Transcription networks Biol-68 guest lecture Feb. 23 rd, 2006 [email protected].

Transcription networks

Biol-68 guest lecture

Feb. 23rd, 2006

[email protected]

2/23/06 A. Erives

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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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…

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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)’.

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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

2/23/06 A. ErivesEgyptian obelisks, from -3500 B.C.

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Astronomical calculators

StonehengeMesolithic

-8000 B.C.E.

30 Sarsen stones-2500 to -2000 B.C.E.

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Analytical Engine

Charles BabbageMath 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

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Charles Babbage & Charles Darwin

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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

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Boolean logic

Schematic notation for digital logic gates

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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

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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

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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

EDVAC ENIACElectronic Numerical Integrator and Computer

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von Neumann architecture

Memory Stores both data and instructions

CPUPerforms 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

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A single storage structure to hold both instructions and dataa.k.a. "stored-program

computer"

Separation of storage from the processing unit

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Currently still living in the von Neumann paradigm

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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 data from 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.

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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.

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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

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DNA-based computers

“Hard-wired” information is stored in discrete 2-bit linear format of DNA

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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?

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DNA-based computers

Instructions are stored in DNA.

Data (solution environment) is only partially stored in DNA (Why?)

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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 outputinput integrator

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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

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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

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Transcriptional output can be binary.

Laybourn, Kadonaga.

Threshold phenomena and long-distance activation of transcription by RNA polymerase II.

Science (1992) 257:1682-5.

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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.

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Transcription networks can implement Boolean logic.

Guet, Elowitz, Hsing, Leibler

Combinatorial synthesis of genetic networks.

Science (2002) 296: 1466-70.

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Transcription networks can implement Boolean logic.

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Complex regulation: sum of collection of discrete modules

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Individual modules composed of discrete signatures

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non-von Neumann feature #1

Data is NOT really stored in the DNA.

Only the program (instruction set) is stored in the DNA.


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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


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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


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von Neumann architecture machines: Instructions specify and operate on the information contained at

various data addresses

DNA architecture machines: Data itself 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


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The DNA computer is MASSIVELY PARALLEL because each instruction (gene) can run or be activated at the same time.

(Both architectures can be synchronous or asynchronous.)


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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.


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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

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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)

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Extra magic of DNA

DNA complementarity1) Replication mechanism

2) Mismatch search --> inrease information load

3) Transcript readouts RNA enzymes RNA structural scaffolds

Joyce laboratory, Scripps

1)

2)

3)

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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.


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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?

Transcription networks Biol-68 guest lecture Feb. 23 rd, 2006 [email protected].

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Transcript of Transcription networks Biol-68 guest lecture Feb. 23 rd, 2006 [email protected].