|Evolution in the News - November 2008|
|by Do-While Jones|
A collection of articles on DNA computers and cell signaling provides some real insight into how the theory of evolution impedes scientific progress.
Yes, we know Scientific American is just a science tabloid that presents pseudo-science in a sensational manner. We subscribe simply to monitor the outrageous claims they make about the theory of evolution from time to time. That’s why we were shocked that they printed an excellent, informative story about a simple computer that plays tic-tac-toe using synthetic DNA as logic elements. It sounds bizarre, but this isn’t the first time that something like this has been done.
Researchers reported logic gates based on synthetic molecules as long ago as the early 1990s. 1
You might wonder why one would bother to build a computer using DNA. After all, modern silicon-based electronic computers are tiny, powerful, and can do almost everything. Why try to compete with them?
We did not aim, however, to compete with silicon-based computers. Instead, because Stojanovic had just finished a brief stint with a pharmaceutical company, we settled on developing a system that could be useful for making “smart” therapeutic agents, such as drugs that could sense and analyze conditions in a patient and respond appropriately with no human intervention after being injected. For example, one such smart agent might monitor glucose levels in the blood and decide when to release insulin. Thus, our molecular logic gates had to be biocompatible. 2
Using this new science, we have constructed molecular versions of logic gates that can operate in water solution. Our goal in building these DNA-based computing modules is to develop nanoscopic machines that could exist in living organisms, sensing conditions and making decisions based on what they sense, then responding with actions such as releasing medicine or killing specific cells. 3
Their goal is to create chemically-based systems that act like computers in the human body. That’s a pretty ambitious project. One has to work up to that ability step-by-step. So, they started with the same simple program that digital computer programmers wrote more than 50 years ago.
The first known video game, OXO (or Noughts and Crosses, 1952) for the EDSAC computer played perfect games of tic-tac-toe against a human opponent. 4
We have demonstrated some of the abilities of our DNA gates by building automata that play perfect games of tic-tac-toe. The human player adds solutions of DNA strands to signal his or her moves, and the DNA computer responds by lighting up the square it has chosen to take next. Any mistake by the human player will be punished with defeat. Although game playing is a long way from our ultimate goals, it is a good test of how readily the elementary molecular computing modules can be combined in plug-and-play fashion to perform complicated functions, just as the silicon-based gates in modern computers can be wired up to form the complex logic circuits that carry out everything that computers do for us today. 5
Since there are only 76 ways to put X’s and O’s on a 3x3 matrix, it is relatively simple to enumerate all the possibilities, and use a lookup table to see where to move next, and that’s basically what they did. The second version of their tic-tac-toe computer is called MAYA-II.
The sheer size of this automaton made building and testing MAYA-II an enormous challenge. One of us (Macdonald) led the project and trained several high school students to test automata, mostly during summers and on Saturdays. The students checked all 76 games multiple times. They had to make changes in MAYA-II’s design to deal with several problems (and then recheck all the games after each tweak).
Our chief concern going into the project was that some sequences might bind in unintended places. Our computer-modeling tools were not advanced enough to be able to predict such difficulties. In fact, spurious binding was relatively rare. Instead the more serious problem turned out to be individual gates cleaving their substrates at different rates. We (or, rather, our students) had to adjust concentrations and structures to correct for this variability. We also quickly discovered that some gates acted differently within a mixture than they did on their own, necessitating other redesigns. Finally, after three consecutive summers and many Saturdays, through some changes of inputs and many small adjustments of gate sequences and concentrations, our team had a system in which we could clearly distinguish active and inactive gates in all wells, for all the games, reproducibly. 6
So, it is possible to create biologic systems which respond intelligently to external stimuli; but it took more than three summers of intelligent design! Imagine how long it would have taken using random trial and error.
Ironically, at the same time as this Scientific American article came out, Science magazine published a special report on cell signaling. It contained several interesting observations about the biologic computations that occur in living things.
Mammalian species use over 3000 signaling proteins and over 15 second messengers to build hundreds of cell-specific signaling systems. Many of the signaling components have multiple upstream regulators and downstream targets, creating a web of connectivity within and between signaling pathways. The presence of multiple feedback loops in these systems poses a challenge to understanding how receptor inputs control cellular behavior. 7
Signaling proteins operate in complex networks in cells. The networks are wired into long serial chains, and these chains are arrayed in numerous parallel pathways that diverge from common inputs, converge onto intermediate nodes, and diverge again to many different effectors. Signals from the external world that are detected at the cell membrane are transmitted in the plane of the membrane and through the cytoplasm, with feedback and feed-forward loops onto organelles and the nucleus. The upshot of this complex connectivity is the control of outputs as diverse as membrane transport, cell metabolism, protein translation, cell shape and migration, gene transcription, cell cycle, and cell survival. The shear number of signaling proteins and complexity of their connectivity is staggering, and the depictions in textbooks and on glossy posters from chemical companies are as dense and as difficult to decipher as spirographs. 8
It makes the MAYA-II look rather pathetic by comparison. Animal bodies already have many chemical computers that do the kinds of things the authors of the Scientific American article want to do. The specific chemical computer the authors of the Scientific American article want to simulate is called, “the pancreas.” But there are many other, less well known biological computers that control “outputs as diverse as membrane transport, cell metabolism, protein translation, cell shape and migration, gene transcription, cell cycle, and cell survival. The shear number of signaling proteins and complexity of their connectivity is staggering.”
Let’s look at this from an evolutionary perspective, and then from an intelligent design perspective.
If one takes an evolutionary approach, believing that these 3000 signaling proteins arose by chance, then the focus of study will be an analysis of the probabilities necessary to make this happen. Scientists will determine the number of independent variables, the number of ways they can be combined, speculate on the rate at which they can combine, and compute the average time it would take for the right combination to occur. This will necessarily lead to the conclusion that evolution must have been going on for a very, very long time for all these lucky breaks to happen.
Here’s how an evolutionary bias has affected one scientist in particular.
Given a signaling center, one can easily imagine how it can organize the pattern of cell differentiation in its neighborhood. But how does the signaling center itself arise? If we start with a more or less homogeneous field of cells, what internal mechanism can make one region different from another and break the symmetry? 9
Subconsciously, he must realize that this could not have happened by chance. But since there is no other explanation than chance, he imagines that cell differentiation takes place in a neighborhood. The mechanism by which cell differentiation takes place isn’t completely understood. (If it were, there would be no need for stem cell research.) But even so, he imagines that there must be some natural process that arose by chance because it happens. But even given his willingness to imagine the unimaginable, he still can’t imagine how the signaling center arose by chance in the first place. So, he is likely to focus his research by starting with a “more or less homogeneous field of cells” and look for some random process that “can make one region different from another and break the symmetry.”
But, if one believes that life is the product of intelligent design, then the scientist is going to ignore chance and focus on the underlying design philosophy of life. That is, the scientist will seek to understand what processes are taking place, and then seek to understand why those processes exist, and what their purpose is. He isn’t going to waste time trying to find some way that these processes might have arisen by chance.
The theory of evolution hinders scientific progress because it ignores the possibility that life operates as it does for a reason. Sometimes evolutionists claim that creationists cop out by saying, “God did it.” But, in fact, it is the evolutionists who cop out by saying, “There’s no reason for it—it just happened by chance.” If you don’t think there is a reason, then you won’t look for the reason—you just give the credit to luck.
Despite the theory of evolution, science is progressing. Scientists actually are studying cell signaling. But, to keep their sponsors happy, they don’t ever mention that signaling is a form of communication, and communication implies intelligence. There is a reason why data is sent from the sensor to the actuator. There really is a purpose to it, but they hope that never occurs to anyone else.
Scientists aren’t free to talk about purpose because of the political and philosophical implications that result from such a conversation. But it has to be in the back of their minds. It slips out every now and again, as in this summary paragraph.
Nothing can be reengineered unless it was engineered in the first place. The signaling pathways were designed, on purpose! If they really happened by chance in the first place, then it is pointless to study them. Just try random combination after random combination and see what happens. If that’s the way they arose, then more will arise through the same technique.
The MAYA-II computer didn’t figure out how to play tic-tac-toe all by itself. It took a conscious arrangement of biologic components by intelligent designers to achieve a goal. It would be foolish to try to reverse engineer it by examining the probabilities that those components arose and were connected by chance.
Intelligent design is a valid scientific hypothesis. But since it is incompatible with the failing theory of evolution, some people feel it must be suppressed.
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Macdonald, et al., Scientific American, November 2008, “Smart DNA: Programming the Molecule of Life for Work and Play”, pages 84-91
5 Macdonald, et al., Scientific American, November 2008, “Smart DNA: Programming the Molecule of Life for Work and Play”, pages 84-91
7 Brandman and Meyer, Science, 17 October 2008, “Feedback Loops Shape Cellular Signals in Space and Time” pp. 390 - 395
8 Gorostiza1 and Isacoff, Science, 17 October 2008, “Optical Switches for Remote and Noninvasive Control of Cell Signaling” pp. 395 - 399
9 Lewis, Science,17 October 2008, “From Signals to Patterns: Space, Time, and Mathematics in Developmental Biology”, pp. 399 - 403
10 Gorostiza1 and Isacoff, Science, 17 October 2008, “Optical Switches for Remote and Noninvasive Control of Cell Signaling” pp. 395 - 399