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Synthetic Gene Networks y
That CountThat Count
Ari E. Friedland, Timothy K. Lu, Xiao Wang, David Shi, GeorgeAri E. Friedland, Timothy K. Lu, Xiao Wang, David Shi, George 
Church, James J. Collins.
( )SCIENCE 324 (2009) pp. 1199‐1202
A IgolkinaA. Igolkina
1
R i + M ti tiReview + Motivation
• What do we need for a counting? g
Memory + Mechanism
• Natural memory: 
– DNA sequence.
– Level of protein concentration. (Temporary, but could be not very 
short in time)
• There are a lot of stable mechanisms in a cell.
• Steps to biocomputers:
– Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001).
L i l ti AND d OR• Logical operation AND and OR.
– Programmable single‐cell mammalian biocomputers. Nature 487 
(2012).( )
• An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic 
in single mammalian cells.
h ’ ll ? h !• Why can’t cells count? They can!
2
R i + M ti tiReview + Motivation
• What do we need for a counting? g
Memory + Mechanism
• Natural memory: 
– DNA sequence.
– Level of protein concentration. (Temporary, but could be not very 
short in time)
• There are a lot of stable mechanisms in a cell.
• Steps to biocomputers:
– Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001).
L i l ti AND d OR• Logical operation AND and OR.
– Programmable single‐cell mammalian biocomputers. Nature 487 
(2012).( )
• An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic 
in single mammalian cells.
h ’ ll ? h !• Why can’t cells count? They can!
2
R i + M ti tiReview + Motivation
• What do we need for a counting? g
Memory + Mechanism
• Natural memory: 
– DNA sequence.
– Level of protein concentration. (Temporary, but could be not very 
short in time)
• There are a lot of stable mechanisms in a cell.
• Steps to biocomputers:
– Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001).
L i l ti AND d OR• Logical operation AND and OR.
– Programmable single‐cell mammalian biocomputers. Nature 487 
(2012).( )
• An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic 
in single mammalian cells.
h ’ ll ? h !• Why can’t cells count? They can!
2
R i + M ti tiReview + Motivation
• What do we need for a counting? g
Memory + Mechanism
• Natural memory: 
– DNA sequence.
– Level of protein concentration. (Temporary, but could be not very 
short in time)
• There are a lot of stable mechanisms in a cell.
• Steps to biocomputers:
– Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001).
L i l ti AND d OR• Logical operation AND and OR.
– Programmable single‐cell mammalian biocomputers. Nature 487 
(2012).( )
• An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic 
in single mammalian cells.
h ’ ll ? h !• Why can’t cells count? They can!
2
C ti h iCounting mechanism
I’m a cell I’ve got a memoryI m a cell. I ve got a memory. 
Now I remember number 0!
3
C ti h iCounting mechanism
Add one, please.
00
3
C ti h iCounting mechanism
Ok. Good.
11
3
C ti h iCounting mechanism
Add one, please.
11
3
C ti h iCounting mechanism
Ok. Good.
22
“Memory” : concentration of some proteinMemory  : concentration of some protein
Regulation : extrinsical chemical stimulius
3
Model 1: Riboregulated transcriptional g p
cascade(RTC), two‐counter
• The cis‐repressor sequence (cr) is complimentary  with ribosome binding site (RBS).
• A stem‐loop in a secondary structure of RNA prevents binding of ribosome (30S).
• A short noncoding taRNA  binds to the cr; the stem‐loop can’t form; translation is allowed. 
• Only T7 polymerase, which is coded by T7RNAP, can bind to promoter PT7.O y po y e ase, c s coded by , ca b d to p o ote .
• A transcription of taRNA depends on concentration pulses of arabinose.
4
Model 1: Riboregulated transcriptional g p
cascade(RTC), two‐counter
5
Model 1: Riboregulated transcriptional g p
cascade(RTC), three‐counter
6
E i t l lt f RTC tExperimental results of RTC counters
Two counter Three counterTwo‐counter                                                          Three‐counter
Shaded areas represent arabinose pulse
l l d h fl l• Experimental results demonstrate that fluorescence increases only
when all two (or three) arabinose pulses are delivered.
• Based on this results, Ari E. Friedland at el. constructed and analyzed aBased on this results, Ari E. Friedland at el. constructed and analyzed a 
mathematical model.
7
P di ti f th th ti l d lPrediction of the mathematical model
Two counter Maximum of the predicted concentration.Two‐counter 
Three‐counter: searching of
Maximum of the predicted concentration.
Three counter: searching of 
optimal combination 
(interval‐length).
Points: the differencePoints:  the difference 
between model and 
experiment.
Three‐counter
The absolute difference in 
fluorescence after three 
pulses and two
8
Model 2: Single Invertase Memory g y
Module(SIMM), three‐counter
DNA – vector
• After the first arabinose pulse only Flpe protein can express.
• Flpe is a site‐specific recombinase. (Site FRT)
9
Model 2: Single Invertase Memory g y
Module(SIMM), three‐counter
10
Model 2: Single Invertase Memory g y
Module(SIMM), three‐counter
• After the second arabinose pulse only Cre protein can express.
• Cre is a site‐specific recombinase. (Site lox)
11
Model 2: Single Invertase Memory g y
Module(SIMM), three‐counter
Counting cascade.
Result: GFP expresses after 3Result: GFP expresses after 3 
Ara pulses.
The same cascade was built forThe same cascade was built for 
two‐counter
12
Experimental results and Modelling of 
SIMM counters
Fluorescence in SIMM three‐counter aftres 3 pulses. 
GFP fluorescence ratios between the single‐inducer 
DIC h d h l fDIC three‐counter exposed to three pulses of 
arabinose (N) versus two pulses of arabinose (N – 1) 
with varying  arabinose pulse lengths and intervals; 
experimental results are represented by black dots.
13
C l i + F tConclusion + Future
• Imitation of computer ticks.
• Temporary effect.
• Unfortunately cell mechanisms have got• Unfortunately, cell mechanisms have got 
mistakes and this counters too.
• There is a dubious future for biocompurets 
which are based on this counterswhich are based on this counters.
14
Thank for your attention!f y

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Slides 2

  • 1. Synthetic Gene Networks y That CountThat Count Ari E. Friedland, Timothy K. Lu, Xiao Wang, David Shi, GeorgeAri E. Friedland, Timothy K. Lu, Xiao Wang, David Shi, George  Church, James J. Collins. ( )SCIENCE 324 (2009) pp. 1199‐1202 A IgolkinaA. Igolkina 1
  • 2. R i + M ti tiReview + Motivation • What do we need for a counting? g Memory + Mechanism • Natural memory:  – DNA sequence. – Level of protein concentration. (Temporary, but could be not very  short in time) • There are a lot of stable mechanisms in a cell. • Steps to biocomputers: – Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001). L i l ti AND d OR• Logical operation AND and OR. – Programmable single‐cell mammalian biocomputers. Nature 487  (2012).( ) • An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic  in single mammalian cells. h ’ ll ? h !• Why can’t cells count? They can! 2
  • 3. R i + M ti tiReview + Motivation • What do we need for a counting? g Memory + Mechanism • Natural memory:  – DNA sequence. – Level of protein concentration. (Temporary, but could be not very  short in time) • There are a lot of stable mechanisms in a cell. • Steps to biocomputers: – Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001). L i l ti AND d OR• Logical operation AND and OR. – Programmable single‐cell mammalian biocomputers. Nature 487  (2012).( ) • An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic  in single mammalian cells. h ’ ll ? h !• Why can’t cells count? They can! 2
  • 4. R i + M ti tiReview + Motivation • What do we need for a counting? g Memory + Mechanism • Natural memory:  – DNA sequence. – Level of protein concentration. (Temporary, but could be not very  short in time) • There are a lot of stable mechanisms in a cell. • Steps to biocomputers: – Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001). L i l ti AND d OR• Logical operation AND and OR. – Programmable single‐cell mammalian biocomputers. Nature 487  (2012).( ) • An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic  in single mammalian cells. h ’ ll ? h !• Why can’t cells count? They can! 2
  • 5. R i + M ti tiReview + Motivation • What do we need for a counting? g Memory + Mechanism • Natural memory:  – DNA sequence. – Level of protein concentration. (Temporary, but could be not very  short in time) • There are a lot of stable mechanisms in a cell. • Steps to biocomputers: – Whole‐cell biocomputing. TRENDS in Biotechnology 19 (2001). L i l ti AND d OR• Logical operation AND and OR. – Programmable single‐cell mammalian biocomputers. Nature 487  (2012).( ) • An idea of digital computations with NOT, AND, NAND and N‐IMPLY expression logic  in single mammalian cells. h ’ ll ? h !• Why can’t cells count? They can! 2
  • 6. C ti h iCounting mechanism I’m a cell I’ve got a memoryI m a cell. I ve got a memory.  Now I remember number 0! 3
  • 7. C ti h iCounting mechanism Add one, please. 00 3
  • 8. C ti h iCounting mechanism Ok. Good. 11 3
  • 9. C ti h iCounting mechanism Add one, please. 11 3
  • 10. C ti h iCounting mechanism Ok. Good. 22 “Memory” : concentration of some proteinMemory  : concentration of some protein Regulation : extrinsical chemical stimulius 3
  • 11. Model 1: Riboregulated transcriptional g p cascade(RTC), two‐counter • The cis‐repressor sequence (cr) is complimentary  with ribosome binding site (RBS). • A stem‐loop in a secondary structure of RNA prevents binding of ribosome (30S). • A short noncoding taRNA  binds to the cr; the stem‐loop can’t form; translation is allowed.  • Only T7 polymerase, which is coded by T7RNAP, can bind to promoter PT7.O y po y e ase, c s coded by , ca b d to p o ote . • A transcription of taRNA depends on concentration pulses of arabinose. 4
  • 14. E i t l lt f RTC tExperimental results of RTC counters Two counter Three counterTwo‐counter                                                          Three‐counter Shaded areas represent arabinose pulse l l d h fl l• Experimental results demonstrate that fluorescence increases only when all two (or three) arabinose pulses are delivered. • Based on this results, Ari E. Friedland at el. constructed and analyzed aBased on this results, Ari E. Friedland at el. constructed and analyzed a  mathematical model. 7
  • 15. P di ti f th th ti l d lPrediction of the mathematical model Two counter Maximum of the predicted concentration.Two‐counter  Three‐counter: searching of Maximum of the predicted concentration. Three counter: searching of  optimal combination  (interval‐length). Points: the differencePoints:  the difference  between model and  experiment. Three‐counter The absolute difference in  fluorescence after three  pulses and two 8
  • 16. Model 2: Single Invertase Memory g y Module(SIMM), three‐counter DNA – vector • After the first arabinose pulse only Flpe protein can express. • Flpe is a site‐specific recombinase. (Site FRT) 9
  • 18. Model 2: Single Invertase Memory g y Module(SIMM), three‐counter • After the second arabinose pulse only Cre protein can express. • Cre is a site‐specific recombinase. (Site lox) 11
  • 19. Model 2: Single Invertase Memory g y Module(SIMM), three‐counter Counting cascade. Result: GFP expresses after 3Result: GFP expresses after 3  Ara pulses. The same cascade was built forThe same cascade was built for  two‐counter 12
  • 20. Experimental results and Modelling of  SIMM counters Fluorescence in SIMM three‐counter aftres 3 pulses.  GFP fluorescence ratios between the single‐inducer  DIC h d h l fDIC three‐counter exposed to three pulses of  arabinose (N) versus two pulses of arabinose (N – 1)  with varying  arabinose pulse lengths and intervals;  experimental results are represented by black dots. 13
  • 21. C l i + F tConclusion + Future • Imitation of computer ticks. • Temporary effect. • Unfortunately cell mechanisms have got• Unfortunately, cell mechanisms have got  mistakes and this counters too. • There is a dubious future for biocompurets  which are based on this counterswhich are based on this counters. 14
  • 22. Thank for your attention!f y