamp Making
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THE Grand Challenge

• Making Biology A Predictive Science

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Whats it good for?

• Basic Science/Understanding Life
• Predicting Phenotype from Genotype
• Understanding/Predicting Metabolism
• Understanding Cellular Networks
• Understanding Cell-Cell Communication
• Understanding Pathogenicity/Toxicity
• Raising the Bar for Biologists

Making Biology a Predictive Science
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Are We Ready?

• 100s of completed genomes
• 1000s of known reactions
• 10,000s of known 3D structures
• 100,000s of protein-ligand interactions
• 1,000,000s of known proteins enzymes
• Decades of biological/chemical know-how
• Computational Mathematical resources

The Push to Systems Biology
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The Technologies of Systems Biology

• Genomics (HT-DNA sequencing)
• Mutation detection (SNP methods)
• Transcriptomics (Gene/Transcript measurement,
  SAGE, gene chips, microarrays)
• Proteomics (MS, 2D-PAGE, protein chips,
  Yeast-2-hybrid, X-ray, NMR)
• Metabolomics (NMR, X-ray, capillary
  electrophoresis)

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-Omics Mania
biome, CHOmics, cellome, cellomics, chronomics,
clinomics, complexome, crystallomics, cytomics,
cytoskeleton, degradomics, diagnomicsTM,
enzymome, epigenome, expressome, fluxome,
foldome, secretome, functome, functomics,
genomics, glycomics, immunome, transcriptomics,
integromics, interactome, kinome, ligandomics,
lipoproteomics, localizome, phenomics,
metabolome, pharmacometabonomics, methylome,
microbiome, morphome, neurogenomics, nucleome,
secretome, oncogenomics, operome,
transcriptomics, ORFeome, parasitome, pathome,
peptidome, pharmacogenome, pharmacomethylomics,
phenomics, phylome, physiogenomics, postgenomics,
predictome, promoterome, proteomics,
pseudogenome, secretome, regulome, resistome,
ribonome, ribonomics, riboproteomics,
saccharomics, secretome, somatonome, systeome,
toxicomics, transcriptome, translatome,
secretome, unknome, vaccinome, variomics...
http//www.genomicglossaries.com/content/omes.asp
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Going From Technology to Systems Biology

• Genomics Genometrics
• Proteomics Proteometrics
• Metabolomics Metabometrics
• Phenomics Phenometrics
• Bioinformatics Biosimulation
• Quantify, quantify, quantify

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How to Do This?

• Complete the parts inventory (GP)
• Figure out what things look like (G,PB)
• Figure out how things connect (G,PB)
• Figure out how to name describe things (B)
• Backfill what has been learned (B)
• Find patterns and learn from them (B)
• Develop a mathematical formalism (B)

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How to Do it?Three Types of Simulation
Atomic Scale 0.1 - 1.0 nm Coordinate data Dynamic
data 0.1 - 10 ns Molecular dynamics
Meso Scale 1.0 - 10 nm Interaction data Kon,
Koff, Kd 10 ns - 10 ms Mesodynamics
Continuum Model 10 - 100 nm Concentrations Diffusi
on rates 10 ms - 1000 s Fluid dynamics
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Whos Doing It?

• E-cell Project (Keio University, Japan)
• BioSpice Project (Arkin, Berkeley)
• Metabolic Engineering Working Group (Palsson
  Church, UCSD, Harvard)
• Silicon Cell Project (Netherlands)
• Virtual Cell Project (UConn)
• Gene Network Sciences Inc. (Cornell)
• Project CyberCell (Edmonton/Calgary)

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www.e-cell.org
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Adam Arkin
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B. Palsson-Genetic Circuits
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Gene Network Sciences
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Nationalism in Simulation

• Petri Nets Germany, Japan
• Flux-Balance Analysis USA
• Pi Calculus France
• ODEs and PDEs Japan, UK
• Agent-Based methods (CA) - Canada

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SimCell
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Drawing Interaction Rules with SimCell
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Some Examples of SimCell in Action

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Enzyme-Substrate Progress Curves
Lactate Lo (1 e-kt)
Lactate Lo (1 e-kt)
pyruvate NADH ? lactate NAD
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The TCA Cycle SimCell
Acetate
Acetyl-CoA
Glycerol
Pyruvate
Oxaloacetate
Citrate
Isocitrate
L-Malate
?-Ketoglutarate
Fumarate
2
1
Succinate dehydrogenase
Succinate
Succinyl-CoA
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Metabolic Profiling
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Succinate Production
Observed Predicted (SimCell)
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Glycerol Consumption
Observed Predicted (SimCell)
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Trp Repressor
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CA for Trp Repressor
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More Trp Repressor
Bolus Trp addition
No trp repressor
molecules (P)
No Trp
time
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Repressilator
Nature, 403 335-338 (2000)
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Repressilator
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Repressilator
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Repressilator
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SimCell vs. DE
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Repressilator Oscillations
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Can We Move to 3D?

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3-D CA of Diffusion Reaction (M. Ellison)
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3D-CA Simulations of Transport (M. Ellison)
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Simulating Membranes Osmotic Shock
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Simulating Membrane Growth
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SimCell and Cell Simulation

• Ideal for model checking and validation
• Conceptually equivalent to spatially dependent
  stochastic Petri nets
• Universally applicable Enzyme kinetics,
  diffusion, excluded volume, binding, vesicle
  fusion, osmotic lysis, osmotic pressure, genetic
  circuits, metabolism, transport, repression,
  signalling, cell division, embryo gene
  expression All from one tool!

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Summary

• Systems Biology requires the integration of data
  archiving, experimentation and novel
  computational approaches
• There is a clear need for bioinformatics to step
  up from the static stamp collection phase to
  thinking about systems in dynamic/interactive/inte
  grated terms
• New tools are needed to make this possible
  consider DCA Petri Nets