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Transforming cells into automata

Transforming cells into automata. Genetic Circuit Building Blocks for Cellular Computation (Gardner, Cantor and Collins) Construction of a Genetic Toggle Switch in Escherichia Coli ( Ron Weiss, Basu, Hooshangi, Kalmbach, Karig, Mehreja, Netravali). Input. Result. computation. 01010110001.

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Transforming cells into automata

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  1. Transforming cells into automata Genetic Circuit Building Blocks for Cellular Computation(Gardner, Cantor and Collins) Construction of a Genetic Toggle Switch in Escherichia Coli (Ron Weiss, Basu, Hooshangi, Kalmbach, Karig, Mehreja, Netravali) CS 374, Algorithms in Biology. Florian Buron

  2. Input Result computation 01010110001 Processor, Transistors 01000010001 Protein A, etc Protein B, etc. DNA From biology to automata

  3. Biologic Computers and Robots…

  4. Applications: • Drug and biomaterial production and creation. • Programmed therapeutics. • Embedded intelligence in materials. • Environmental sensing and effecting. • Nanoscale fabrication.

  5. 5’ 3’ Promoter Promoters • Promoters are sequences in the DNA just upstream of transcripts that define the sites of initiation. • The role of the promoter is to attract RNA polymerase to the correct start site so transcription can be initiated. • Repressors or Activators can decrease or increase this attraction.

  6. Basic Inverter

  7. Improved Inverter dimere

  8. Improved Inverter

  9. NAND operator

  10. A => B  not(A) or B  not(A and not(B)) We are kind of done!!! • With the NAND and the negation you can build a computer! • What about the implies operator?

  11. Implies operator

  12. And operator

  13. Toggle switch

  14. Toggle switch

  15. Circuit Design How do we choose the right proteins, inducers, etc? • Rational Design (simulator) • Directed Evolution

  16. Rational design

  17. Rational design

  18. Rational design

  19. Rational design

  20. Directed evolution • Optimizing circuit performance is quite labor intensive… • Advantage of biological system: ability to evolve and be optimized under the pressure of natural and artificial selection. • => directed evolution: random mutation in specific region.

  21. Directed evolution

  22. Directed Evolution • Results: • Evolved mutant adjust the kinetic characteristics of the genetic network to produce the right behavior. • Produce effective amino acid substitution that would have been hard to develop rationally! • Ultimately, combining both approach should lead to the best solutions.

  23. Cell to cell communication

  24. Cell to cell communication

  25. Cell to cell communication

  26. Decoding 2 incoming signals: Decoding 2 incoming signals

  27. Concentration band detector

  28. Conclusion • Ability to program cell to do what we want (command the production of molecules). • Starting to understand and predict such process (simulator). • Directed evolution. • Still one main problems: biology is stochastic (reliability, accuracy, etc.)

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