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7.32/7.81J/8.591J Systems Biology Systems Biology ‘modeling biological networks’ ‘modeling biological networks’ 1 Introducing Introducing ... L t R it ti Lectures: Recitations: TR 1:00 -2:30 PM W 4:00 - 5:00 PM Rm. 6-120 Rm. 26-204 (under construction) Alexander van Oudenaarden Jialing Li Rm. 68-371B [email protected] avano@mit edu Rm 68 383 avano@mit.edu Rm. 68-383 Bernardo Pando 2 [email protected] Rm. 68-365 Take-home Exams: Exam 1 due 10/06/09 15 % Exam 1 due 10/06/09 15 % Exam 2 due 10/20/09 15 % Exam 3 due 11/03/09 15 % E 4 d 11/17/09 15 % Exam 4 due 11/17/09 15 % Exam 5 due 12/01/09 15 % Final due 12/10/09 25 % Final Problem Set will be a take-home PS broadly covering the course material 3 Text books: none Text books: none Handouts will be available on-line Good reference (biology textbook): Molecular biology of the cell Alberts et al. Matlab will be used intensively during the Matlab will be used intensively during the course, make sure you known (or learn) how to use it (necessary for problem sets) Web-page: web.mit.edu/biophysics/sbio (contains all course materials) 4

L1 notes - MITweb.mit.edu/biophysics/sbio/PDFs/L1_slides.pdfL1 Introduction L2-3 Chemical kinetics, Equilibrium binding, cooperativity L4 Lambda ppghage L5 Cellular memory L6-7 Genetic

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7.32/7.81J/8.591J

Systems BiologySystems Biology‘modeling biological networks’‘modeling biological networks’

1

IntroducingIntroducing ...

L t R it tiLectures: Recitations:

TR 1:00 -2:30 PM W 4:00 - 5:00 PMRm. 6-120 Rm. 26-204

(under construction)

Alexander van Oudenaarden Jialing LiRm. 68-371B [email protected]@mit edu Rm 68 [email protected] Rm. 68-383

Bernardo Pando

2

[email protected]. 68-365

Take-home Exams:

Exam 1 due 10/06/09 15 %Exam 1 due 10/06/09 15 %Exam 2 due 10/20/09 15 %Exam 3 due 11/03/09 15 %E 4 d 11/17/09 15 %Exam 4 due 11/17/09 15 %Exam 5 due 12/01/09 15 %Final due 12/10/09 25 %

Final Problem Set will be a take-home PSbroadly covering the course material

3

Text books: noneText 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 theMatlab will be used intensively during thecourse, make sure you known (or learn) howto use it (necessary for problem sets)

Web-page: web.mit.edu/biophysics/sbio(contains all course materials)

4

Intrinsic challenge of this class:Intrinsic challenge of this class:

mixed audience with wildly different backgrounds

⇒ read up on your biology or math if neededp y gy

⇒ recitations (W 4PM, Rm. 26-204) are intended to close the gaps and prepare for homeworkclose the gaps and prepare for homework

5

Systems Biology ?Systems Biology ?

6

Systems Biology ≈ Network Biology

GOAL: develop a quantitative (mathematical) understanding of thebiological function of genetic and biochemical networks

INPUT

gene A gene B

INPUT

gene C gene D

gene E gene F OUTPUT

- function of gene product A-F can be known in detail but this is notfunction 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

7

g- what is the function of the individual interactions (feedbacks and

feedforwards) in the context of the entire network ?

Three levels of complexity

I Systems Microbiology (~14 Lectures)

‘The cell as a well-stirred biochemical reactor’

II Systems Cell Biology (~8 Lectures)II Systems Cell Biology ( 8 Lectures)

‘The cell as a compartmentalized system withconcentration gradients’concentration gradients

III Systems Developmental Biology (~3 Lectures)

‘The cell in a social context communicating withneighboring cells’

8

neighboring cells

I Systems Microbiology (14 Lectures)

‘The cell as a well-stirred biochemical reactor’

L1 IntroductionL2-3 Chemical kinetics, Equilibrium binding, cooperativityL4 Lambda phagep gL5 Cellular memoryL6-7 Genetic switchesL8-9 E. coli chemotaxisL10 Stability analysisL11-12 Genetic oscillatorsL13-14 Stochastic chemical kinetics

9

I Systems Microbiology (14 Lectures)

‘The cell as a well-stirred biochemical reactor’

L1 IntroductionL2-3 Chemical kinetics, Equilibrium binding, cooperativityL4 Lambda phagep gL5 Cellular memoryL6-7 Genetic switchesL8-9 E. coli chemotaxisL10 Stability analysisL11-12 Genetic oscillatorsL13-14 Stochastic chemical kinetics

10

Introduction phage biology

Phage genome:48512 base pairs ~ 12 kB‘phage jpg’ ~ 10 kBphage.jpg ~ 10 kB

Excellent book:

A genetic switch by Mark Ptashne

11

A genetic switch by Mark Ptashne

12

The central dogma defines three major groups of biomolecules (biopolymers):

( 9 /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.)

13

The lysis-lysogeny decision:

As the phage genome is injectedphage genes are transcribed andtranslated by using the host’smachinerymachinery.

Which set of phage proteins areexpressed determines the fate of thephage: lysis or lysogenyphage: lysis or lysogeny

14

The lysis-lysogeny decision is a genetic switch

RNA RNARNAp RNAp

15only ‘space’ for one RNA polymerase (mutual exclusion)

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.

ti ti

cI transcriptinhibition

activation

more repressor monomers

16more repressor dimers

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).

17

III Negative control dimer binding to OR3 inhibitsIII Negative control, dimer binding to OR3 inhibitsRNAp binding to left PRM promoter.

Positive control dimer binding to OR3 allowsPositive control, dimer binding to OR3 allowsRNAp binding to right PR promoter.

Cro transcript

more Cro monomers

18more Cro dimers

Repressor-DNA binding is highly cooperative

intrinsic association constants:intrinsic association constants:KOR1 ~ 10 KOR2 ~ 10 KOR3

However KOR2* >> KOR2 (positive cooperativity)OR2 OR2

cI OFF Cro OFF

cI ON Cro OFF

O19

cI OFF Cro OFF

Flipping the switch by UV:

In lysogenic state, [repressor]is maintained at constant levelby negative feedback(“chemo stat”)( chemo-stat )

20

UV radiation induces SOS response (DNA damage)protein RecA becomes specific protease for λ repressor

after cleavage monomers cannot dimerize anymore,[repressor dimers] decreases,when all repressors vacate DNA, Cro gene switches on.

21

p , g

other lytic genes

22

Cooperative effects make sharp switch(‘ ll d fi d’ d i i )(‘well defined’ decision)

Note: several layers of cooperativity:

23

Note: several layers of cooperativity:dimerization, cooperative repressor binding

24

I Systems Microbiology (14 Lectures)

‘The cell as a well-stirred biochemical reactor’

L1 IntroductionL2-3 Chemical kinetics, Equilibrium binding, cooperativityL4 Lambda phagep gL5 Cellular memoryL6-7 Genetic switchesL8-9 E. coli chemotaxisL10 Stability analysisL11-12 Genetic oscillatorsL13-14 Stochastic chemical kinetics

25 26

The Flagellum

1000 H+ / rotation

27> 40 genes involved

Absence of chemical attractant

28

Presence of chemical attractantPresence of chemical attractant

h i l di t d i t l29

chemical gradient sensed in a temporal manner30

Chemotaxis of Escherichia coli

CW rotation: ‘tumbles’CCW rotation: ‘runs’

absence aspartate gradient random walk (diffusion)presence aspartate gradient biased random walk towards

t t

31

aspartate source

32

Chemotactic pathway in E. coli.

Adaptation:

Correlation of receptor methylation with behavioral response. p y p

33

slow

fast

What is the simplest mathematical modelthat is consistent with the biology and

34

that is consistent with the biology andreproduces the experiments ?

I Systems Microbiology (14 Lectures)

‘The cell as a well-stirred biochemical reactor’

L1 IntroductionL2-3 Chemical kinetics, Equilibrium binding, cooperativityL4 Lambda phagep gL5 Cellular memoryL6-7 Genetic switchesL8-9 E. coli chemotaxisL10 Stability analysisL11-12 Genetic oscillatorsL13-14 Stochastic chemical kinetics

35 36

37Kondo et al. PNAS (1994) 38

The circadian clock consists of only 3 proteins:of only 3 proteins:KaiA, KaiB and KaiC

39

Circadian Oscillations inSingle Cells

40

The circadian clock consists of only 3 proteins and can bereconstituted in vitroreconstituted in vitro

41Kondo et al. (2005) 42

43

Modeling the cell cycle

44

45http://www.mpf.biol.vt.edu/research/budding_yeast_model

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 theoryL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamicsL21 22 Modeling cytoskeleton dynamics

46

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 theoryL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamicsL21 22 Modeling cytoskeleton dynamics

47

Eukaryotic Chemotaxis

How is this different from E. coli chemotaxis ?

48temporal versus spatial sensing

cyclic AMP (cAMP) is an attractantfor Dictyostelium (social amoeba)

49

cyclic AMP (cAMP) is an attractantfor Dictyostelium (social amoeba)

50

uniform step in cAMP

cAMP gradient

51

Response of Dictyostelium to cAMP

uniform stepin cAMP

cAMP gradient

initialdistributiont ~ 3 s

steady-statesteady statedistributiont ⌫ ∞

52uniform and transient polarized and persistent

Response of Dictyostelium to pulsed cAMP

53

geometry of cell: circularinside cytoplasm: well-stirredy pinside membrane: diffusion-limited

54GFP-PH binds special lipids in membrane:PIP2 and PIP3

The molecules in the model:

55

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 theoryL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamicsL21 22 Modeling cytoskeleton dynamics

56

how to find the middle ofa cell ?

57

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 andof 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 cellg pphalf, and the process repeats, resulting in a pole-to-poleoscillation cycle of the division inhibitor.A full cycle takes about 50 s.

minEminEminC/D

58

59gfp-minC

GFP-minD

60

minEminDminD

61gfp-minE is localizedin a ring

gfp-minE

62

63

Recent results demonstratethat the min proteins assemble inthat the min proteins assemble in

helices

64

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 theoryL16 Local excitation, global inhibition theoryL17-18 Models for eukaryotic gradient sensingL19-20 Center finding algorithmsL21-22 Modeling cytoskeleton dynamicsL21 22 Modeling cytoskeleton dynamics

65

Center finding in an eukaryotic cell: fission yeastThe importance of the cytoskeletonp y

66

67 68Chang et al.

Microtubules

69

Microtubules

70

71

kinesin is a molecular motor that runs to the plus end of a MT72

dynein is a molecular motor that runs to the minus end of a MT

Black tetraBlack tetra

73Movies !

III Systems Developmental Biology (3 Lectures)

‘The cell in a social context communicating withneighboring cells’

L23 Quorum sensingL24 25 Drosophila developmentL24-25 Drosophila development

74

III Systems Developmental Biology (3 Lectures)

‘The cell in a social context communicating withneighboring cells’

L23 Quorum sensingL24 25 Drosophila developmentL24-25 Drosophila development

75 76

77

major advantage ofDrosphila:

each stripe in theembryo correspondsto certain body parts

78

to certain body partsin adult fly

zygote (contains DNAfrom father and motherfrom father and mother,zygotic effects)

egg (contains maternal components,

79

maternal effects, onlydetermined by mother)

nuclei formplasma membrane

80early development

radioactive labeled RNA reveals localizationat pole

81

at pole

interpreting the bicoid gradient (createdby maternal effects) by zygotic effectby maternal effects) by zygotic effect(gene expression by embryo itself)

82hunchback is a zygotic effect !

hunchback readsthe bicoid gradientthe bicoid gradient

83 84Houchmandzadeh et al.

Center finding in the Drosophila embryo

bicoid

85

hunchback86

I Systems Microbiology (14 Lectures)

‘The cell as a well-stirred biochemical reactor’

L1 IntroductionL2-3 Chemical kinetics, Equilibrium binding, cooperativityL4 Lambda phagep gL5 Cellular memoryL6-7 Genetic switchesL8-9 E. coli chemotaxisL10 Stability analysisL11-12 Genetic oscillatorsL13-14 Stochastic chemical kinetics

87