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“There is thus this completely decisive property of complexity, that there exists a critical size below which the process of synthesis is degenerative, but above which the phenomenon of synthesis, if properly arranged, can become explosive, in other words, where syntheses of automata can proceed in such a manner that each automaton will produce other automata which are more complex and of higher potentialities than itself“. ~ John von Neumann, in his 1949 University of Illinois lectures on the Theory and Organization of Complicated Automata [J. von Neumann, Theory of self-reproducing automata, 1949 Univ. of Illinois Lectures on the Theory and Organization of Complicated Automata, ed. A.W. Burks (University of Illinois Press, Urbana, IL, 1966).].
[…] Analogy is the core of all thinking. – This is the simple but unorthodox premise that Pulitzer Prize-winning author Douglas Hofstadter and French psychologist Emmanuel Sander defend in their new work. Hofstadter has been grappling with the mysteries of human thought for over thirty years. Now, with his trademark wit and special talent for making complex ideas vivid, he has partnered with Sander to put forth a highly novel perspective on cognition. We are constantly faced with a swirling and intermingling multitude of ill-defined situations. Our brain’s job is to try to make sense of this unpredictable, swarming chaos of stimuli. How does it do so? The ceaseless hail of input triggers analogies galore, helping us to pinpoint the essence of what is going on. Often this means the spontaneous evocation of words, sometimes idioms, sometimes the triggering of nameless, long-buried memories.
Why did two-year-old Camille proudly exclaim, “I undressed the banana!”? Why do people who hear a story often blurt out, “Exactly the same thing happened to me!” when it was a completely different event? How do we recognize an aggressive driver from a split-second glance in our rear-view mirror? What in a friend’s remark triggers the offhand reply, “That’s just sour grapes”? What did Albert Einstein see that made him suspect that light consists of particles when a century of research had driven the final nail in the coffin of that long-dead idea? The answer to all these questions, of course, is analogy-making – the meat and potatoes, the heart and soul, the fuel and fire, the gist and the crux, the lifeblood and the wellsprings of thought. Analogy-making, far from happening at rare intervals, occurs at all moments, defining thinking from top to toe, from the tiniest and most fleeting thoughts to the most creative scientific insights.
Like Gödel, Escher, Bach before it, Surfaces and Essences will profoundly enrich our understanding of our own minds. By plunging the reader into an extraordinary variety of colorful situations involving language, thought, and memory, by revealing bit by bit the constantly churning cognitive mechanisms normally completely hidden from view, and by discovering in them one central, invariant core – the incessant, unconscious quest for strong analogical links to past experiences – this book puts forth a radical and deeply surprising new vision of the act of thinking. […] intro to “Surfaces and Essences – Analogy as the fuel and fire of thinking” by Douglas Hofstadter and Emmanuel Sander, Basic Books, NY, 2013 [link] (to be released May 1, 2013).
“Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!” ~ The Red Queen, at “Through the looking-glass, and what Alice found there“, Charles Lutwidge Dogson, 1871.
Your move. Alice was suddenly found in a new strange world. And quickly needed to adapt. As C.L. Dogson (most known as Lewis Carroll) brilliantly puts it, all the running you can do, does not suffices at all. This is a world (Wonderland) with different “physical” laws or “societal norms”. Surprisingly, those patterns appear also to us quite familiar, here, on planet Earth. As an example, the quote above is mainly the paradigm for Biological Co-Evolution, in the form of the Red-Queen effect.
In Wonderland (1st book), Alice follows the white rabbit, which end-ups driving her on this strange habitat, where apparently “normal” “physical” laws do not apply. On this second book however, Alice now needs to overcome a series of great obstacles – structured as phases in a game of chess – in order to become a queen. Though, as she moves on, several other enigmatic personages appear. Punctuated as well as surrounded by circular arguments and logical paradoxes, Alice must keep on, in order to found the “other side of the mirror“.
There are other funny parallel moves, also. The story goes on that Lewis Carroll gave a copy of “Alice in Wonderland” to Queen Victoria who then asked him in return to send her his next book as she fancied the first one. The joke is that the book was (!) … “An Elementary Treatise on Determinants, With Their Application to Simultaneous Linear Equations and Algebraic Equations (link)”. Lewis Carroll then went on to write a follow-on to Alice in Wonderland entitled “Through the Looking-Glass, and what Alice found there” that features a chess board on his first pages, and where chess was used to gave her, Queen Victoria, a glimpse on what Alice explored on this new world.
In fact, the diagram on the first pages contains not only the entire book chapters of this novel as well how Alice moved on. Where basically, each move, moves the reader to a new chapter (see below) representing it. The entire book could be found here in PDF format. Besides the beauty and philosophical value of Dogson‘s novel on itself, and his repercussions on nowadays co-Evolution research as a metaphor, this is much probably the first “chess-literature” diagram ever composed. Now, of course, pieces are not white and black, but instead white and red (note that pieces in c1 – queen – and c6 – king – are white). Lewis Carroll novel, then goes on like this: White pawn (Alice) to play, and win in eleven moves.
However, in order to enter this world you must follow the “rules” of this new world. “Chess” in here is not normal, as Wonderland was not normal to Alice’s eyes. Remember: If you do all do run you could do, you will find yourself at the same place. Better if you could run twice as fast! First Lewis Carroll words on his second book (at the preface / PDF link above) advise us:
(…) As the chess-problem, given on a previous page, has puzzled some of my readers, it may be well to explain that it is correctly worked out, so far as the moves are concerned. The alternation of Red and White is perhaps not so strictly observed as it might be, and the ‘castling’ of the three Queens is merely a way of saying that they entered the palace; but the ‘check’ of the White King at move 6, the capture of the Red Knight at move 7, and the final ‘check-mate’ of the Red King, will be found, by any one who will take the trouble to set the pieces and play the moves as directed, to be strictly in accordance with the laws of the game. (…) Lewis Carroll, Christmas,1896.
Now, the solution, could be delivered in various format languages. But here is one I prefer. It was encoded on classic BASIC, running on a ZX Spectrum emulator. Here is an excerpt:
1750 LET t$=”11. Alice takes Red Queen & wins(checkmate)”: GO SUB 7000 (…)
9000 REM
9001 REM ** ZX SPECTRUM MANUAL Page 96 Chapter 14. **
9002 REM
9004 RESTORE 9000 (…)
9006 LET b=BIN 01111100: LET c=BIN 00111000: LET d=BIN 00010000
9010 FOR n=1 TO 6: READ p$: REM 6 pieces
9020 FOR f=0 TO 7: REM read piece into 8 bytes
9030 READ a: POKE USR p$+f,a
9040 NEXT f
9100 REM bishop
9110 DATA “b”,0,d,BIN 00101000,BIN 01000100
9120 DATA BIN 01101100,c,b,0
9130 REM king
9140 DATA “k”,0,d,c,d
9150 DATA c,BIN 01000100,c,0
9160 REM rook
9170 DATA “r”,0,BIN 01010100,b,c
9180 DATA c,b,b,0
9190 REM queen
9200 DATA “q”,0,BIN 01010100,BIN 00101000,d
9210 DATA BIN 01101100,b,b,0
9220 REM pawn
9230 DATA “p”,0,0,d,c
9240 DATA c,d,b,0
9250 REM knight
9260 DATA “n”,0,d,c,BIN 01111000
9270 DATA BIN 00011000,c,b,0
(…) full code on [link]
This is BASIC-ally Alice’s story …
“I saw them hurrying from either side, and each shade kissed another, without pausing; Each by the briefest society satisfied. (Ants in their dark ranks, meet exactly so, rubbing each other’s noses, to ask perhaps; What luck they’ve had, or which way they should go.)” — Dante, Purgatorio, Canto XXVI.
Video documentary: A 15-minute program produced from February 1949 to April 1952, Kieran’s Kaleidoscope presented its writer and host in his well-acquainted role as the learned and witty guide to the complexities of human knowledge (Production Company: Almanac Films). This is probably the most genuinely entertaining of all the John Kieran‘s Kaleidoscope films. On Ant City (1949) [Internet Archive] produced by Paul F. Moss, the poor ants are anthropomorphized to the nth degree; we even hear the Wedding March when the “queen” and her drone fly away from the nest. Kieran‘s patter has never been more meandering; he sounds like a befuddled uncle narrating home movies. Clumsy but enjoyable.
[…] In conclusion, much elegant work has been done starting from activated mono-nucleotides. However, the prebiotic synthesis of a specific macromolecular sequence does not seem to be at hand, giving us the same problem we have with polypeptide sequences. Since there is no ascertained prebiotic pathway to their synthesis, it may be useful to try to conceive some working hypothesis. In order to do that, I would first like to consider a preliminary question about the proteins we have on our Earth: “Why these proteins … and not other ones?”. Discussing this question can in fact give us some clue as to how orderly sequences might have originated. […] A grain of sand in the Sahara – This is indeed a central question in our world of proteins. How have they been selected out? There is a well-known arithmetic at the basis of this question, (see for example De Duve, 2002) which says that for a polypeptide chain with 100 residues, 20^100 different chains are in principle possible: a number so large that it does not convey any physical meaning. In order to grasp it somewhat, consider that the proteins existing on our planet are of the order of a few thousand billions, let us say around 10^13 (and with all isomers and mutations we may arrive at a few orders of magnitude more). This sounds like a large number. However, the ratio between the possible (say 20^100) and the actual chains (say 10^15) corresponds approximately to the ratio between the radius of the universe and the radius of a hydrogen atom! Or, to use another analogy, nearer to our experience, a ratio many orders of magnitude greater than the ratio between all the grains of sand in the vast Sahara and a single grain. The space outside “our atom”, or our grain of sand, is the space of the “never-born proteins”, the proteins that are not with us – either because they didn’t have the chance to be formed, or because they “came” and were then obliterated. This arithmetic, although trivial, bears an important message: in order to reproduce our proteins we would have to hit the target of that particular grain of sand in the whole Sahara. Christian De Duve, in order to avoid this “sequence paradox” (De Duve, 2002), assumes that all started with short polypeptides – and this is in fact reasonable. However, the theoretically possible total number of long chains does not change if you start with short peptides instead of amino acids. The only way to limit the final number of possible chains would be to assume, for example, that peptide synthesis started only under a particular set of conditions of composition and concentration, thus bringing contingency into the picture. As a corollary, then, this set of proteins born as a product of contingency would have been the one that happened to start life. Probably there is no way of eliminating contingency from the aetiology of our set of proteins. […]
Figure – The ratio between the theoretical number of possible proteins and their actual number is many orders of magnitude greater than the ratio between all sand of the vast Sahara and a single grain of sand (caption on page 69).
[…] The other objection to the numerical meaning suggested by Figure (above) is that the maximum number of proteins is much smaller because a great number of chain configurations are prohibited for energetic reasons. This is reasonable. Let us then assume that 99.9999% of theoretically possible protein chains cannot exist because of energy reasons. This would leave only one protein out of one million, reducing the number of never-born proteins from, say, 10^60 to 10^54. Not a big deal. Of course one could also assume that the total number of energetically allowed proteins is extremely small, no larger than, say, 10^10. This cannot be excluded a priori, but is tantamount to saying that there is something very special about “our” proteins, namely that they are energetically special. Whether or not this is so can be checked experimentally as will be seen later in a research project aimed at this target. The assumption that “our” proteins have something special from the energetic point of view, would correspond to a strict deterministic view that claims that the pathway leading to our proteins was determined, that there was no other possible route. Someone adhering strictly to a biochemical anthropic principle might even say that these proteins are the way they are in order to allow life and the development of mankind on Earth. The contingency view would recite instead the following: if our proteins or nucleic acids have no special properties from the point of view of thermodynamics, then run the tape again and a different “grain of sand” might be produced – one that perhaps would not have supported life. Some may say at this point that proteins derive in any case from nucleic-acid templates – perhaps through a primitive genetic code. However, this is really no argument – it merely shifts the problem of the etiology of peptide chains to etiology of oligonucleotide chains, all arithmetic problems remaining more or less the same. […] pp. 68-70, in Pier Luigi Luisi, “The Emergence of Life: From Chemical Origins to Synthetic Biology“, Cambridge University Press, US, 2006.
Darwin by Peter Greenaway (1993) – Although British director Peter Greenaway is best known for feature films like The Cook, the Thief, His Wife and Her Lover, Prospero’s Books, and The Pillow Book, he has also completed several highly respected projects for television, including this 53-minute exploration (now free) of the life and work of Charles Darwin. Darwin is structured around 18 separate tableaux, each focusing on another chapter in the naturalist’s life, and each consisting of just one long uninterrupted shot. Other than the narrator’s voice-over, there is no dialogue.
“It is very difficult to make good mistakes“, Tim Harford, July 2011.
TED talk (July 2011) by Tim Harford a writer on Economics who studies Complex Systems, exposing a surprising link among the successful ones: they were built through trial and error. In this sparkling talk from TEDGlobal 2011, he asks us to embrace our randomness and start making better mistakes [from TED]. Instead of the God complex, he purposes trial and error, or to be more precise, Genetic Algorithms and Evolutionary Computation (one of those examples over his talk is indeed the evolutionary optimal design of an airplane nozzle).
Now, we may ask, if it’s clear to you from the talk whether the nozzle was computationally designed using evolutionary search as suggested by the imagery, or was the imagery designed to describe the process in the laboratory? … as a colleague ask me the other day over Google plus. A great question, since as I believe it will be not clear to everyone watching that lecture.
Though, it was clear to me from the beginning, for one simple reason. That is a well-know work in the Evolutionary Computation area, done by one of its pioneers, Professor Hans-Paul Schwefel from Germany, in 1974 I believe. Unfortunately, at least to me I must say, Tim Harford did not mentioned the author, neither he mentions over his talk, the entire Evolutionary Computation or Genetic Algorithms area, even if he makes a clear bridge between these concepts and the search for innovation. The optimal nozzle design was in fact produced for the first time, on Schwefel‘s PhD thesis (“Adaptive Mechanismen in der Biologischen Evolution und ihr Einfluß auf die Evolutiongeschwindigkeit“), and he did arrive at this results by using a branch of Evolutionary Computation know as (ES) Evolution Strategies [here is a Wikipedia entry]. The objective was to achieve the maximum thrust and for that some parameters should be adjusted, such as in which point the small aperture should be put between the two entrances. What follows is a rather old video from YouTube on the process:
The animation shows the evolution of a nozzle design since its initial configuration until the final one. After achieving such a design it was a a little difficult understanding why the surprising design was good and a team of physicists and engineers gathered to provide an investigation aiming at devising some explanation for the final nozzle configuration. Schwefel (later on with his German group) also investigated the algorithmic features of Evolution Strategies, what made possible different generalizations such as a surplus of offspring created, the use of non-elitist evolution strategies (the comma selection scheme), and the use of recombination beyond the well known mutation operator to generate the offspring. Here are some related links and papers (link).
Albeit these details … I did enjoyed the talk a lot as well as his quote above. There is still a subtle difference between “trial and error” and “Evolutionary search” even if linked, but when Tim Harford makes a connection between Innovation and Evolutionary Computation, it remembered me back the “actual” (one decade now, perhaps) work of David Goldberg (IlliGAL – Illinois Genetic Algorithms laboratory). Another founding father of the area, now dedicated to innovation, learning, etc… much on these precise lines. Mostly his books, (2002) The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publishers, and (2006) The Entrepreneurial Engineer by Wiley.
Finally, let me add, that there are other beautiful examples of Evolutionary Design. The one I love most – however – (for several reasons, namely the powerful abstract message that is sends out into other conceptual fields) is this: a simple bridge. Enjoy, and for some seconds do think about your own area of work.
Figure – Understanding the Brain as a Computational Network: significant neuronal motifs of size 3. Most over-represented colored motifs of size 3 in the C. elegans complex neuronal network. Green: sensory neuron; blue: motor neuron; red: interneuron. Arrows represent direction that the signal travels between the two cells. (from Adami et al. 2011 [Ref. below])
Abstract: […] Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm’s locomotion. The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available. […] from Qian J, Hintze A, Adami C (2011) Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain, PLoS ONE 6(3): e17013. doi:10.1371/journal.pone.0017013
[…] The role of evolution in producing these patterns is clear, said Adami. “Selection favors those motifs that impart high fitness to the organism, and suppresses those that work against the task at hand.” In this way, the efficient and highly functional motifs (such as the sensory neuron-interneuron-motor neuron motif) are very common in the nervous system, while those that would waste energy and give no benefit to, or even harm, the animal are not found in the network. “Adami and his team have used evolutionary computation to develop hypotheses about the evolution of neural circuits and find, for these nematode worms, that simplicity is the rule,” says George Gilchrist, program director in NSF’s Division of Environmental Biology (in the Directorate for Biological Sciences), which funds BEACON. “By including functional information about each node in the circuit, they have begun decoding the role of natural selection in shaping the architecture of neural circuits.” […] from Danielle J. Whittaker “Understanding the Brain as a Computational Network“, NSF, April 2011.
The following letter entitled “Darwin among the machines“, was sent on June 1863, by Samuel Butler, the novelist (signed here as Cellarius) to the editor of the Press, Christchurch, New Zealand (13 June, 1863). The article was much probably the first to raise the possibility that machines were a kind of “mechanical life” undergoing constant evolution, something that is now happening on the realm of Evolutionary Computation, Evolution Strategies along with other types of Genetic Algorithms, and that eventually machines might supplant humans as the dominant species. What follows are some excerpts. The full letter could be achieved here. It is truly, an historic visionary document:
[…] “Our present business lies with considerations which may somewhat tend to humble our pride and to make us think seriously of the future prospects of the human race. If we revert to the earliest primordial types of mechanical life, to the lever, the wedge, the inclined plane, the screw and the pulley, or (for analogy would lead us one step further) to that one primordial type from which all the mechanical kingdom has been developed. (…) We refer to the question: What sort of creature man’s next successor in the supremacy of the earth is likely to be. We have often heard this debated; but it appears to us that we are ourselves creating our own successors; we are daily adding to the beauty and delicacy of their physical organisation; we are daily giving them greater power and supplying by all sorts of ingenious contrivances that self-regulating, self-acting power which will be to them what intellect has been to the human race. In the course of ages we shall find ourselves the inferior race. Inferior in power, inferior in that moral quality of self-control, we shall look up to them as the acme of all that the best and wisest man can ever dare to aim at. (…) Day by day, however, the machines are gaining ground upon us; day by day we are becoming more subservient to them; more men are daily bound down as slaves to tend them, more men are daily devoting the energies of their whole lives to the development of mechanical life. The upshot is simply a question of time, but that the time will come when the machines will hold the real supremacy over the world and its inhabitants is what no person of a truly philosophic mind can for a moment question. (…) For the present we shall leave this subject, which we present gratis to the members of the Philosophical Society. Should they consent to avail themselves of the vast field which we have pointed out, we shall endeavour to labour in it ourselves at some future and indefinite period.” […]
Drawing (Pedigree of Man, 1879) – Ernst Haeckel‘s “tree of life”, Darwin‘s metaphorical description of the pattern of universal common descent made literal by his greatest popularizer in the German scientific world. This is the English version of Ernst Haeckel‘s tree from the The Evolution of Man (published 1879), one of several depictions of a tree of life by Haeckel. “Man” is at the crown of the tree; for Haeckel, as for many early evolutionists, humans were considered the pinnacle of evolution.
Drawing – “The distinction between “natural” and “artificial” always struck me as somewhat… artificial“, unknown author (source: Abstruse Goose link) via Cesar Reyes | dpr-barcelona, Nov. 2010.
[…] What is nature? Dictionary.com has 17 different definitions. The first four definitions make no room for man in nature. Five is a correlationist universe of appearing phenomena. Six is a Newtonian universe of quantifiable forces. Seven defines nature as opposite to culture. Eight defines nature as the present-at-hand. Nine defines nature through conforming to an innate pre-determined behavior. Nine to fourteen define nature through a norm or original consistency. Fifteen defines nature as barbarism. And lastly, seventeen, nature as the absence of God’s will. The distinct thread running through all these definition is that nature is something Other to human beings or that human beings are in but out of joint with nature and with the natural. […], in Mike’s Avoiding the Void blog, “The Great Pan is dead“: A rebuke of the myth of natural balance” (link).
Nature is language. Can you read it? … mull it over.
Book – Erwin Schrödinger (1944), “What Is Life?” Cambridge: Cambridge University Press, (my edition is from 2002 – book cover above).
“…living matter, while not eluding the “laws of physics” as established up to date, is likely to involve “other laws of physics” hitherto unknown, which however, once they have been revealed, will form just as integral a part of science as the former.“, Erwin Schrödinger (1944), Chapter VI, [1].
[…] The structure of DNA and the genetic code may have alluded us for some time more if Crick had not read Erwin Schrödinger‘s What Is Life? [1,2]. The research lead that Crick got by doing so was how a small set of repeating elements could give rise to a large number of combinatorial products, a mathematical relationship that Schrödinger illustrated using the Morse Code, based on an idea that he had actually got from the visionary work of Max Delbrück. Delbrück, Schrödinger and Crick were physicists with an enthusiasm for tackling the unknown for the natural world. Crick‘s own motivation came directly from reading What Is Life? [3]. It seemed reasonable to make the cross-over as the infant field of biochemistry was bound to be governed by the same chemical and physical laws revealed in other, non-biological, disciplines. This was especially true given the progressive focus of biology on the increasingly small, until an effective convergence of scales in the studies of the biologically relevant on the biologically irrelevant. Hence the justification for Schrödinger‘s unspecific book title. Although some of the notions in the book have been superseded by modern science, this remains a classic, written with great insight and modesty (Schrödinger downplays his potential as a biologist), and is worth the read if only as a portal in to the minds of those luminary workers. By the time Watson and Crick were piecing together the jigsaw that would lead to their grand discovery, the far-reaching potential of Schrödinger‘s code script had been aligned with Chargaff‘s finding of a variable sequence of nucleotide bases, and the stage was set for that immortal terminal sentence, “It has not escaped our notice that the specific pairing we have postulated immediately suggests a possible copying mechanism for the genetic material.” […], Derry, J. F. (2004). Review of What Is Life? By Erwin Schrödinger. Human Nature Review. 4: 124-125.
“This is the first time any synthetic DNA has been in complete control of a cell“, Craig Venter, May 2010 (video below).
“In 1953, when the structure of DNA was determined, there were 53 kilobytes of high-speed electronic storage on planet earth. Two entirely separate forms of code were set on a collision course. Primitive as it may be, we now have one of the long-awaited results.”, George Dyson, May 2010.
On April 9, 2010, 41 days ago, Science Journal receives a manuscript for revision signed by no least than 24 scientists. Then, 7 days ago it was accepted for publication. It was released today, May 20, 2010. And what we are now assisting today, is no less than a pivotal moment in Human history, in fact, a turning-point for the entire planet and it’s life. Entitled “Creation of a Bacterial Cell controlled by a Chemical Synthesized Genome” [4], the paper describes how these 24 scientists have succeeded in developing the first synthetic living cell. Being the ability to design and create new forms of life so extraordinary, that a truly scientific landmark was indeed today realized. That’s -indeed- one small step for synthetic biology, one giant leap for mankind.
The new cell, is in some-ways a code within a code. As science historian George Dyson points out, “from the point of view of technology, a code generated within a digital computer is now self-replicating as the genome of a line of living cells. From the point of view of biology, a code generated by a living organism has been translated into a digital representation for replication, editing, and transmission to other cells.”
First step was to previously made a synthetic bacterial genome, and transplanted the genome of one bacterium into another. Then, both methods were put together in order to create the present synthetic cell, even if only its genome is truly synthetic. By sequencing its genetic code and then using synthesis machines to chemically construct a copy, a different organism could then be form, taking the synthetic chromosome, and transplant it into a recipient cell. As Venter and his team point out, “As soon as this new software goes into the cell, that cell reads that software and converts the new cell into the species specified in that genetic code.”
We code it, and the new cell reads it. It’s anyhow of full interest to follow with caution their final words on the paper [4] (the entire work could be accessed here from where both pictures were depicted):
[…] If the methods described here can be generalized, design, synthesis, assembly, and transplantation of synthetic chromosomes will no longer be a barrier to the progress of synthetic biology. We expect that the cost of DNA synthesis will follow what has happened with DNA sequencing and continue to exponentially decrease. Lower synthesis costs combined with automation will enable broad applications for synthetic genomics. We have been driving the ethical discussion concerning synthetic life from the earliest stages of this work. Assynthetic genomic applications expand, we anticipate that this work will continue to raise philosophical issues that have broad societal and ethical implications. We encourage the continued discourse . […]
Watermarked on the new synthetic cell DNA (embedded) there is a quote from Richard Feynman: “What I can not build I can not understand“. No matter what, from this point on, we should really re-question what Life is?
TED in the field video – Craig Venter unveils synthetic life, May 2010.
Ref. notes: [1] Erwin Schrödinger (1944), “What Is Life?” Cambridge: Cambridge University Press, (novel edition 2002). | [2] Francis Crick (1989) What Mad Pursuit. Penguin. | [3] James Watson (1981) The Double Helix. Weidenfeld and Nicholson. | [4] Daniel G. Gibson, John I. Glass, … Craig Venter et al., (2010), “Creation of a Bacterial Cell Controlled by a Chemically Synthesized Genome“, Science Journal, released and visited on-line on May 20, 2010.
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