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Transforming Cells into Automata
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  1. Transforming Cells into Automata Michael Fischer Engineering Life: Building a FAB for Biology (D. Baker, G. Church, J. Collins, D. Endy, J. Jacbson, J. Keasling, P. Mordrich, C. Smolke, and R. Weiss) Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing (R. Weiss, S. Basu, A. Kalmbach, D. Karig, and I. Netravali)

  2. Imagine a world where… • Cells can perform computation, communications, and signal processing • Cells can be programmed to detect high blood sugar levels and are able to release insulin • A cell can detect when arsenic is present and clean the toxin from the environment

  3. Electrical and Biological Circuits

  4. Background Florescent Protein DNA Reporter Gene Promoter Protein mRNA RNAp • The promoter is the regulatory region of the DNA upstream of the gene that promotes transcription • The florescent protein is used to determine if the final protein was produced

  5. The NOT Gate (Fundamental Idea) Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  6. The NOT Gate (Fundamental Idea) No input mRNA Output protein Output mRNA Gene RNAp 1 0

  7. The NOT Gate (Fundamental Idea) Input mRNA No output mRNA Input protein Gene RNAp 0 1

  8. NAND Gate X=1 No output mRNA No output mRNA X RNAp RNAp = Y=1 Output=0 Y

  9. NAND Gate X=1 No mRNA X Gene RNAp RNAp = Output=1 Y=0 Gene R

  10. NAND Gate = Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  11. NAND then NOT Gate

  12. The Importance of NAND gates • Like NOR gates, NAND gates can be combined to form any type of logical gate • NOR and NAND gates are the most popular gates in processor design because of their versatility • From this we can build the equivalent of a processor

  13. Inputting Data and Detecting Conditions • Electrically based computers • Biologically based computers Repressors and Inducers Used to detect environmentalconditions

  14. Definitions • Inducer • A small molecule that binds to a specific area of the activator or repressor • Activator • A DNA binding protein that regulates genes by increasing the rate of transcription by attracting RNAp to the promoter • Repressor • A DNA binding protein that regulates genes by decreasing the rate of transcription by repelling RNAp from the promoter Active Inactive

  15. IMPLIES Gate for External Interaction Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  16. The AND Gate Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  17. Cell-to-Cell Communication • Cell-to-cell communication allows cells to send and receive inducers • Quorum-sensing • Allows cells to determine how many other cells there are in the surrounding area • Cells diffuse an inducer that permeates their own membrane. The inducer then permeates surrounding cell membranes. Decisions are then able to be made based on the concentration of the chemical.

  18. Cell-to-Cell Communication • 1. Sender sends out signaling inducer using metabolic pathways • 2. The small molecules diffuse outside the membraneand into the environment. • 3. The signals then diffuses into neighboring cells • 4. Molecule interacts with protein in second cell Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  19. Experimentation Source: Genetic Circuit Building Blocks for Cellular Computation, Communication, and Signal Processing

  20. Ring Oscillator • Protein X repress the expression of Y which does not repress Z • Circuit has to be designed to prevent unintended interactions Px Py Pz Rx Ry Rz X Y Z

  21. Circuit Design • Given a specification how do we get the cell to perform a given action? • Designing the circuit • Rational Design • Directed Evolution • Choosing the right proteins • Preventing interactions • Creating building blocks

  22. Rational Design (Bottom up) • Rational design is used for current electronics • Use knowledge of how other systems work • Why it is hard for biological systems • Working with a non-deterministic system • Interactions are difficult to predict • Timing is hard to get accurate • Accurately modeling noise in the system

  23. Rational Design • Once a design has been modeled it needs to be tested • Goal is to be able to print long pieces of DNA quickly and reliably

  24. Constructing a nucleotide sequence • Repeating this cycle makes it possible to synthesize a nucleotide with an error rate of 1 in 100 • Simple network: in the thousands • Small Genome: in the millions

  25. Comparison • Trillionfold performance difference

  26. Divide and Conquer • Use a microarray • 1,000,000 dots/cm2 • Cutable linkers are used to piece together small chains, called oligos • Designed so smaller pieces can overlap with each other to form longer DNA constructs Microarray

  27. Error Correcting • Also print the complement to the specified strand to see if they pair up correctly • Release and redo any pair that did not form correctly • Improves accuracy to 1 error in 1,300 bases

  28. Directed Evolution (Black box) • Creating an artificial Darwinian Environment, with an artificial selective force • “natural selection of those variants best-suited for their environment” • Create large libraries that have known behaviors • Mutate and recombined to create larger libraries Screen the outputted proteins for desirable characteristics and continue to mutate them until a desired result is found. Directed Evolution for star shape

  29. Conclusion • Main Goal: To be able to run computation inside cells • To create artificial networks that can be modified and extended • Areas of Current Research • Expanding out the directed evolution libraries • Learning how to create building blocks for circuits • Perfecting cell-to-cell communications • Areas of future Research • Correctly modeling behavior of synthetic gene networks • Reliability • Accuracy