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7.81J/8.591J/9.531J
Systems Biology
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Introducing ...
Lectures: Recitations:
TR 1:00 -2:30 PM W 4:00 - 5:00 PM
Alexander van Oudenaarden Juan Pedraza
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Text books: none
Handouts will be available on-line
Good reference (biology textbook):Molecular biology of the cellAlberts et al.
Matlab will be used intensively during thecourse, make sure you known (or learn) howto use it (necessary for problem sets)
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Intrinsic challenge of this class:
mixed audience with wildly different backgrounds
⇒ read up on your biology or math if needed
⇒ recitations (W 4PM,) are intended to close the gaps and prepare for homework
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Systems Biology ?
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Systems Biology ≈ Network Biology
GOAL: develop a quantitative understanding of the biological functionof genetic and biochemical networks
gene A
gene C
gene E
gene B
gene D
gene F
INPUT
OUTPUT
- function of gene product A-F can be known in detail but this is notsufficient to reveal the biological function of the INPUT-OUTPUT relation
- a system approach (looking beyond one gene/protein) is necessary toreveal the biological function of this whole network
- what is the function of the individual interactions (feedbacks andfeedforwards) in the context of the entire network ?
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Three levels of complexity
I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
II Systems Cell Biology (8 Lectures)
‘The cell as a compartmentalized system withconcentration gradients’
III Systems Developmental Biology (3 Lectures)
‘The cell in a social context communicating withneighboring cells’
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I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
L1 IntroductionL2 Chemical kinetics, Equilibrium binding, cooperativityL3 Lambda phageL4 Stability analysisL5-6 Genetic switchesL7 E. coli chemotaxisL8 Fine-tuned versus robust modelsL9 Receptor clusteringL10-11 Stochastic chemical kineticsL12-13 Genetic oscillatorsL14 Circadian rhythms
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I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
L1 IntroductionL2 Chemical kinetics, Equilibrium binding, cooperativityL3 Lambda phageL4 Stability analysisL5-6 Genetic switchesL7 E. coli chemotaxisL8 Fine-tuned versus robust modelsL9 Receptor clusteringL10-11 Stochastic chemical kineticsL12-13 Genetic oscillatorsL14 Circadian rhythms
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Introduction phage biology
Phage genome:48512 base pairs ~ 12 kB‘phage.jpg’ ~ 10 kB
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda. 3rd ed. Cold Spring Harbor,N .Y.: Cold Spring Harbor Laboratory Press, 2004.
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Image by MIT OCW.
DNA
DNA
RNA
Protein
Nuclear Envelope
Information
Ribosome
Protein
TRANSLATIONProtein Synthesis
TRANSCRIPTIONRNA Synthesis
Cytoplasm
Nucleus
REPLICATIONDNA Duplicates
Information
Information
mRNA
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The central dogma defines three major groups of biomolecules (biopolymers):
1. DNA (passive library, 6×109 bp, 2 m/cell,75×1012 cells/human, total length150×1012 m/human ~ 1000 rsun-earth)
2. RNA (‘passive’ intermediate)3. Proteins (active work horses)
The fourth (and final) group consists ofso-called ‘small molecules’.
4. Small molecules (sugars, hormones,vitamines, ‘substrates’ etc.)
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The lysis-lysogeny decision:
As the phage genome is injectedphage genes are transcribed andtranslated by using the host’smachinery.
Which set of phage proteins areexpressed determines the fate of thephage: lysis or lysogeny
Image by MIT OCW.
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The lysis-lysogeny decision is a genetic switch
Image by MIT OCW. After Ptashne, Mark. A genetic switch : phage lambda. 3rd ed. Cold Spring Harbor, N.Y. :Cold Spring Harbor Laboratory Press, 2004.
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Single repressor dimer bound - three cases:
I Negative control, dimer binding to OR2 inhibitsRNAp binding to right PR promoter.
Positive control, dimer binding to OR2 enhancesRNAp binding to left PRM promoter.
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
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II Negative control, dimer binding to OR1 inhibitsRNAp binding to right PR promoter.
Negative control, dimer binding to OR1 inhibitsRNAp binding to left PRM promoter (too distant).
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
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III Negative control, dimer binding to OR3 inhibitsRNAp binding to left PRM promoter.
Positive control, dimer binding to OR3 allowsRNAp binding to right PR promoter.
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
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Repressor-DNA binding is highly cooperative
intrinsic association constants:KOR1 ~ 10 KOR2 ~ 10 KOR3
However KOR2* >> KOR2 (positive cooperativity)
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
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Flipping the switch by UV:
Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda. 3rd ed. Cold
Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
In lysogenic state, [repressor]is maintained at constant levelby negative feedback
Image by MIT OCW.
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UV radiation induces SOS response (DNA damage)protein RecA becomes specific protease for λ repressor
Images removed due to copyright considerations.See Ptashne, Mark. A genetic switch : phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
after cleavage monomers cannot dimerize anymore,[repressor dimers] decreases,when all repressors vacate DNA, Cro gene switches on.
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Image removed due to copyright considerations.See Ptashne, Mark. A genetic switch: phage lambda.3rd ed. Cold Spring Harbor, N.Y.: Cold Spring Harbor Laboratory Press, 2004.
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Cooperative effects make sharp switch(‘well defined’ decision)
Note: several layers of cooperativity:dimerization, cooperative repressor binding
Images by MIT OCW.
1.0
0.8
0.6
0.4
0.2
0.0
0 1 2 3 4 5
(mM)[S] Y
nH=1, non cooperative
nH=3, positively cooperativenH=3, positively cooperative
100
50
Repressor concentration
% R
epre
ssio
n promoter controlled by a
single repressor-operator system
99.7% repression
lysogen
λPD
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I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
L1 IntroductionL2 Chemical kinetics, Equilibrium binding, cooperativityL3 Lambda phageL4 Stability analysisL5-6 Genetic switchesL7 E. coli chemotaxisL8 Fine-tuned versus robust modelsL9 Receptor clusteringL10-11 Stochastic chemical kineticsL12-13 Genetic oscillatorsL14 Circadian rhythms
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Images removed due to copyright considerations.
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The Flagellum
Image removed due to copyright considerations.
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Absence of chemical attractant
Image by MIT OCW.
Tumble
Run
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Presence of chemical attractant
Image by MIT OCW.
Tumble
Chemical Gradient Sensed in a Temporal Manner
Run
Attractant
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Figure 1A in Mittal, N., E. O. Budrene, M. P. Brenner, and A. Van Oudenaarden."Motility of Escherichia coli cells in clusters formed by chemotactic aggregation." Proc NatlAcad Sci U S A. 100, no. 23 ( Nov 11, 2003): 13259-63. Epub 2003 Nov 03.
Copyright (2003) National Academy of Sciences, U. S. A.
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Chemotaxis of Escherichia coli
Images removed due to copyright considerations.
absence aspartate gradient random walk (diffusion)presence aspartate gradient biased random walk towards
aspartate source
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Image by MIT OCW. After figure 4 in Falke, J. J., R. B. Bass, S. L. Butler, S. A. Chervitz, and M. A. Danielson. "The two-component signaling pathway of bacterial chemotaxis: a molecular view of signal transduction by receptors, kinases,and adaptation enzymes." Annu Rev Cell Dev Biol 13 (1997):457-512.
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Adaptation:
Image by MIT OCW.
1 2 3 4
tumbling
methylation
add attractant
Correlation of Receptor Methylation with Behavioral Response
remove attractant
add moreattractant
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fast
slow
intermediate
What is the simplest mathematical modelthat is consistent with the biology andreproduces the experiments ?
Figures 2 in Spiro, P. A., J. S. Parkinson, and H. G. Othmer."A model of excitation and adaptation in bacterial chemotaxis." Proc Natl Acad Sci U S A.
94, no. 14 (Jul 8, 1997): 7263-8.Copyright (1997) National Academy of Sciences, U. S. A.
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I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
L1 IntroductionL2 Chemical kinetics, Equilibrium binding, cooperativityL3 Lambda phageL4 Stability analysisL5-6 Genetic switchesL7 E. coli chemotaxisL8 Fine-tuned versus robust modelsL9 Receptor clusteringL10-11 Stochastic chemical kineticsL12-13 Genetic oscillatorsL14 Circadian rhythms
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II Systems Cell Biology (8 Lectures)
‘The cell as a compartmentalized system withconcentration gradients’
L15 Diffusion, Fick’s equations, boundary and initial conditionsL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamics
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II Systems Cell Biology (8 Lectures)
‘The cell as a compartmentalized system withconcentration gradients’
L15 Diffusion, Fick’s equations, boundary and initial conditionsL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamics
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Eukaryotic Chemotaxis
Image removed due to copyright considerations.
How is this different from E. coli chemotaxis ?
temporal versus spatial sensing
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cyclic AMP (cAMP) is an attractantfor Dictyostelium (social amoeba)
Image removed due to copyright considerations.
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Response of Dictyostelium to cAMP
uniform stepin cAMP
cAMP gradient
initialdistributiont ~ 3 s
steady-statedistributiont ∞
uniform and transient polarized and persistent
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geometry of cell: circularinside cytoplasm: well-stirredinside membrane: diffusion-limited
GFP-PH binds special lipids in membrane:PIP2 and PIP3
Image by MIT OCW.
Chemoattractant
Micropipette
Cell
Trailing edge
Leading edge
GFP-Protein
θ
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The molecules in the model:
Image by MIT OCW.
PI4P,5P2
Plasma Membrane
EndoplasmicReticulum
Receptor Regulated Step
DG
IP3
Inositol
PA CDP.DG
CDP.DGS PIS
DGK PI4K
PITP Cytosol
PLC PI4P5K
+
PI4P
PI
PIPA
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II Systems Cell Biology (8 Lectures)
‘The cell as a compartmentalized system withconcentration gradients’
L15 Diffusion, Fick’s equations, boundary and initial conditionsL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamics
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how to find the middle ofa cell ?
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Most of MinE accumulates at the rim of this tube, in the shapeof a ring (the E ring). The rim of the MinC/D tube andassociated E ring move from a central position to the cellpole until both the tube and ring vanish. Meanwhile, a newMinC/D tube and associated E ring form in the opposite cellhalf, and the process repeats, resulting in a pole-to-poleoscillation cycle of the division inhibitor.A full cycle takes about 50 s.
Image removed due to copyright considerations.
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Recent results demonstratethat the min proteins assemble in
helices
Image removed due to copyright considerations.
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II Systems Cell Biology (8 Lectures)
‘The cell as a compartmentalized system withconcentration gradients’
L15 Diffusion, Fick’s equations, boundary and initial conditionsL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamics
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Center finding in an eukaryotic cell: fission yeastThe importance of the cytoskeleton
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III Systems Developmental Biology (3 Lectures)
‘The cell in a social context communicating withneighboring cells’
L23 Quorum sensingL24-25 Drosophila development
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III Systems Developmental Biology (3 Lectures)
‘The cell in a social context communicating withneighboring cells’
L23 Quorum sensingL24-25 Drosophila development
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major advantage ofDrosphila:
each stripe in theembryo correspondsto certain body partsin adult fly
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interpreting the bicoid gradient (createdby maternal effects) by zygotic effect(gene expression by embryo itself)
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hunchback readsthe bicoid gradient
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Center finding in the Drosophila embryo
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I Systems Microbiology (14 Lectures)
‘The cell as a well-stirred biochemical reactor’
L1 IntroductionL2 Chemical kinetics, Equilibrium binding, cooperativityL3 Lambda phageL4 Stability analysisL5-6 Genetic switchesL7 E. coli chemotaxisL8 Fine-tuned versus robust modelsL9 Receptor clusteringL10-11 Stochastic chemical kineticsL12-13 Genetic oscillatorsL14 Circadian rhythms