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AI - The art and science of making computers do interesting things that

are not in their nature.

• Introduction

• Biological Computing and the Systematic Engineering of Biological Systems

  • From Etch a Sketch to Bact a Sketch at iGEM
  • E. Coli edge detectors
  • "They don't really conjugate one at a time. They can go, but they can't stop."
  • Silicomimetic Noughts and Crosses in DNA
  • E. Coli edge detectors (2)
  • Self-defence in the gut: a bacterial control system
  • "Die Amerikaner haben einen wunderschönen, lebenden Lichtsensor entwickelt"
  • Genetic inverters, NOR gates and binary counters
  • The Operon Model
  • Biobricks: standardised modular engineering of biological systems
  • Plasmids and restriction enzymes
  • Abstraction hierarchies and modularity
  • Wet, wet, wet
  • How the biologist should comprehend the radio
  • Links

• Blood Music
• Evolving Computation in Bacterial Collectives

• Links

• Snippets

Introduction

Just before writing this month's issue, I

happened to tune into a BBC Radio 4

programme about a man who trains birds

to act. He subverts the imprinting instinct that makes them

treat the first moving object seen after hatching as a parent, and

even gets them to fly in formation beind an ultralight aircraft.

I missed the name, but a Web search found

quite a few candidates, such as the

Patuxent

Wildlife Center Crane Migrators.

At about the same time, I was reading a new book entitled

The

Gecko's Foot by

Peter Forbes. It's all about understanding and

imitating biological structures for use in

engineering. Structures like the

exquisitely multi-levelled compliance of the gecko's

foot, which is inspiring a sticky tape

- not Spiderman but Geckoman - and synthetic

gecko hairs for

ultra-mobile robots.

Engineers show as much ingenuity in exploiting

evolution's products as evolution did in inventing them.

That, on a microscopic scale, is the subject of this month's

issue: bacterial and genetic computation, and the

systematic engineering of microbiological systems.

I look at some of the projects built for the

intercollegiate Genetically Engineered Machine

competition or iGEM, including such explicitly computational

systems as a

binary counter developed by the

Eidgenössische Technische Hochschule Zürich which

runs on gene expression

regulated by zinc finger proteins.

If this seems a long way from AI, think how far we've come since

the

first transistor

amplifier or

J-K flip-flop. In fact, the

Zürich team's counter uses

a genetic J-K flip-flop. Other teams are reverse-engineering

E. Coli bacteria, and some are connecting E. Coli

control systems to bits taken from other

organisms. There's

research on programming noisy and unreliable

biological systems, and on new concepts

of biological modularity. It's probably not long before

roboticists test-run their favourite

control paradigms in an E. Coli chassis.

I shan't explain how to build a bacterial

Prolog interpreter - come back in ten years' time for

that. But I shall exhibit some of iGEM's computational

achievements, explain a bit of the background, and

show how biology is becoming computing. Or, as two of

the subject's

researchers write:

we don't fix

radios by shooting randomly at their components; and we don't

build microprocessors from chunks of metal and silicon found

lying about the countryside.

Biological Computing and the Systematic Engineering of Biological Systems

From Etch a Sketch to Bact a Sketch at iGEM

Etch a Sketch® is a toy many will

recall from childhood Christmases. It's a small red plastic case with a grey

screen. Two knobs on the case drag a stylus vertically or horizontally

along the screen's backside,

scraping

off the fine aluminium powder with which it's coated. Shaking the case

recoats the screen and erases your picture; and

when you're 5 and find one in your stocking, it's magic.

Bact a

Sketch is also an erasable drawing screen; but not one to

give the average child. You draw using a UV pen, erase with heat,

and the drawing surface is bacteria.

Bact a Sketch - also known as

BioSketch - was Harvard's

entry for

iGEM,

the intercollegiate

Genetically Engineered Machine competition.

iGEM is an annual event: it

began in 2003 with an MIT course in synthetic genetic systems,

and preparations for 2006 are already

underway. The idea behind it is to take advantage of

the increasing capacity for bulk DNA synthesis,

which is doubling every 18 months in a biochemical version of

Moore's Law.

Competitors have to

design,

build, and characterise genetically encoded machines,

going from idea to design to DNA to cell

in 3 months. Its organisers hope that we'll also

learn a lot about how to engineer biological systems. As

the University of Texas at Austin team

say

in their iGEM wiki page:

This year we have several project plans which,

if they don't self-assemble into green goo and

eat the molecular biology building, should be fairly

amusing. Being at heart a bunch of hackers, we believe

that the greatest contribution to the field will come

from actual experiments and thus we are plowing ahead

with our experiments while modeling our system.

E. Coli edge detectors

For this feature, I've done a bit of reading on

iGEM and the biological background of its and other projects

in computational biology,

in the hope that I can explain something of their

workings to non-biologists. I'm not a biologist myself,

so can't claim too much professional knowledge, but

I have tried to check the material carefully. Links are

ones that seemed useful, interesting and

authoritative: I won't guarantee that they are always the

best for their topic.

With that caveat, let's continue with another iGEM entry: a

bacterial

edge detector

from the Universities of Texas at Austin and

California in San Francisco.

In image processing,

edge detection locates boundaries between areas

of different light intensity in an image,

possibly indicating the edges of objects.

It's a well-known task; but

most edge detection algorithms do not run on

massively parallel E. Coli.

On the way to their edge-detector, Austin/UCSF

built the world's first bacterial

camera, using a genetically engineered

light-to-pigment-converter which

led one

expert to say "Die

Amerikaner haben einen wunderschönen, lebenden Lichtsensor entwickelt" -

"the Americans have developed a wonderful living light sensor".

Pictures taken with the camera, including

a portrait of co-adviser Dr. Andy "Escherichia" Ellington,

can be seen on an Austin

photos

page.

The bacterial camera is way too slow to replace

conventional film, but that's not its

purpose. If we can program bacteria to

lay down opaque pigment when stimulated by

light, we should also be able to make them deposit

polymers or metals:

bacterial

microlithography, and note that the

bacterial camera has a resolution of about

100 megapixels per square inch. Also, the stimulus

need not be light: we might program bacteria to

detect

TNT

from buried landmines,

indicating its presence by synthesising a fluorescent protein.

Or, following the seven-segment display designed by

Davidson College

Synth-Aces, which takes one of its inputs from

a caffeine detector, how about a bacterial probe you could stick into

your morning coffee to see whether the cappuccino

machine has dispensed a sufficient dose to wake you

up for the day -

a caffeine thermometer?

"They don't really conjugate one at a time. They can go, but they can't stop."

Surveying the Web, I found a

wealth of material about

iGEM. I've already mentioned the iGEM wiki.

Its contents include a list of awards

given at the iGEM Jamboree.

We can all have nightmares about what

the Best Show Must Go On Moment might have been like, and

sympathise with whoever had to deliver

the Best Honest Answer. The Best Analogy might well

have helped me write this feature; and

I would like to know how ETH - the Zürich team who I

mentioned in the Introduction -

won the George W. Bush

Geography award, MIT the Least Transportable Visual

Aid, and Oklahoma the Best "Hail Mary" Cloning.

Pennsylvania

State University won

Best New Sport. Their

wiki page says that this arose from the idea

of a "bacterial maze," in which bacteria

would use logic to find their way through a microfabricated labyrinth.

That seemed too difficult, so Penn State linearized the the concept and

added transfer of a signal, naming the result

a bacterial relay race.

Some iGEM projects are written up in Nature's

24th November feature

Synthetic

biology: Designs on life. The feature describes

Berkeley's entry:

a new way for cells to communicate, important

because it would enable us to build complex control

circuits that span groups of cells, instead of relying on the

simple intracellular gene circuits I explain

later on. Bacteria exchange genetic information

by temporarily linking cells and transferring

small rings of DNA called plasmids, a process known as

conjugation. To quote Nature:

The group managed to trigger the

conjugation response with synthetic circuits. But the

bacteria turned out to be so eager to join up that they

did so in huge bunches - and once they did, it was hard to

separate them. "They don't really

conjugate one at a time," said team spokeswoman Melissa Li.

"They can go, but they can't stop."

Silicomimetic Noughts and Crosses in DNA

Where will synthetic biology lead? Milan

Stojanovic, one of the researchers who devised MAYA,

a

Noughts-and-Crosses-playing

automaton based on DNA logic gates, is quoted as saying that

we

might add logic to engineered

cells, programming them with

rules such as

  Release a tumor-toxic molecule 

  IF you detect tumor indicator molecule A 

             OR tumor indicator molecule B.

Even if we discover that biology is too noisy and unreliable to carry

out long chains of logical calculations, these simple

rules could be used in biological devices which seek and

destroy medical disorders such as tumours.

Incidentally, MAYA's logic gates

work differently from the genetic control

used by most of the iGEM-ers. MAYA's

gates are single molecules which take short strands of DNA as input. These

strands

bind to matching regions on the gates, causing

part of the gate molecule known as a "stem-loop structure"

to change its conformation from a hairpin shape

to something more open. The gates also

contain a catalytic region. In YES gates, for example,

this is blocked by its neighbouring closed stem-loop.

When a DNA strand binds to the loop and opens it,

the loop moves away from the catalytic region, which

can than perform the function

for which evolution designed it. It cuts strands

of RNA to which are attached both a fluorescent region

and a quencher. These separate, causing the fluorescent

region to emit light, hence to be a visible signal of the

gate's output.

The entire system is explained in the Nature

papers cited in my links,

which also tell how the

Noughts and Crosses strategy was encoded in logic and

then implemented in DNA. As

Nature's

Playing to win at DNA computation

says:

The effort required to

assemble such a complex, functional group of molecular

catalysts was extraordinary. Each enzyme had to be designed to

interpret the same set of effectors differently. Effectors that might

form stable secondary structures were excluded using computational methods,

and the multiple deoxyribozymes were all engineered to preclude misfolding.

Effector and enzyme concentrations were then empirically tweaked to differentiate

signal from noise, and any designs that displayed nondigital behavior or

cross-reactivity were further modified or replaced. Ultimately, these arduous

efforts culminated in a tour-de-force implementation that included 23 different

deoxyribozymes operating simultaneously in

9 different wells with 8 different possible oligonucleotide effectors.

E. Coli edge detectors (2)

What I now want to do is to describe the

bacterial edge detector

in some detail, in order to display

the variety

of abstraction levels with which biological engineers have to

cope. This will lead on to an account

of Biobricks, an

important system for standardised modular

assembly of biological systems (including computational systems).

Let's start at the top level with the edge detection logic.

As I mentioned

earlier,

it was the Universities of Texas at

Austin and California in San Francisco who

entered the edge-detector to iGEM. However, I've

not found an explanation of the logic on their

pages, while ETH

do explain

in a Wiki page how it

might work.

Imagine a "lawn" of light-sensitive bacteria.

If illuminated, a bacterium synthesises protein L;

if in the dark, it synthesises D. The bacteria

are in a moist culture medium, through which L and

D will diffuse. However, we'll assume they

diffuse slowly (or break down rapidly), so don't travel very far.

This means the concentration of L and D will vary across the image,

both being present only near a light-dark

boundary. If we have then engineered the bacteria to

synthesis an opaque pigment when L and

D are both present, the bacteria will "paint" a picture

of the boundaries.

Self-defence in the gut: a bacterial control system

Next, I need go down some levels and

reverse-engineer an E. Coli.

Living in the gut, this bacterium has

no need to sense light. To make it do so,

Austin used a light sensor engineered

by Anselm Levskaya at UCSF's Voigt Lab and

hooked into the back end

of a sensing

mechanism that E. Coli does have

and that I'll now explain.

The E. Coli cell is

double-walled.

The cellular contents or cytoplasm is

enclosed by an inner membrane which

is surrounded by an outer membrane. The space

in between the membranes is called the periplasm ("peri-"

is Greek for "around", as in perimeter

and peripatetic). Of the many proteins

in E. Coli's inner membrane are

two conventionally written as OmpF and

OmpC. These are "porins" or pore proteins:

they have open spaces through which

small molecules such as waste products and nutrients

can diffuse. OmpC has

small pores; OmpF has big pores.

Threaded through the inner membrane are

molecules of a "transmembrane" protein

named EnvZ.

When the end on the outer or periplasmic

side finds itself in a high concentration

of dissolved substances, the protein

changes its electronic structure. As a result,

the end on the inner or cytoplasmic side

of the inner membrane releases a phosphate group,

transferring it to another protein

known as OmpR. This

is one example of a classic type of bacterial control

system, in which stimulation of one protein

adds a phosphate to a second protein,

which may in turn propagate the signal further.

We may, if we want, think of the

first protein as a sensor, and say that it

detects a signal.

In the case of our E. Coli EnvZ-OmpR system, OmpR

that has gained a phosphate can then react with

a control area on the gene for our small-pore protein, OmpC.

This switches on, or "expresses", the gene, causing

synthesis of OmpC to begin. And via another switching

mechanism, the phosphorylated OmpR

turns off the gene for OmpF. The effect is that

E. Coli responds to

a high solute concentration by rebuilding its cell

membrane to have smaller pores.

In general, despite the handicap of not having a nervous

system, bacteria have evolved sophisticated

control systems for regulating their genes and hence

synthesis of proteins, thus adapting to

changes in dissolved chemicals, heat, and all

kinds of other conditions.

Other organisms have

such systems too: cells whose DNA is neatly

packaged into a cell nucleus

(eukaryotes) have

more, and more complex, systems than cells

that, like bacteria

have no nucleus (prokaryotes). However,

bacteria are quite versatile enough, and

provide a rich library of parts and

systems for us to hack.

"Die Amerikaner haben einen wunderschönen, lebenden Lichtsensor entwickelt"

It's the

E. Coli control system described above

that Austin and UCSF used as an intermediary

between light and pigment production.

Their light sensor was

constructed

by using part of a "phytochrome" taken from the

photosynthetic blue-green algae

Synechocystis.

The phytochrome is a protein. Functionally speaking, it resembles

the EnvZ protein described above,

in that the front end is a sensor, and the

back end passes on a signal

to other control pathways. But

unlike EnvZ, the front end senses light,

and the back end signal doesn't

affect gene activity as EnvZ does.

Solution:

design a new, chimaeric, protein whose front end is

the algae's light sensor, and whose

back end is that of EnvZ. Then

code this up as a gene, and then insert that into

the E. Coli.

In such engineering, there is always a

risk that the final system won't work. For

example, suppose the light-sensitive protein

had ended up in the middle of the cell rather than

in the cell membrane, it wouldn't have been so effective a light detector.

Or suppose something in the engineered cell had interfered with

an existing control system. You wouldn't want light to shut

down sugar metabolism, for example.

Note also that we're dealing with two entities

here: genes and the proteins they code for. This, plus

the complexities just mentioned, creates

a number of difficulties: difficulties which

synthetic biologists are trying to reduce with

design tools, simulations, and standardised assembly.

We have to know which proteins we want, and if

designing new proteins, we'll probably want to synthesise them

and check they work in our intended environment.

Here, for example, the researchers tested various

possible chimaeras for their sensitivity to

light. These were made by varying the amount of

the algal front-end protein included, because chain

length is known to affect the signalling mechanisms.

Then we have to code the appropriate genes (including their

control regions), and work

out a method of inserting them into the target cell

and its genome. Then, because such methods are never 100% certain,

we must verify experimentally that we now have organisms

containing the new genes; and that they behave as we

want. It's more work than hacking Perl.

It remains to explain how this wonderful

chimaeric light sensor controlled pigment production.

The back end of the sensor, as I've said, is EnvZ. As I've

also said, EnvZ

phosphorylates the OmpR protein, which

affects a control region on the gene for OmpC. Now,

researchers have already designed E. Coli

in which that OmpC control region is fused

to a different gene, one that controls production

of the protein β-galactosidase. And β-galactosidase

reacts with a certain colourless chemical one can

put in the culture medium, producing

an opaque product. Putting all this together, and

skipping lightly over man-months of lab work, we

have our light-to-pigment converter. The logic is simple;

the engineering less so.

Genetic inverters, NOR gates, and binary counters

I can now exhibit a genetic inverter. That's what

E. Coli's OmpR-OmpF/OmpC system is.

Its input is the protein OmpR and its output

is the protein OmpF. If we could find a gene which

produces a third protein, and that

OmpF switches off, we would have another

inverter.

I don't know whether there are in fact

genes repressed by OmpF - evolution

may have used it as a structural unit

only. However, that doesn't matter, because plenty

of genetic inverters do exist. They work in the

same general way, and can be cascaded together.

One serious problem with the above is that

the inverter output is a different

kind of signal from the input,

making system design more complicated.

This is something that

Biobricks

tries to solve.

If we can make an inverter, we should be able to make a NOR gate, just

by giving our genes more than one control region, so that they can

be switched off by any one of several inputs. This is what

ETH

did for their binary counter. They used a family of "zinc finger" proteins

as signals, and designed genes which had several control regions,

each sensitive to a different protein. Then they built a J-K flip-flop

by combining NOR gates.

The Operon Model

It's worth saying that these control regions

are an extremely important concept in biology - so important that

Francis Jacob and Jacques Monod won a Nobel prize for uncovering

their function. There's a nice explanation in

MIT's Biology

Hypertextbook. Essentially, it's a roadblock system.

Molecules of RNA polymerase land on a "promoter region" (labelled

P in the hypertextbook diagrams), latch on to the DNA, and run down

the following gene

initiating the synthesis of protein until they hit a terminator.

However, there may be a "repressor binding site" (labelled O in the hypertextbook)

between the promoter and

the gene to be transcribed. This site can be empty, or it can

have a repressor protein bound to it. In the latter case, the repressor

blocks the RNA polymerase from getting past, hence also blocking the synthesis

of whatever protein the gene encodes. I like to think of the repressor

as a wrestler or security guard, and the RNA polymerase as a little man who

tries to get past and copy the information on the DNA. He can do so if there's no guard; but also -

as the hypertextbook page goes on to explain -

if the guard's girlfriend has turned up and become so intimate

that guard and girl have rolled off the DNA, leaving the repressor seat empty.

Biobricks: standardised modular engineering of biological systems

On the page

Notes on Tom Knight's Talk of

the iGEM wiki, there's the following quote:

initial tremendous frustation in biology. every experiment turned into two

experiments. first, there was the experiment that you wanted to do. second, there was

the experiment that you had to do in order to do the experiment that you

wanted to do? e.g., will this restricition enzyme work with this DNA, or, is it not

methylated, or something else, or something else. Basically, there are too many things to worry about. think about LEGOS.

everything is designed to go together. even the flowers snap together.

"Even the flowers snap together". Replace

"experiment" by "function", and the

experience becomes one familiar to every

programmer. We've developed

tools to solve the problem: standard libraries;

functions whose internal workings are hidden so that

the caller need know only the inputs and outputs;

mutually replaceable modules that you can swap without

damaging the code using them;

abstraction hierarchies. It's

such intellectual tools that

Knight is applying to biology.

He describes how

in his paper Idempotent

Vector Design for Standard Assembly of

Biobricks.

"Vector" has its customary

meaning, a method of carrying DNA into

a cell and incorporating it into the cell's genome.

"Idempotent" is a mathematical term for an

operator whose effect is the same no matter

how many times it's applied. In Knight's

paper, Biobricks's standardised assembly technique

is idempotent because if we use it to

join biological parts A and B into compound part AB,

then we can use the same technique to join

AB with a third part C; and so on, as many times

as needed.

Plasmids and restriction enzymes

This works as follows. As well as the

chromosome containing their main genetic

material, bacteria have evolved a mechanism

for transferring supplementary genetic information

between cells. This consists plasmids - the small rings

of DNA I mentioned

earlier. Genes for

antibiotic resistance are transferred via

plasmids; and plasmids were the

information carriers in Berkeley's

project on Addressable

Bacterial Communication.

There are specific patterns of DNA that,

if occurring in a plasmid, the bacterial cell will recognise

and insert into its genome.

We can subvert this mechanism to insert

engineered DNA into the bacterium,

inducing it to synthesise insulin, say,

or spider silk. It's a standard

technique in genetic engineering.

Suppose now that we have one plasmid containing

DNA sequence A and another containing B, and we

want to make a component plasmid that will

insert AB.

Because plasmids are rings, if we want to

combine their contents, we need to

cut the relevant DNA sequences out

of each, splice them together, and

then reclose the result into a ring,

making sure it still contains the

"natural" sequences by which the

bacterium recognises it. The cutting is

done with

restriction enzymes:

naturally occuring proteins that cut DNA at

specific patterns or "restriction sites".

Biobricks components are plasmids equipped with standard

"connectors": standard restriction sites

that can be relied on regardless of the component. As long as you

cut and join as just described, you'll have another

plasmid with the same "connectors", which can therefore be

used as a component in its own right.

Abstraction hierarchies and modularity

Even with a standard means of joining components, the

components' functions can vary widely. An op-amp

may have the same pin layout as a set of logic gates, but

that doesn't mean one chip can be substituted for the other.

As well as standardised assembly, synthetic biologists

need to develop abstraction hierarchies, so that we

only think about components in as much detail as we need;

and modular implementations of components, so that the

physical embodiment of an abstraction doesn't "leak".

I've already explained how we can build inverters and more

complicated components; this gives us a high level of abstraction

at which to think while avoiding details of implementation. Biobricks

has its own specific systems-devices-parts-DNA

hierarchy; my

links point at others, amongst them

a Biological Network Layer Model based on the

Open Systems

Interconnection Reference Model.

How do we design genetic components so that implementation

details are reliably hidden? In the section

on

inverters, NOR gates and binary counters,

I mentioned the problems associated with using different

proteins as signals in different parts of a circuit.

Biobricks's answer to this is a shift of viewpoint -

think not of proteins as the signal carriers, but

of RNA polymerase. This entails rearranging

the inverter design so that RNA polymerase coming in

expresses a gene which codes for a repressor protein

which acts on a repressor site just "downstream" of

the gene, at the inverter's output. The notion is

explained at the end of the Biobricks

abstraction hierarchy page, where

we learn that PoPS - Polymerase Per Second - may one

day become as important as FLoating point

Operations Per Second and Logical Inferences

Per Second.

Comics fans may prefer the depiction in Drew Endy and Chuck Wadey's

ADVENTURES IN SYNTHETIC BIOLOGY

comic strip. Follow the adventures of Bacteria Buddy, Device Dude, and System Sally as they

solve the problem of protein signal proliferation

and make Bacteria Buddy smile.

Wet, wet, wet

When someone talks about "a system built from

a large number of unreliable components of

limited life", I think of my plumbing.

Or British Rail. Biological computing

systems would qualify too, and researchers are

working on programming and understanding collective

behaviour in single cells and in systems

of communicating cells. I've linked

to a few relevant pages. It's worth remembering

the patterns that Nature manages to

program: zebra stripes, compound eyes, brains,

gecko foot hairs.

There is a huge difference between

biological and silicon computing. As Jonathan

Goler says in a

paper

on the BioJADE design tool,

electronic signals are localised to wires:

biological ones are not. They exist in solution, they

diffuse, and they will turn up far from where you

want them. They are noisy, may be slow, and (at least in bulk)

are not at all digital and sharp. ETH modelled the dynamics of

their gene circuits using

ordinary

differential equations.

A problem of a completely different kind, discussed at the

end of Goler's paper, is security. I don't want

to give anyone ideas, so will just note that there are many things to which

we're not immune. Some people believe

complete openness about synthetic biology will ensure maximum

knowledge and experience is available to reputable

researchers who must counter threats. Others disagree.

How the biologist should comprehend the radio

Let me finish.

According to Yuri Lazebnik as explained by Sharon Begley in the

Wall Street Journal,

this is how a team of biologists would fix a radio:

First, they'd secure a large grant to purchase hundreds of identical working radios.

After describing and classifying scores of components (metal squares, shiny

circles with three legs, etc.), they'd shoot the radios with .22s.

Examining the corpses, the biologists would pick out those that no longer work.

They'd find one radio in which a .22 knocked out a wire and triumphantly declare

they had discovered the Key Component (KC) whose presence is required for normal operation.

But a rival lab would discover a radio in which the .22 left the Key Component

intact but demolished a completely different Crucial Part (CP), silencing the radio.

Moreover, the rivals would demonstrate that the KC isn't so "key" after all; radios can work fine without it.

A clever post-doc then goes on to find a switch which determines

whether it's KC or CP that the radio currently requires. But the biologists still can't fix the radio,

and they haven't really understood it. They lack the intellectual tools -

the equivalent of circuit diagrams and formal languages. Computing and AI

can help provide these. Then, as

Drew Endy writes

in Foundations

for engineering biology:

The refining of

natural parts to produce engineered biological parts may be similar

to nature's use of negative selection against promiscuous, deleterious

molecular interactions within specific cell types, and is analogous to

the processing of physical materials in other engineering disciplines.

For comparison, microprocessors and other electronic systems are

not built directly from chunks of metal and silicon found lying about

the countryside.

We shall one day be able to engineer biological computing systems from

scratch, without having to rummage around inside intestinal

bacteria for

our components.

Links

From Etch a Sketch to Bact a Sketch at iGEM

href="http://www.eecs.harvard.edu/~rad/igem05/">www.eecs.harvard.edu/~rad/igem05/ -

The Harvard team's page for iGEM 2005. Describes

their Biowire and Bact a sketch projects, and links to

more info, including their

href="http://karma.med.harvard.edu/wiki/IGEM_2005">wiki and a href="http://www.eecs.harvard.edu/~rad/igem05/igem05-gazette.pdf">Harvard Gazette feature.

karma.med.harvard.edu/w/images/7/7c/FinalFinalPresentation_2005-11-05.ppt -

Harvard's Power Point presentation, diagramming the

biological implementatation of

BioWire and Bact a sketch (a.k.a BioSketch). Includes some

info on experimental design, and a picture of

Bact a sketch in a nifty blue case.

www.etch-a-

sketch.com/ -

Etch A Sketch®.

href="http://www.howstuffworks.com/question317.htm">www

.howstuffworks.com/question317.htm -

How does an Etch-a-Sketch work?, at

HowStuffWorks.

href="http://parts2.mit.edu/wiki/index.php/Main_Page">parts2.mit.edu/wiki/index.php/Main_Page -

Main page for the iGEM wiki.

href="http://web.mit.edu/endy/www/igem/iGEM.supplement.pdf">web.mit.edu/endy/www/igem/iGEM.supplement.pdf -

Backgound to iGEM 2003 and 2004 and their research

context, including Amorphous Computing, the increasing improvements

in DNA synthesis,

biological modularisation, and how the Biobricks

abstraction hierarchy

is implemented. By Drew Endy, MIT.

href="http://parts2.mit.edu/wiki/index.php/UT_Austin">parts2.mit.edu/wiki/index.php/UT_Austin -

University of Texas at Austin wiki page for iGEM,

from which I took the "at heart a bunch of

hackers" quote.

href="http://www.wired.com/wired/archive/13.01/mit.html">www.wired.com/wired/archive/13.01/mit.html -

Life, Reinvented, by

Oliver MortonPage, Wired, January 2005.

"Proper engineering, by contrast [with DNA bashing], means designing what you want to

make, analyzing the design to be sure it will work, and then building it

from the ground up. And that's what synthetic biology is about: specifying

every bit of DNA that goes into an organism to determine its form and function

in a controlled, predictable way, like etching a microprocessor or building a bridge.

The goal, as Endy puts it, is nothing less than to 'reimplement life in a manner of our choosing.'".

E. Coli edge detectors

www.ftd.de/rd/31875.html -

Produktion im Dunkeln, by

Constanze Böttcher, Financial Times

Deutschland, from where I took the "wonderful

living light-sensor" quote.

www.utexas.edu/opa/media/photos.php -

Austin download page, with some of the bacterial photos.

www.nature.com/nature/journal/v438/n7067/full/nature04405.html -

Synthetic biology: Engineering Escherichia coli to see light,

by Anselm Levskaya,

Aaron Chevalier,

Jeffrey Tabor, Zachary Simpson,

Laura Lavery, Matthew Levy, Eric Davidson,

Alexander Scouras,

Andrew Ellington,

Edward Marcotte and Christopher Voigt.

Nature438, 441-442 (24 November 2005). Explains how

the engineered light sensor works.

N.B.

I've linked to several Nature pages,

but they may not be universally accessible.

Testing them during my final check, I wasn't able to access

them from home - it seems Nature charges for them.

www.rand.org/publications/MR/MR1608/MR1608.appr.pdf -

BIOLOGICAL SYSTEMS (PAPER I), by

Robert Burlage, University of Wisconsin.

RAND report on Microbial Mine Detection.

parts2.mit.edu/wiki/index.php/Davidson -

Davidson College Synth-Aces wiki page for iGEM,

with the 7-segment chemical display.

parts2.mit.edu/wiki/index.php/Jamboree and

parts2.mit.edu/wiki/index.php/IGEM_2005_Awards -

iGEM Jamboree and Awards.

parts2.mit.edu/wiki/index.php?title=Penn_StateProjectDes -

Penn State wiki page for iGEM,

with the bacterial relay race.

parts.mit.edu/wiki/index.php/Berkeley -

Berkeley wiki page,

with Addressable Bacterial Communication.

www.nature.com/nature/journal/v438/n7067/full/438417a.html -

Synthetic

biology: Designs on life,

by Erika Check,

Nature438, 417-418 (24 November 2005). Shortish

feature on iGEM.

Silicomimetic Noughts and Crosses with DNA

href="http://www.nature.com/nbt/journal/v21/n9/full/nbt862.html">www.nature.com/nbt/journal/v21/n9/full/nbt862.html -

A deoxyribozyme-based molecular automaton by

Milan Stojanovic and Darko Stefanovic.

Nature Biotechnology 21, 1069 - 1074 (2003).

href="http://www.trnmag.com/Stories/2003/082703/DNA_plays_tic-tac-toe_082703.html">www.trnmag.com/Stories/2003/082703/DNA_plays_tic-tac-toe_082703.html -

DNA plays tic-tac-toe, by Kimberly Patch.

Technology Research News, August 27/September

3, 2003.

href="http://www.nature.com/nbt/journal/v21/n9/full/nbt0903-1013.html">www.nature.com/nbt/journal/v21/n9/full/nbt0903-1013.html -

Playing to win at DNA computation by

Jeffrey Tabor and Andrew Ellington.

Nature Biotechnology 21, 1013 - 1015 (2003). A

short account of the work, with a diagram showing

moves on the game board and corresponding states of

the logic gates.

www.nature.com/nbt/journal/v23/n11/full/nbt1105-1374.html -

Boolean calculations made easy (for ribozymes),

by Adam A Margolin & Milan N Stojanovic. Online copy of

Nature Biotechnology 23, 1374 - 1376 (2005).

www.ra.informatik.uni-stuttgart.de/~ghermanv/Lehre/Seminar/material/Presentation10/report.pdf -

Logic Gates made with DNA, by

Maria Belen Canadas Ruiz-Perez,

University of Stuttgart.

A paper for

an Innovative Computer Architectures and Concepts Seminar,

2002. A detailed account of the physics and

chemistry behind MAYA-style DNA logic

gates, with diagrams of the molecules

and processes involved.

E. Coli edge detectors (2)

parts2.mit.edu/wiki/index.php/Edge_Detection -

ETH Edge Detection wiki page,

with their ideas on its implementation.

Self-defence in the gut: a bacterial control system

href="http://www.bio.davidson.edu/Courses/Molbio/MolStudents/spring2005/Champaloux/first.html">www.bio.davidson.edu/Courses/Molbio/MolStudents/spring2005/Champaloux/first.html -

Function and Structure of OmpF Porin, by

Paul Champaloux, Davidson College. Includes nice

diagrams of E. Coli's cell membranes and of the

pore protein OmpF.

href="http://nmr.uhnres.utoronto.ca/ikura/1008/calcium/EnvZ/envz.html">nmr.uhnres.utoronto.ca/ikura/1008/calcium/EnvZ/envz.html -

E. coli Histidine Kinase EnvZ, by

Mitsu Ikura,

Department of Medical Biophysics, University of

Toronto. How EnvZ regulates pore size.

href="http://pub.ucsf.edu/magazine/200305/gross.html">pub.ucsf.edu/magazine/200305/gross.html -

Carol Gross: Feeling the Heat, by Mike Mason,

in UCSF Magazine, 2003. The complicated genetic

control systems that E. Coli uses to defend

itself against heat and stress, related to what I've

talked about here.

href="http://web.mit.edu/esgbio/www/cb/prok_euk.html">web.mit.edu/esgbio/www/cb/prok_euk.html -

Characteristics of Prokaryotes and Eukaryotes.

Similarities and differences between these two

fundamental types of cell, from the MIT Biology

Hypertextbook.

Genetic inverters, NOR gates and binary counters

href="http://parts2.mit.edu/wiki/index.php/ETH_Zurich">parts2.mit.edu/wiki/index.php/ETH_Zurich -

ETH's main wiki page, with

details of their genetic counter.

The Operon Model

web.mit.edu/esgbio/www/pge/lac.html -

The Lac Operon. MIT Biology

Hypertextbook page for the Operon Model.

Biobricks: standardised modular engineering of biological systems

href="http://parts2.mit.edu/wiki/index.php/Notes_on_Tom_Knight's_talk">parts2.mit.edu/wiki/index.php/Notes_on_Tom_Knight's_talk -

Notes on Tom Knight's talk wiki page,

including the

"Every experiment turned into two experiments" quote.

href="https://dspace.mit.edu/bitstream/1721.1/21168/1/biobricks.pdf">dspace.mit.edu/bitstream/1721.1/21168/1/biobricks.pdf -

Idempotent Vector Design for Standard Assembly of

Biobricks,

by Tom Knight,

MIT Artifcial Intelligence Laboratory.

Link appears to have died.

parts.mit.edu/ -

MIT Registry of Standard

Biological Parts. There's a nice user interface on the

parts pages, which enables you to display them in a

number of different ways.

href="http://austin.che.name/docs/bbpp.pdf">austin.che.name/docs/bbpp.pdf -

BioBricks++: Simplifying Assembly of Standard DNA

Components, by Austin Che. There's also

Austin's poster BioBricks++: Simplifying

Assembly of Standard DNA Components Mindless

Module Manipulation by Monkeys, href="http://austin.che.name/docs/bbpp_poster.pdf">austin.che.name/docs/bbpp_poster.pdf.

Abstraction hierarchies and modularity

href="http://parts2.mit.edu/r/parts/htdocs/AbstractionH

ierarchy/index.cgi">parts2.mit.edu/r/parts/htdocs/Abstr

actionHierarchy/index.cgi -

Abstraction Hierarchy. Describes the design and

implementation of the BioBricks abstraction hierarchy,

using an inverter as example.

href="http://openwetware.org/wiki/BioBricks_abstraction_hierarchy">openwetware.org/wiki/BioBricks_abstraction_hierarchy -

OpenWetWare discussion on the

Biobricks abstraction hierarchy - the proper

distinction between part, device, and system.

href="http://openwetware.org/wiki/Network_Layer_Model">openwetware.org/wiki/Network_Layer_Model -

OpenWetWare page on a biological

network layer model based on OSI, the

Open Systems Interconnection Reference Model.

href="http://openwetware.org/wiki/Dedicated_systems">openwetware.org/wiki/Dedicated_systems -

OpenWetWare on

biological virtual machines and dedicated systems:

decoupling the function of an engineered biological system from the function of its chassis.

Wet, wet, wet

www.livescience.com/technology/050428_bacteria_computer.html -

Scientists Make Bacteria Behave Like Computers,

by Robert Roy Britt, LiveScience, April 2005.

Short popular feature, with pictures of

programmed bacterial patterns.

web.mit.edu/jakebeal/www/Talks/AC-language-overview.pdf -

Amorphous Computing's

Programming Languages, by

Jacob Beal,

2005. Slideshow presentation on methods and

notations for amorphous computing.

www.eecs.harvard.edu/~rad/ -

Radhika Nagpal, Harvard. Page on Radhika's interests:

programming and understanding robust collective behavior

in biological systems.

dspace-demo.mit.edu/bitstream/1721.2/3328/2/AITR-2004-003.pdf -

BioJADE: A Design and Simulation Tool for Synthetic Biological Systems,

by Jonathan Goler, 2004.

Link appears to have died.

ra.csail.mit.edu/cjt/ProcIEEE-Jan-00.pdf -

An Interactive Learning Environment

for VLSI Design, by

Jonathan Allen and Christopher Terman. The original

Java design toolkit JADE.

How the biologist should comprehend the radio

www.cipic.ucdavis.edu/~dmrocke/papers/Can%20a%20Biologist%20Fix%20a%20Radio.pdf -

Can a biologist fix a radio? Or, what I

learned while studying apoptosis, Yuri Lazebnik.

Compares how a biologist would, and should,

think about systems.

www.mindfully.org/GE/2003/Systems-Biology21feb03.htm -

Biologists' New Approach:

Do Not Shoot the Radio, by

Sharon Begley, Wall Street Journal, 21 February 2003.

www.bio.davidson.edu/courses/synthetic/papers/Synthetic_Foundations.pdf -

Foundations for engineering biology,

by Drew Endy, 2005.

ADVENTURES IN SYNTHETIC BIOLOGY

href="http://openwetware.org/wiki/Adventures">openwetware.org/wiki/Adventures -

ADVENTURES IN SYNTHETIC BIOLOGY,

STARRING: Bacteria Buddy, Device Dude, and System Sally.

OpenWetWare wiki page linking to various implementations

of the comic.

openwetware.org/wiki/Endy:Writing:CCfGDScript -

Drew Endy's script and references for the above.

href="http://openwetware.org/wiki/Endy:Writing:CCfGDThoughts">openwetware.org/wiki/Endy:Writing:CCfGDThoughts -

How the comic was designed: finding good visual analogies.

See also

openwetware.org/wiki/Adventure_Background and

openwetware.org/wiki/Endy:Writing:CCfGDAdvice.

Comics fans will appreciate the

references to Scott McCloud,

www.scottmccloud.com/.

Other

www.princeton.edu/~rweiss/papers/weiss-bridge-2004.pdf -

Challenges and opportunities in Programming Living Cells,

by Ron Weiss.

Online copy of

The Bridge, Winter 2003.

www.ee.princeton.edu/people/Weiss.php -

Ron Weiss's page. "In my research group, we are exploring

mechanisms for harnessing various organisms as computational

substrates and micron-scale robots, and extending their behavior by

embedding biochemical logic circuitry that precisely controls intra- and

inter-cellular processes. This engineering effort of constructing reliable

in-vivo logic circuitry with predictable behavior enables a wide range of

programmed applications. The application areas include drug and biomaterial

manufacturing, programmed therapeutics, embedded intelligence in

materials, environmental sensing and effecting, and nanoscale fabrication."

www.dnahack.com/ -

DNA Hack. "The website for Amateur Genetic Engineering".

syntheticbiology.org/ -

The Synthetic Biology site. "Synthetic Biology refers to

A) the design and construction of new biological parts, devices, and systems.

B) the re-design of existing, natural biological systems for useful purposes."

href="http://openwetware.org/wiki/Main_Page">openwetware.org/wiki/Main_Page -

OpenWetWare.

"OpenWetWare is an effort to promote the sharing of information,

know-how, and wisdom among researchers and groups who are working in

biology & biological engineering. OWW provides a place for labs, individuals, and

groups to organize their own information and collaborate with others easily and efficiently."

www.blueheronbio.com/index.html -

Blue Heron Bio, the company who made many

of the iGEM components. You can

paste your DNA spec into a form and order

it,

say the interesting notes at

interconnected.org/notes/2005/03/etcon/tue_dna.txt.

href="http://openwetware.org/wiki/Flourless_chocolate_cake">openwetware.org/wiki/Flourless_chocolate_cake -

Synthesis anyone can do:

Drew Endy's recipe for flourless chocolate cake.

Links used in this month's introduction

href="http://www.kare11.com/news/news_article.aspx?storyid=72171">www.kare11.com/news/news_article.aspx?storyid=72171

- The Patuxent Whooping Crane Migrators, a feature by

Mark Daly, KARE 11 News.

href="http://books.guardian.co.uk/reviews/scienceandnature/0,6121,1640650,00.html">books.guardian.co.uk/reviews/scienceandnature/0,6121,1640650,00.html

- Guardian review of The Gecko's Foot,

by Georgina Ferry.

www.me.cmu.edu/faculty1/sitti/nano/projects/geckohair/ -

Gecko Hair Manufacture, CMU NanoRobotics Lab.

href="http://www.pbs.org/transistor/album1/addlbios/aylesworth.html">www.pbs.org/transistor/album1/addlbios/aylesworth.html -

Recreating the First Transistor, at PBS.org.

Blood Music

The spookiest moment came when he realised he was doing more than creating

little computers. Once he started the process and

switched on the genetic sequences which could

compound and duplicate the biologic DNA segments,

the cells began to function as autonomous units.

They began to "think" for themselves and

develop more complex "brains".

His first E. Coli mutations

had had the learning capacity

of planarian worms; he had run them through

simple T-mazes, giving sugar rewards.

They had soon outperformed planaria.

The bacteria - lowly prokaryotes - were doing

better than multicellular eukaryotes!

And within months, he had them running more complex

mazes - allowing for scale adjustments - comparable

to those of mice.

...

There, very clearly, were the roughly circular

lymphocytes in which he had invested two years of

his life. They were busy transferring

genetic material to each other through

long, straw-shaped tubes rather like bacterial

pili. Some of the characteristics picked up

during the E. Coli experiments

had stayed with the lymphocytes, just how

he wasn't sure. The mature lymphocytes were

not reproducing by themselves, but they were

busily engaged in an orgy of genetic

exchange.

Every lymphocyte in the sample he was watching

had the potential intellectual capacity of

a rhesus monkey.

From Blood Music,

by Greg Bear. Published

as a short story in 1983; expanded to a novel in 1985.

Evolving Computation in Bacterial Collectives

Coincidentally, given my main feature,

reader Dr. Erach A. Irani sent me a proposal,

developed in collaboration with Dr. Surendra. B. Khadkikar,

for creating computational

bacterial collectives.

The idea is to drive them up the evolutionary

curve by equipping them with carbon nanotubes

or other nanoparticles.

Many will know that

buckyballs,

or buckminsterfullerenes, are

hollow football-shaped

balls composed of 60 carbon atoms linked to form an icosahedron

truncated at the vertices, with chemical

bonding similar to that of graphite.

Carbon atoms can bond into nanotubes as well as nanoballs,

a fact discovered in 1991 by

Sumio Iijima

of NEC Labs

when investigating buckyball synthesis.

Nanotubes can be very small in diameter, only

a few nanometers long, yet up to a millimeter long.

They are also extremely strong.

Erach and Surendra propose to use either these,

or the gold nanoparticles developed by

Dr. Murali Sastry - Google

"gold nanoparticles Murali Sastry" for copious

references on the latter.

The first stage is to breed bacteria in culture

media containing high concentrations of

nanotubes (or other nanoparticles). Many bacteria will doubtless die,

cell membranes pierced Sebastian-like with a forest of nanotube

arrows. Hopefully though, a few will survive

long enough to undergo fission, or at least to

contribute plasmids to neighbouring bacteria;

evolution will then amplify whatever mechanisms were responsible

for their tolerance. It is possible that some might

even come to depend on the nanotubes.

In the next stage, we induce the bacteria to

form colonies, self-organising collectives

containing bacterial "specialists". Each

collective is a blob having specialist

"defensive" bacteria on the outside. We do this

by adding buckyballs to the culture

medium and

mechanically agitating it, in the hope that the

nanotube-rich bacteria will first evolve to defend

themselves against the buckyballs,

deflecting these with the nanotubes; and

then to aggregate and act as symbiotes

organised into colonies. The nanotube-equipped bacteria

will hopefully be on the

outside.

As What being in

a biofilm means to bacteria

explains,

many bacteria do form colonies when

under environmental stress, and colonies can also form between

bacteria of different species.

The plaque on our teeth

is such a colony.

Although not essential to the project,

Erach and Surendra speculate that bacteria in a collective

might evolve to

signal one another via the nanotubes. If a

nanotube inside one bacterium is near a protein

which (for example) gains or loses a phosphate group,

the protein may transfer charge to or

from the nanotube.

Should that

nanotube be touching one in another

bacterium, the latter will also be affected, and could in turn

affect biomolecules in its "owner". Such interactions do exist, as

Charge

Transfer from Adsorbed

Proteins describes, and they're already being

investigated for biosensing: see, for example,

Carbon

Nanotube Transistors for Biosensing Applications.

If they turn out to benefit the collectives in any way,

evolution will probably exploit them.

It's an extreme example of what

Yoram Gerchman and Ron Weiss

talk about in

Teaching

bacteria a new language, namely the

development of novel cell-to-cell signalling methods that

we can then use in biological programming.

Finally, we subject the collectives to increasing

amounts of stress from chemicals and other insults.

Erach and Surendra hope that this will evolve increasingly

complex signalling behaviour, both within

collectives and between them.

If a sufficiently versatile repertoire of

signalling methods evolves, we can then

"program" the colonies by sending

the right signals. This would require the kind of amorphous

computing techniques that I linked to in my

main feature.

It may also be possible, regardless of the signalling

method, to apply regular signal pulses to the collectives

and evolve them to exploit the benefits of synchronous,

rather than asynchronous, signalling.

One possible

application is programming the collectives to

seek and destroy cancerous tumours,

possibly by attacking them with the nanotubes.

Links

href="http://www.labs.nec.co.jp/Eng/innovative/E1/01.html">www.labs.nec.co.jp/Eng/innovative/E1/01.html -

The discovery of carbon nanotubes - Guided by

serendipity, NEC Laboratories. The discoverer of

nanotubes, Sumio Iijima,

on their history and future.

href="http://www.pa.msu.edu/cmp/csc/nanotube.html">www.pa.msu.edu/cmp/csc/nanotube.html -

The Nanotube Site at Michigan State University

Department of Condensed Matter Physics.

www.erc.montana.edu/CBEssentials-SW/bf-basics-99/bbasics-bfcharact.htm -

What being in a biofilm means to bacteria,

introductory page from the Center for Biofilm Engineering. The page

shows a cartoon: one bacterium to another,

I just can't go with the flow anymore. I've been thinking about joining a biofilm.

www.thejcdp.com/issue003/overman/08over.htm -

Biofilm: A New View of Plaque. Short page with

diagrams of dental plaque, including

bacterium-bacterium signalling.

star.tau.ac.il/~inon/baccyber0.html -

On The Origin of Collectives - Bacterial Evolution,

Bacterial Cybernetics Group,

Tel Aviv University. Growth and

pattern formation in

bacterial colonies. The page has

some stunning photos, for which

see also the Gallery

at

star.tau.ac.il/~inon/pictures/pictures.html.

Last updated 2000.

These links and the one below may be dead - I couldn't access them

during my final check, although fragments survive in

Google's cache.

star.tau.ac.il/~inon/wisdom1/preprint.html -

Bacterial Wisdom, Gödel's Theorem

and Creative Genomic Webs

by Eshel Ben-Jacob,

School of Physics and Astronomy,

Tel-Aviv University. Online copy of

a paper published in

Physica A, 248:57-76, 1998.

www.physics.ucla.edu/research/biophysics/pubs/pdf/pub_03.pdf -

Charge

Transfer from Adsorbed

Proteins, by K. Bradley, M. Briman, A. Star and G. Gruner;

Department of Physics at UCLA, and Nanomix Inc., 2003.

www.physics.ucla.edu/research/biophysics/pubs/pdf/conf_paper_01.pdf -

Carbon Nanotube Transistors for Biosensing Applications,

by G. Gruner, Department of Physics at UCLA, and Nanomix Inc.

www.princeton.edu/~rweiss/papers/weiss-pnas-2004.pdf -

Teaching bacteria a new language,

by Yoram Gerchman and Ron Weiss,

Departments of Electrical Engineering and Molecular Biology, Princeton.

PNAS, February 24 2004. Vol. 101, No. 8.

The engineering of novel cell-to-cell signalling mechanisms.

www.geocities.com/erach27/ConsciousMachinePage.html -

Conciousness, conscious bacteria, Gödel's

theorem, and the Turing Machine, by

Erach Irani.

Snippets

"Genetic Programming. Don't worry yet. This still usually just

means programming using the genetic algorithm."

[Not Any More - Ed.]

Glossary entry

for "GP", Stammtisch Beau Fleuve!,

www.plexoft.com/SBF/index.html.

"Evolution is so notorious for producing quirks that the

existence of quirks is a good test for separating evolved

systems from rationally designed systems."

Mark Turner,

The Literary Mind, 1996.

"It is possible that the designs of

natural biological systems are not optimized by evolution for the

purposes of human understanding and engineering."

Foundations for engineering biology,

Drew Endy,

www.bio.davidson.edu/courses/synthetic/papers/Synthetic_Foundations.pdf.

"The goal of the GA-IDS project is to determine whether

it is possible to evolve visualizations of computer network

and computer systems data that make intrusions or anomalies

easier for network or system administrators to detect than existing

visualization schemes. Instead of starting with a preconceived

visualization model, we start with a language for expressing

visualizations and then use genetic

programming to produce increasingly refined visualizations

based on user feedback."

GA-IDS: Genetic Art For Intrusion Detection,

ga-ids.cs.northwestern.edu/.

"The adaptation described below is a classic example of

intricate design in evolution. One wonders how it could have

arisen through random bit flips, as every component of the

code must be in place in order for the algorithm to function.

Yet the code includes a classic mix of apparent intelligent

design, and the chaotic hand of evolution. The optimization

technique is a very clever one invented by humans, yet it is

implemented in a mixed up but functional style that no

human would use (unless perhaps very intoxicated)."

Thomas Ray

writing about his Tierra artificial-life system. Quote taken

from an abstract at

www.talkorigins.org/faqs/tierra.html;

Tierra and related software at

www.his.atr.jp/~ray/tierra/.

"I shall edit this email slowly, so that you can read it when drunk."

King Arthur: [about the inscription on the rock] What does it say, Brother Maynard?

Brother Maynard: It reads, "Here may be found the last words of Joseph of Aramathia. He who is valiant and pure of spirit may find the holy grail in the Castle of Aaauuuggghhh..."

King Arthur: What?

Brother Maynard: "The Castle of Aaaauuuggghhhh"

Sir Bedevere: What is that?

Brother Maynard: He must have died while carving it.

Memorable Quotes from

Monty Python and the Holy Grail,

www.imdb.com/title/tt0071853/quotes.

"The idea of incongruity-resolution has frequently been suggested as an account of many types of joke. However, there is

no precise statement either of this 'theory' nor of its main concepts (incongruity and resolution), and different authors

may disagree on details. We concentrate on two particular variants and attempt to clarify what would be needed to make

these into computational models."

Developing the Incongruity-Resolution Theory,

Graeme Ritchie,

www.csd.abdn.ac.uk/~gritchie/papers/aisb99.pdf.

"More recently the concept of competition among evolving analogies has

been introduced by the exploitation of concurrency of processes, that

is, each tentative analogy is incrementally built by a separate process

which has to compete for resources with other processes which

are attempting to construct alternative analogies."

Analogical Reasoning section, Machine Learning & Knowledge Discovery

Research page, University of Aberdeen,

www.csd.abdn.ac.uk/~pedwards/research/ml_kd.html.

"A PROPOSAL TO CREATE TWO BIODIVERSITY RESERVES: ONE DIGITAL AND ONE ORGANIC."

Thomas Ray,

http://www.his.atr.jp/~ray/pubs/reserves/index.html.

"A processor so small even the bugs are hunchbacked."

Stephen Figgins

on TuxBot Programming with Python,

www.onlamp.com/pub/a/python/2001/03/21/pythonnews.html.

"Unix is like the maritime transit system in an

impoverished country. The ferryboats are dangerous as hell,

offer no protection from the weather and leak like sieves. Every monsoon

season a couple of them capsize and drown all the passengers,

but people still line up for them and crowd aboard."

An analogy of Operating Systems, from Hunzeker,

Paul Vixie,

www.netfunny.com/rhf/jokes/90q3/unixboat.html.

"SegMan is a perceptual substrate that uses computational

vision to 'see' the Microsoft Windows graphical direct-manipulation interface.

SegMan enables other programs to be able to see the graphical interface screen

as a human would see it. This enables programs to interact with Microsoft Windows

as if it were a user sitting at the console instead of relying on low-level

APIs. With SegMan we can create and test more realistic cognitive models of

direct-manipulation interface usage, build AI agents that can reason about

and use the graphical interface, and write scripts and programs that

learn and perform routine tasks in the graphical interface."

Mark O. Riedl and Rob St. Amant,

www.csc.ncsu.edu/faculty/stamant/segman-introduction.html.

"With the built-in Prolog interpreter

in Windows NT (no kidding! it was used for configuration. I don't know if

it's still there) there may be more Prolog systems in use than ever before."

Richard O'Keefe, SWI-Prolog maillist,

gollem.science.uva.nl/SWI-Prolog/mailinglist/archive/old/0501.html.

"Artificial intelligence is not a term generally used at IBM."

Kathleen Keeshen, IBM spokesperson, 1982. Stottler Henke's

Artificial Intelligence Quotations,

www.stottlerhenke.com/ai_general/quotations.htm.

Originally from

The Tumultuous History of the Search for Artificial Intelligence,

Daniel Crevier, 1993.

"Prolog doesn't have assignment statements. This is deeply upsetting

to most programmers. *No* declarative programming language has

achieved popularity."

Richard O'Keefe, ibid.

To iterate is human;

To recurse, divine.

        Programmers' saying.

Pojem rekurzie,

neuron-ai.tuke.sk/~krankill/ui/rekurzie.html.

Where recursion begins,

Sense ends.

        Student saying.

ibid.

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