Transcript
Synthetic Gene Networks yThat 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
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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 mechanismI’m a cell I’ve got a memoryI m a cell. I ve got a memory. Now I remember number 0!
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C ti h iCounting mechanismAdd one, please.
00
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C ti h iCounting mechanism
Ok. Good.
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C ti h iCounting mechanismAdd one, please.
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C ti h iCounting mechanism
Ok. Good.
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“Memory” : concentration of some proteinMemory : concentration of some proteinRegulation : extrinsical chemical stimulius
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Model 1: Riboregulated transcriptional g pcascade(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.
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Model 1: Riboregulated transcriptional g pcascade(RTC), two‐counter
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Model 1: Riboregulated transcriptional g pcascade(RTC), three‐counter
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E i t l lt f RTC tExperimental results of RTC countersTwo counter Three counterTwo‐counter Three‐counter
Shaded areas represent arabinose pulse
l l d h fl l• Experimental results demonstrate that fluorescence increases onlywhen 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.
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P di ti f th th ti l d lPrediction of the mathematical modelTwo 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
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Model 2: Single Invertase Memory g yModule(SIMM), three‐counter
DNA – vector
• After the first arabinose pulse only Flpe protein can express.• Flpe is a site‐specific recombinase. (Site FRT)
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Model 2: Single Invertase Memory g yModule(SIMM), three‐counter
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Model 2: Single Invertase Memory g yModule(SIMM), three‐counter
• After the second arabinose pulse only Cre protein can express.• Cre is a site‐specific recombinase. (Site lox)
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Model 2: Single Invertase Memory g yModule(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
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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.
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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.
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Thank for your attention!f y
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