2. Course outline 1 Introduction 2
Theoretical background Biochemistry/molecular biology 3
Theoretical background computer science 4
History of the field 5
Splicing systems 6
P systems 7
Hairpins 8
Detection techniques 9
Micro technology introduction 10
Microchips and fluidics 11
Self assembly 12
Regulatory networks 13
Molecular motors 14
DNA nanowires 15
Protein computers 16
DNA computing - summery 17
Presentation of essay and discussion

3. CMOS
Complementary Metal-Oxide Semiconductor

4. Introduction

5. Electronic pathway

6. Seoul subway

7. Pyrimidine pathway

8. From DNA to pathways

9. Biological information
Two Types of Biological Information
The genome, digital information
Environmental, analog information

10. Genome information Two types of digital genome information
Genes, the molecular machines of life
Gene regulatory networks, specify the behavior of the genes

11. What is systems biology?
Biological System
DNA
RNA
Biomodules
Cells
Networks
Proteins

12. A gene network …

13. A gene network in a physical network

14. Cell programming

15. Programming cell communities
E. coli
Diffusing signal
proteins
Interest: prog cells to perform tasks
Focus: use inter. Comm. for coordinated behavior
Explain substrate pic
Chromosome = cell OS
Design DNA, put into cell
Signals=protein concentrations
Describe communication – diffusion of signal chemicals

16. Programming cell communities
Program cells to perform various tasks using
Intra-cellular circuits
Digital & analog components
Inter-cellular communication
Control outgoing signals, process incoming signals
Interest: prog cells to perform tasks
Focus: use inter. Comm. for coordinated behavior
Explain substrate pic
Chromosome = cell OS
Design DNA, put into cell
Signals=protein concentrations
Describe communication – diffusion of signal chemicals

17. Programmed cell applications
Biomedical
combinatorial gene regulation with few inputs; tissue engineering
Environmental sensing and effecting
recognize and respond to complex environmental conditions
Engineered crops
toggle switches control expression of growth hormones, pesticides
Cellular-scale fabrication
cellular robots that manufacture complex scaffolds
Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology.
They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products.
But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable.
Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results.
Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

18. Programmed cell applications
Pattern formation
Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology.
They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products.
But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable.
Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results.
Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

19. Programmed cell applications
Analyte source detection
analyte source
reporter rings
Cells are a novel substrate for engineering design -- providing an outstanding interface to the worlds of chemistry and nanotechnology.
They have unique features that make them attractive for a wide variety of applications. These features include a miniature scale, energy efficiency, the ability to self reproduce, and the ability to manufacture biochemical products.
But in order to effectively harness cells for our purposes, we need to address the following problem, which is how to engineer complex behaviors in cells in a fashion that is both predictable and reliable.
Addressing these issues is especially important with biological substrates, where it’s often very difficult to obtain reliable and reproducible results.
Effective solutions to engineering complex cell behaviors will enable applications such as sensing of complex environmental conditions, etc…

20. Biological cell programming

21. Biological cell programming

22. In vivo logic circuits

23. e. coli

24. A genetic circuit building block
Title genetic circuit building block
Threshold detector
Amplifier
delay

25. Logic circuit based on inverters
Proteins are the wires/signals
Promoter + decay implement the gates
NAND gate is a universal logic element:
any (finite) digital circuit can be built!

26. NAND and NOT gate x y
NAND 1 X XY Y x
NOT 1 X X

27. = Logic circuit based on inverters NAND NOT X R1 Z gene Y gene gene X

28. Why digital? We know how to program with it
Signal restoration + modularity = robust complex circuits
Cells do it
Phage λ cI repressor: Lysis or Lysogeny? [Ptashne, A Genetic Switch, 1992]
Circuit simulation of phage λ [McAdams & Shapiro, Science, 1995]
Also working on combining analog & digital circuitry

29. Why digital?

30. BioCircuit CAD
SPICE

31. BioCircuit CAD steady state dynamics intercellular
BioSPICE a prototype biocircuit CAD tool
simulates protein and chemical concentrations
intracellular circuits, intercellular communication
single cells, small cell aggregates

32. Genetic circuit elements
translation
RBS
RBS
input
mRNA
output
mRNA
ribosome
ribosome
transcription
operator
promoter
RNAp

33. Modeling a biochemical inverter
input
repressor
promoter
output

34. A BioSPICE inverter simulation
input
repressor
promoter
output

35. Smallest memory: RS-latch flip-flop1 1 1 1
The output a of the R-S latch can be set to 1 by momentarily setting S to 0 while keeping R at 1.
When S is set back to 1 the output a stays at 1.
Conversely, the output a can be set to 0 by keeping S at 1 and momentarily setting R to 0.
When R is set back to 1, the output a stays at 0.

36. RS-latch flip-flop truth tableQ ~Q
(n+ 1 )
(n+ 1 ) Q ~Q
Q = R + ~Q
(n)
(n) 1 1
~Q = S + Q 1 1 1 1

37. Proof of Concept in BioSPICE
RS-Latch (“flip-flop”)
Ring oscillator _
[R]
[A] _ R _
[S] A
[B]
time (x100 sec)
[B] _ S B
[C]
[A]
time (x100 sec)
time (x100 sec)
They work in vivo
Flip-flop [Gardner & Collins, 2000]
Ring oscillator [Elowitz & Leibler, 2000]
However, cells are very complex environments
Current modeling techniques poorly predict behavior
Work in BioSPICE simulations [Weiss, Homsy, Nagpal, 1998]

38. The IMPLIES gate Inducers that inactivate repressors:
IPTG (Isopropylthio-ß-galactoside)  Lac repressor
aTc (Anhydrotetracycline)  Tet repressor
Use as a logical IMPLIES gate: (NOT R) OR I
Repressor
Output
Inducer

39. The IMPLIES gate active repressor inactive repressor RNAP inducer
no transcription
transcription
RNAP
promoter
operator
gene
promoter
operator
gene

40. The toggle switch pIKE = lac/tet pTAK = lac/cIts
[Gardner & Collins, 2000]

41. The toggle switch promoter protein coding sequence
[Gardner & Collins, 2000]

42. The ring oscillator
[Elowitz, Leibler 2000]

43. The ring oscillator
The repressilator network. The repressilator is a cyclic negative-feedback loop composed of three repressor genes and their corresponding promoters, as shown schematically in the centre of the left-hand plasmid. It uses PLlacO1 and PLtetO1, which are strong, tightly repressible promoters containing lac and tet operators, respectively6, as well as PR, the right promoter from phage l (see Methods). The stability of the three repressors is reduced by the presence of destruction tags (denoted `lite'). The compatible reporter plasmid (right)
expresses an intermediate-stability GFP variant11 (gfp-aav). In both plasmids, transcriptional units are isolated from neighbouring regions by T1 terminators from the E. coli rrnB operon (black boxes).

44. The ring oscillator

45. Evaluation of the ring oscillator
Reliable long-term oscillation doesn’t work yet:
Will matching gates help?
Need to better understand noise
Need better models for circuit design
[Elowitz, Leibler 2000]

46. Evaluation of the ring oscillator
Examples of oscillatory behaviour and of negative controls. a±c, Comparison of the repressilator dynamics exhibited by sibling cells. In each case, the fluorescence timecourse of the cell depicted in Fig. 2 is redrawn in red as a reference, and two of its siblings are shown in blue and green. a, Siblings exhibiting post-septation phase delays relative to the reference cell. b, Examples where phase is approximately maintained but amplitude varies significantly after division. c, Examples of reduced period (green) and long delay (blue). d, Two other examples of oscillatory cells from data obtained in different experiments, under conditions similar to those of a±c. There is a large variability in period and amplitude of oscillations. e, f, Examples of negative control experiments. e, Cells containing the repressilator were disrupted by growth in media containing 50mM IPTG. f, Cells containing only the reporter plasmid.

47. Ring oscillator with mismatched inverters
A = original cI/λP(R)
B = repressor binding 3X weaker
C = transcription 2X stronger

48. Device physics in steady state
“Ideal” inverter
Transfer curve
gain (flat,steep,flat)
adequate noise margins
“gain”
[output] 1
[input]
Curve can be achieved with certain dna-binding proteins
Inverters with these properties can be used to build complex circuits

49. Measuring a transfer curve
Construct a circuit that allows:
Control and observation of input protein levels
Simultaneous observation of resulting output levels
inverter
CFP R
YFP
“drive” gene
output gene
Also, need to normalize CFP vs YFP

50. Flow cytometry (FACS)

51. Drive input levels by varying inducer
IPTG (uM)
lacI
[high]
YFP
(Off)
P(lacIq)
P(lac)
IPTG
P(lacIq)
lacI
100
IPTG
P(lac)
YFP
1000
promoter
protein coding sequence

52. Measuring a transfer curve
for lacI/p(lac)
aTc
YFP
lacI
CFP
tetR
[high]
(Off)
P(LtetO-1)
P(R)
P(lac)
measure TC
P(R)
tetR
P(lac)
YFP
aTc
P(Ltet-O1)
lacI
CFP

53. Transfer curve data points
01
undefined
10
1 ng/ml aTc
10 ng/ml aTc
100 ng/ml aTc

54. lacl/p(lac) transfer curve
aTc
YFP
lacI
CFP
tetR
[high]
(Off)
P(LtetO-1)
P(R)
P(lac)
gain = 4.72

55. Evaluating the transfer curve
Gain / Signal restoration
Noise margins
high gain
note: graphing vs. aTc (i.e. transfer curve of 2 gates)

56. Signal processing circuits

57. Cell-cell communication circuits
VAI
Receiver cells
Sender cells
tetR
P(tet)
luxI
P(Ltet-O1)
aTc
GFP(LVA)
Lux P(R)
luxR
Lux P(L) +
Sender cells
Receiver cells
LuxR
GFP
tetR
luxI VAI
aTc
VAI
aTc
pLuxI-Tet-8
pRCV-3

58. 2:4 multiplexer C(4)HSL qsc box C(6)HSL lux box Cell Color none 1
none 1
Green
(GFP)
Red
(HcRED)
Cyan
(CFP)

59. Significance of multiplexer
With a 2:4 mux, the combination of 2 inputs produces 4 different output states / expressed proteins
In Eukaryotic cells, these proteins could potentially differentiate the cell into one of four cell types
Applications include tissue engineering and more understanding for stem cell fate and determination

60. Mux the sum of three circuits
qsc
lux A 1
green
qsc
lux B 1
red + +
qsc
lux C 1
cyan
qsc
lux D 1
green
red
cyan =

61. Case A
C6HSL
C4HSL
luxR
GFP
lux
box
qsc
box
RhlR
qsc
lux A 1
green

62. Case B
C6HSL
C4HSL
luxR
HcRED
qsc
box
lux
box
RhlR
qsc
lux B 1
red

63. Case C, AND gate cI
lux
box
CFP
λP(R)
qsc
lux C 1
cyan cI
qsc
box

64. luxR RhlR GFP HcRED Case A + B C6HSL C4HSL lux box qsc box qsc lux
AxorB 1
green
red

65. Design considerations
qsc binding site
plasmid copy number
production of C(x)HSL

66. Phenotype tests triple plasmid, regulatory double plasmid, antisensing
double plasmid, antisensing + regulatory
chromosome, antisensing + regulatory

67. Case A
pASK-102: Single
“Parent” Offspring
QSC box

68. Case A
Plasmid 1

69. Case A
Plasmid 2
Parents:
pASK-102-qsc117 (vector)
pECP61.5 (insert)

70. Detecting chemical gradients
signal O O O N H O N H
analyte
source H N O O O N H O N H O N H O N H O N H O N H O N H O N H O O H N O O
reporter rings
Biomat fab: scale, new materials possibilities
Env. Sensing : toxin source pinpointing
Forward engineering: understanding development
Toxin source detection uses the concentration detect circuit
Split this slide into two
Toxin source detection slide
Analyte source detection

71. Circuit components Components Acyl-HSL detect Low thresholdx R O N H
P(lux) X Y Z
P(W)
GFP
P(Z)
P(X) W
P(Y)
luxR
P(R)
Components
Acyl-HSL detect
Low threshold
High threshold
Negating combiner

72. Detecting chemical gradients
acyl-hSL detection L u x R O N H
P(lux) X Y Z
P(W)
GFP
P(Z)
P(X) W
P(Y)
luxR
P(R)
X used for low threshold and Y used as high threshold
X low threshold
Y high threshold

73. Detecting chemical gradients
low threshold detection L u x R O N H
P(lux) X Y Z2
P(W)
GFP
P(Z) Z1
P(X) W
P(Y)
luxR
P(R)

74. Detecting chemical gradients
high threshold detection L u x R O N H
P(lux) X Y Z2
P(W)
GFP
P(Z) Z1
P(X) W
P(Y)
luxR
P(R)

75. Detecting chemical gradients
protein Z determines range L u x R O N H
P(lux) X Y Z2
P(W)
GFP
P(Z) Z1
P(X) W
P(Y)
luxR
P(R)

76. Detecting chemical gradients
negating combiner L u x R O N H
P(lux) X Y Z2
P(W)
GFP
P(Z) Z1
P(X) W
P(Y)
luxR
P(R)

77. Engineering circuit characteristics
HSL-mid: the midpoint where GFP has the highest concentration
HSL-width: the range where GFP is above 0.3uM
Draw a figure explaining about hsl-width and hsl-mid
HSL-mid
0.3
HSL-width