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Transcription networks

Transcription networks. Biol-39 guest lecture Feb. 26th, 2008 Albert.Erives@Dartmouth.edu. Introduction: transcription networks. This lecture is about transcription networks as computers . This lecture is NOT about using computers as tools for studying transcription networks.

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Transcription networks

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  1. Transcription networks Biol-39 guest lecture Feb. 26th, 2008 Albert.Erives@Dartmouth.edu

  2. Introduction:transcription networks • This lecture is about transcription networks as computers. • This lecture is NOT about using computers as tools for studying transcription networks.

  3. Background:transcription networks • "The Evolution of Gene Regulatory Logic" workshop at the Santa Fe Institute, New Mexico, Jan. 6-8th, 2006. • Currently, much desire to understand DNA systems in a formal theoretical framework of computation. • Let’s explore the analogy…

  4. What is computation? • compute (Oxford English Dictionary) • reckon or calculate (a figure or amount) — DERIVATIVES computable (adjective), computation (noun) — ORIGIN Latin computare, from putare ‘settle (an account)’.

  5. Counting tokens • -9000 B.C.E. to -1500 B.C.E. • ancient near east • Iraq • Iran • Israel • Syria • Turkey • unit clay tokens • hundreds of types

  6. Egyptian obelisks, from -3500 B.C.

  7. Astronomical calculators • Stonehenge • Mesolithic • -8000 B.C.E. • 30 Sarsen stones • -2500 to -2000 B.C.E.

  8. Analytical Engine • Charles Babbage • Math professor at Cambridge • Analytic Engine, 1840’s • Designed to store programs on cards • Work done by mechanical cogs and wheels • Data stored by positions of cogs and wheels

  9. Charles Babbage & Charles Darwin

  10. 1930’s, 1940’s • Electromagnetic relays used instead of gearwheels to build big electromachanical calculators. • Data and programs stored or implemented in different formats in an inflexible way (Harvard architecture). Harvard IBM Mark I “First universal calculator” 1944

  11. Boolean logic Schematic notation for digital logic gates

  12. Boolean logic: addition Incrementor inputs outputs ai ci ci+1 si 0 0 0 0 0 1 0 1 1 0 0 1 1 1 1 0

  13. Internally Stored Modifiable Program Leap of genius: programs should be stored in just the same way as data is stored. • Alan Turing --> Universal Turing Machine • John von Neumann

  14. John von Neumann, 1903-1957 • Hungarian-American mathematician • wrote "The First Draft of a Report on the EDVAC”, 1945 • EDVAC - Electronic Discrete Variable Automatic Computer • EDVACENIACElectronic Numerical Integrator and Computer

  15. von Neumann architecture • Memory • Stores both data and instructions • CPU • Performs the calculations • Control Unit • Controls which operation the CPU performs • Selects the next instruction based on the current instruction and the state of the machine

  16. von Neumann architecture • A single storage structure to hold both instructions and data • a.k.a. "stored-program computer" • Separation of storage from the processing unit

  17. von Neumann architecture • Currently still living in the von Neumann paradigm

  18. Role of the controller unit • Information is stored in a linear string of bits (0’s and 1’s). • Because data and programming instructions are both stored in the computer's main memory, a need arises to distinguish where these pieces of information begin and end. • Von Neumann's control unit is the mechanism used to distinguish datafrom instructions. • A component called the program counter "points" to the address of an instruction's location in memory. • The instruction is then fetched for execution by the processor. • The address of a data’s location in memory is provided by the instruction itself.

  19. Serial execution • The process of fetching and executing instructions is sequential • I.e. instructions are executed in an ordered, sequential fashion, one instruction at a time. • Basic hallmark of von Neumann computer architecture • In contrast, parallel processing architectures execute multiple instructions in tandem. • True parallel processing computers are considered "non-von Neumann architecture" machines.

  20. von Neumann bottleneck • Sequential execution of programming is limited by the speed of executing one instruction at a time by the computer's processor. • Today’s CPUs are faster than the rate at which information can be retrieved from memory • Many fixes for reducing information bottlenecks of von Neumann architectures: • use of cache memory (a smaller, faster memory device) • use of wider data buses to carry information more quickly between memory and the CPU • reduction of wait states, the time the CPU is in suspended processing while waiting for information

  21. DNA-based computers • “Hard-wired” information is stored in discrete 2-bit linear format of DNA

  22. DNA-based computers • Both DNA and von Neumann machines store information in discrete linear formats. • What does DNA information correspond to in von Neumann computers?

  23. DNA-based computers Instructions are stored in DNA. Data (solution environment) is only partially stored in DNA

  24. Bipartite structure of genes • Instructions are encoded in a set of genes, • a.k.a. the genome. • Genes have two architectural components • A passive integrator or sensor of cell state • A transcribed output or state readout transcribed output input integrator

  25. Bipartite structure of genes Genes can be expressed or not expressed in response to a variety of signals. This basic "switch" logic constitutes the basic building block for an infinitely diverse number of seemingly complex biological phenomena. input signals transcribed output input integrator

  26. Bipartite structure of genes We can think of the transcribed portion of the gene as the instruction called by various types of data specified by the input integrator. input signals transcribed output input integrator

  27. Transcriptional output can be binary. Laybourn, Kadonaga. Threshold phenomena and long-distance activation of transcription by RNA polymerase II. Science (1992) 257:1682-5.

  28. Transcriptional output can be binary. Walters, Magis, Fiering, Eidemiller, Scalzo, Groudine, Martin. Transcriptional enhancers act in cis to suppress position-effect variegation. Genes Dev. (1996) 10:185-95.

  29. Transcription networks can implement Boolean logic. Guet, Elowitz, Hsing, Leibler Combinatorial synthesis of genetic networks. Science (2002) 296: 1466-70.

  30. Transcription networks can implement Boolean logic.

  31. Complex regulation: sum of collection of discrete modules

  32. Complex regulation: sum of collection of discrete modules

  33. Complex regulation: sum of collection of discrete modules

  34. Individual modules composed of discrete signatures

  35. non-von Neumann feature #1 • Data is NOT really stored in the DNA. • Only the program (instruction set) is stored in the DNA. transcribed output input integrator

  36. non-von Neumann feature #1 • The input and output data is stored in a common diffusive storage medium • i.e. the cellular environment • Data is is complex, analog, dynamic and diffusive • [ions+/-], [organic chemicals], [RNA], [protein] • phosphorylation states of specific epitopes • methylation states of specific epitopes • acetylation states of specific epitopes • other transcribed output input integrator

  37. non-von Neumann feature #2 • Input data is never transformed into a discrete linear string of bits. • Regulatory modules act as organizational scaffolds for a 3-D protein complex (input) whose formation indicates a specific set of cell state conditions have been met transcribed output input integrator

  38. non-von Neumann feature #3 • von Neumann architecture machines: • Instructionsspecify andoperate on the information contained at various data addresses • DNA architecture machines: • Dataitself induces the operation of various instructions (i.e. genes), which can then act on data. • In a sense, instructions still specify the “addresses” of data, which can access the specific instruction transcribed output input integrator

  39. non-von Neumann feature #4 • The DNA computer is MASSIVELYPARALLEL because each instruction (gene) can run or be activated at the same time. • (Both architectures can be synchronous or asynchronous.) transcribed output input integrator

  40. non-von Neumann feature #5 • Local control of each instruction: • There is NO central control unit. Each gene specifies when, where and how it is to be activated. • I.e. there is NO centralized component containing all the cis-regulatory DNA. Each gene has it’s own physical set of controllers. transcribed output input integrator

  41. Programming in non-von Neumann architectures • Difficult to predict results or engineer through full-design • Probably require a mixture of engineering and evolutionary tinkering • DNA-based machines may be suitable for a different tasks than traditional von Neumann machines • word processing: von Neumann machine • analytical calculators: von Neumann machine • complex dynamical system control: DNA machine

  42. Programming in non-von Neumann architectures • need special programming languages designed for highly parallel, distributed, fault-tolerant systems

  43. Programs run on DNA. • Sense very complex environmental conditions and respond in very complex ways. • Concentration gradient readouts. • All possible Boolean gates. • Specify a discrete number of cells and their cell states. • Specify arbitrary spatial and temporal patterns. • Solve combinatorial problems (traveling salesman) • Break the DES - Data Encryption Standard (1 kg DNA, est.) • Random number generator: • Choose 1 out of 1000 instructions to run (olfactory system) • Choose 1 out of millions of instructions to run (Ig rearrangement system)

  44. Extra magic of DNA • DNA complementarity • Replication mechanism • Mismatch search --> reduce genetic linkage • Transcript readouts • RNA enzymes • RNA structural scaffolds 3) 1) 2) Joyce laboratory, Scripps

  45. Compression of DNA programs • Evidence suggests that all known DNA-based programs are the result of evolutionary mechanisms that favor local optima satisfying both robustness and compression. transcribed output input integrator

  46. Parting questions and thoughts. • All current known DNA computers are highly derived examples of complex evolutionary trajectories. • I.e. an infinitesimally small number of DNA computer programs have been sampled. • Are there things that DNA computers can formally or practically compute or construct that von Neumann computers cannot?

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