A Christian friend asked me, "What are the boundaries between all science theories and the Bible? As a Christian, to me, the Bible is the truth. So what position and conclusion do we give to these theories?"
That's an interesting question, and I'll try to answer it here.
A Christian scientist (not the cult that calls themslves that) realizes that he is examining God's creation. The facts never contradict the Bible, but give glory to God. It is the theories that are sometimes the problem. When the theories are based on non-biblical principles, the Christian should doubt them. So we must know what is the basis for the theories and put biblical principles first.
In my blog, Thinking Outside the Box, I said "Evolutionists don't want to think outside the box of materialism." They like the definition of science as "systematic knowledge of the physical or material world", because materialists believe that everything is material, and that science explains everything.
But there is a broader definition of science: "a study dealing with a body of facts or truths systematically arranged and showing the operation of general laws". Science actually includes immaterial things like information, and abstract mathematical concepts.
And Christians know that there is a spiritual realm that is part of God's creation, but beyond the reach of science. And of course, God is also beyond the reach of science, because He exists outside of His creation. Yet He can reach inside His creation to communicate with His creatures and to intervene in our affairs. The epitome of that was when He made himself a human body in the womb of a virgin, and putting aside His power and glory for a while, indwelt that body as Jesus.
So Christians see science as an incomplete study of God's creation -- we can study some parts in enough detail that we can partly understand and thus enjoy and use.
We realize that science doesn't explain everything. We don't even claim that it potentially could explain everything, given enough time for study. We realize that there are things outside the scope of science, even important things that our Creator has revealed to us.
The Bereans were commended for comparing the apostle Paul's preaching to the Scriptures, to check whether they should believe what he said (Acts 17:10-11). And Christians today should also check Bible teachers against the Bible. Likewise, Christians should check scientific ideas against God's Word, knowing that science is incomplete and developed by fallible men, but the Bible was inspired by God. In fact, scientific theories should also be checked against scientific facts, because some theories are inspired by political or philosophical agendas.
Showing posts with label evolution. Show all posts
Showing posts with label evolution. Show all posts
Wednesday, July 27, 2011
Friday, July 08, 2011
Thinking Outside the Box
Synopsis
First, I'll explain how, I think, the expression "thinking outside the box" originated. Then I'll give two examples of how great strides in math / science / engineering were accomplished by "thinking outside the box". But the most important point that I want to make is that evolutionists are stuck inside the box of materialism because they are afraid to think outside the box. I discuss a vibrating string as an example of the limitations of materialism. Outside the box of materialism (but not outside science) is information theory, and in particular, the structured information of design. Here, the evolutionists are "out of their element".
The Puzzle
I think that the expression "thinking outside the box" was inspired by the following puzzle. You are presented with an array of nine spots arranged as shown below on the left, and are challenged to draw a sequence of four connected straight lines such that they will pass over all of the spots, touching each spot only once. The lines must be connected end-to-end, and are allowed to cross over each other, for example, as shown on the right.
It was reported that the puzzle is difficult for most people because they mentally think of the array of nine spots as defining a square area as depicted in the next diagram, and they assume that the puzzle operates within this area.
The puzzle does not require the lines to stay inside this assumed 'box'. Adding this restriction prevents a solution, because the puzzle solution must go "outside the box", as shown next.
Science and mathematics have grown by "thinking outside the box".
Imaginary Numbers
For example, it was once thought that it was meaningless to speak of the square root of a negative number. It could be proved, for example, that the square root of minus one, if it had a value, could not equal any known numeric value. But if we IMAGINE that it had a value -- call this value 'i' (for Imaginary) -- then, logically, we could compute the square root of any other negative number. The square root of -9 would equal the square root of 9 (that is, 3) times i. So 3i would be an "imaginary" number, while 4 is a "real" number.
But then it was reasoned that we could think of these out-of-the-box numbers as newly discovered numbers rather than "imaginary" numbers. We could even add "real" and "imaginary" numbers to create "complex" numbers. It all made sense (it didn't lead to contradictions), and it opened up a new world of mathematical discovery. At first, it appeared that this branch of mathematics was an abstract, theoretical-only world unrelated to the physical world; but scientists and engineers found that it could precisely describe the behaviour of resonant electronic circuits. Our modern electronic devices could not be designed without the aid of this out-of-the-box mathematics.
Finite Numbers
I'll give one more example from mathematics and engineering, but I'll keep it extremely simple. We are all familiar with doing arithmetic with integers (whole numbers), and we know that we have an infinite supply of integers, because no matter how large an integer we might be given, we can always add one to it and get a larger integer. Wouldn't it be weird if we had a finite supply of numbers, and no matter what arithmetic operation (add, subtract, multiply, or divide) we did with any two of them, the result would be found in our finite supply of numbers? Well, mathemeticians have discovered how to construct a finite set (of any size) of 'numbers' with associated arithmetic operations that operate like this. (They are called finite fields, a kind of finite algebras. Math geeks, see http://mathworld.wolfram.com/Ring.html) The arithmetic is weird, but easy to learn to do in most cases.
Like the "imaginary" numbers, these weird systems of arithmetic seem like mathematical toys or games that bear no resemblance or relationship to the real world. Why don't we stick to normal math that describes how the real world operates? But engineers have used this weird math to construct codes used to detect and correct errors that occur in the communication or storage of digital data. For example, CDs and DVDs would not function without Reed-Solomon error-correction codes, which are based on these "finite fields".
The Box of Materialism
Evolutionists don't want to think outside the box of materialism. One of the definitions of science (from dictionary.com), is "systematic knowledge of the physical or material world gained through observation and experimentation", which limits the scope of science to the material world.
Of course, evolutionists have a problem with the "observation" part, because nobody has observed genetic changes from one kind of creature to a completely different kind (such as dog to horse, rather than dog to another kind of dog). So they mostly content themselves with inferring (by theory) past events from current observations.
And of course, evolutionists also have a problem with the "experimentation" part, because a true experiment requires control over the conditions of the experiment. Their experiments only show things such as that one can breed flies just like one can breed dogs, not major changes of kind. Even by broadening the concept of experiment so that "predictions" can be made and tested regarding past events fails. For example, evolution predicts that there should be millions of intermediate forms ("missing links"), but none are found.
But the evolutionist argues, and rightly so, that the creationist also has similar problems with the "observation" and "experimentation" parts of the definition of science, and thus feels that he is on a 'level playing field' with his game of 'my story is more plausible than your story'. (He considers this story-telling to be "science", but if it weren't based on a theological / philosophical battle, it would be called "science fiction" instead.)
But the evolutionist thinks that the "physical or material world" part of the definition of science works to his advantage, because it rules out the supernatural, which is the essential part of the creationist's "theory". And, after all, his main objective is to rule out God. But is it honest to 'win' an argument by virtue of a definition?
Vibrating String Example
Consider, for example, the vibration of a guitar string. If we know certain characteristics of the string, we can apply the laws of physics by means of a branch of mathematics called differential calculus to determine how the string will vibrate, and the nature of the sound that it will produce. We need to know:
(1) the weight (such as ounces per foot) of the string
(2) the tension (such as pounds of force) of the string
(3) the length (between fixed points: a fret and the bridge) of the string
These, which can all be measured, suffice to compute the 'steady state' vibration of the string. To determine the initial 'transient' component of the vibration that quickly fades before settling into the 'steady state', we would also need to know if the string were plucked or struck, and where on the string. To know the intial amplitude and how quickly the vibration will fade, we would also need to know how hard the string was plucked or struck, and other details.
So by observing (measuring) a guitar string, we can use science to predict precisely what will happen when we pluck the string. But what if we are not in control?
First, we have a 'future' problem. We might observe the string vibrating and make a prediction, only to find that the guitarist (the one in control) stops the vibration, without our permission, before our prediction can be fulfilled.
Second, we have a 'past' problem. If our observation of the string began after the 'transient' component of the vibration has faded, we will have insufficient data to determine when the vibration started, and we will be unable to determine if it were plucked or struck, or were given its inital energy some other way.
Note that our problem, essentially, is not that the guitarist is a material object too complex for us to analyze, but rather that the guitarist has a mind outside of the scope of our observation and control. If the guitarist were a robot, our problem would be difficult, but not impossible.
So what do we learn from this vibrating string parable?
We realize that the size and age of the material world makes nearly all of it beyond the scope of our observation and control. And science that is limited by definition to apply only to the material world is, by definition, limited in its application. Defining this box does not prove that there is nothing outside the box. If there is a Creator outside the box who is ultimately in control, and if you want to be the one in control, you may be inclined to hide inside your box, but you can't make God go away.
A Broader Definition of Science
Dictionary.com gives a broader definition of science, one that precedes the definition that we quoted earlier: "a branch of knowledge or study dealing with a body of facts or truths systematically arranged and showing the operation of general laws: the mathematical sciences." So more generally, science is not limited to material things, but anything that can be studied "systematically" such that it can be explained in terms of "the operation of general laws". For greater clarity, "mathematical sciences" is mentioned, indicating that there should be sufficient precision that the language and methods of mathematics can be applied.
So what is immaterial that can be included in this broader definition of science? Information is immaterial, and is studied systematically and operates in accordance with specific laws with sufficient precision so that the language and methods of mathematics are applied. This area of science consists of information theory and related theories of formal languages, algorithms, etc., and the corresponding applied science consists of the technologies of information storage, communication, and processing.
In a previous blog, ALL Things, I made the case that the material world consists of four interrelated elements: matter, energy, space, and time. Then in a later blog, Is Encoded Information an Essential Part of the Universe?, I made the case that encoded information is an optional fifth element, not required by the laws of physics, but nonetheless present where (and only where) life is present. The reason why information is found only where life is found is that the design paradigm of life is chemistry guided by DNA information, as I explained in the blog, Life is more than chemistry. Without the guiding information, chemistry can only make inorganic molecules. DNA information is needed to make organic molecules, which are much larger and more complex. (Some simple molecules such as certain amino acids are traditionally classified as 'organic' if they are used as components of large organic molecules, but that is like listing raw iron as a machine part, along with the nuts, bolts, and cotter pins.)
Information Outside the Box
Before the functions of DNA and RNA, and the genetic code were discovered, evolutionists could take advantage of the mystery of genetics to tell imaginative stories of how evolution might operate. But these discoveries irreversibly brought information theory into the scientific arena of the creationism / evolutionism debate. Just as someone caught in a lie feels forced to tell new lies or to modify the first lie to maintain credibilty, the evolutionists felt compelled to change their story; and just as a gang of liars are not likely to agree except on their innocence, the evolutionists don't agree except that God is not involved.
Some evolutionists insist that there is no information in DNA and RNA, as though closing their eyes will make the information bogeyman go away. Some insist that information theory is not a valid science (because information is not material). Some claim that information can come from nothing, or from randomness (which is zero information according to information theory), attempting to prove this by equating patterns and information. And some claim that new information can be generated by random re-arrangements of scraps of information, as though it were possible that if you scrambled parts of the Koran long enough, you might end up with the Bible. Even those that admit that information always originates from intelligence deny that God is a plausible source of that intelligence, but prefer an "extraterrestrial" source, replacing the question "How did life information originate on earth?" with the question "How did life information originate on planet X?" (Do I need to explain the fallacy of that logic?)
So the evolutionists that dare to wander outside the box of materialism either flounder like someone diving into water without learning to swim first, or they retreat to the more comfortable zone of materialism.
But that is only the beginning of the problems for the materialists. The information in DNA is not just information, but more specificly, DESIGN information. And here the evolutionists are completely lost in an unfamiliar world. Most of the contributors to the science of intelligent design have a background in the applied science of engineering, because this is familiar territory that they understand.
Structured Information (Top-Down Design) Outside the Box
The key to understanding the evolutionary problem is that design information is structured information. Let me give a simple example to make this clear. In a book, whether fiction or nonfiction, letters are arranged to make words, words arranged to make phrases, phrases arranged to make clauses, clauses arranged to make sentences, sentences arranged to make paragraphs, and paragraphs arranged to make make chapters. Does any author start with letters and play with different sequences to make words, etc., finally making chapters? No, the author starts with an array of related concepts, and starts at some high level of organization and works downward, finally working out the details of how best to arrange a sentence and how to spell the words. Rarely does the author accomplish the final work in one pass, but each revision starts with a new concept at some level and works downward.
Does a designer start with an assortment of parts, like a box of legos, and wonder what he might do with them? No, he starts with a concept, such as using suction to remove household dirt, and designs a vaccuum cleaner "top down", as designers like to say. He may begin with a simple set of features, and add features such as exchangeable attachments, but additions and revisions are always "top down". For example, when he decides that he needs a hose to connect attachments to the vaccuum pump, he first determines its desired properties (lightweight, flexible, does not collapse like a fire hose, etc.) and then works out the details, such as using a wire coil to keep the hose from collapsing.
Unlike the book example, a design typically mixes different technologies. For example, the engineer needs to choose appropriate materials, and so depends on experts in metallurgy, plastics, etc. Or he needs a motor, and orders one meeting his specications designed by a specialist.
Living things, even single-celled organisms, are likewise complex designs, systems made of subsystems that are made of sub-subsystems, etc. And they mix mechanical, chemical, electrical, communication, etc. 'technologies' to acheive coordinated purposes.
So, the evolutionist, in re-telling his story to adapt to the undeniable presence of design, imagines that accidental genetic changes can modify designs to make new designs. But experienced designers recognize this as "bottom-up" design: that is, a foolish, unworkable strategy. Fiddling with the details never makes a truly new design; it only 'tunes up' or adjusts a design.
For example, the first television sets had about a dozen adjustment knobs in front, which was dangerous, because people that had no clue about the internal technology would fiddle with them with disasterous results. It took a while for the engineers to design automatic adjustment mechanisms to replace all of those knobs except for the channel selector and the volume control. But note that a billion adjustment knobs on the television will never suffice to make it function like a cell phone or a vaccuum cleaner.
Complex systems generally require many automatic adjustment mechanisms. And that is exactly what scientists observe in biology. Genetic adjustment (adaptation) is just one category of these mechanisms. So species are designed to adapt to environmental changes, and we can influence the process by breeding (outside intelligence). But breeding dogs to make horses takes a leap of imagination, and supposing that inorganic matter can turn into human beings with no outside intelligence in something less than an eternity takes a enormous leap of faith.
So if and when evolutionists dare to wander outside the box of materialism, they are likely to discover that evolution is a religion, after all, not a science. That is, if they are willing to be honest with themselves.
First, I'll explain how, I think, the expression "thinking outside the box" originated. Then I'll give two examples of how great strides in math / science / engineering were accomplished by "thinking outside the box". But the most important point that I want to make is that evolutionists are stuck inside the box of materialism because they are afraid to think outside the box. I discuss a vibrating string as an example of the limitations of materialism. Outside the box of materialism (but not outside science) is information theory, and in particular, the structured information of design. Here, the evolutionists are "out of their element".
The Puzzle
I think that the expression "thinking outside the box" was inspired by the following puzzle. You are presented with an array of nine spots arranged as shown below on the left, and are challenged to draw a sequence of four connected straight lines such that they will pass over all of the spots, touching each spot only once. The lines must be connected end-to-end, and are allowed to cross over each other, for example, as shown on the right.
It was reported that the puzzle is difficult for most people because they mentally think of the array of nine spots as defining a square area as depicted in the next diagram, and they assume that the puzzle operates within this area.
The puzzle does not require the lines to stay inside this assumed 'box'. Adding this restriction prevents a solution, because the puzzle solution must go "outside the box", as shown next.
Science and mathematics have grown by "thinking outside the box".
Imaginary Numbers
For example, it was once thought that it was meaningless to speak of the square root of a negative number. It could be proved, for example, that the square root of minus one, if it had a value, could not equal any known numeric value. But if we IMAGINE that it had a value -- call this value 'i' (for Imaginary) -- then, logically, we could compute the square root of any other negative number. The square root of -9 would equal the square root of 9 (that is, 3) times i. So 3i would be an "imaginary" number, while 4 is a "real" number.
But then it was reasoned that we could think of these out-of-the-box numbers as newly discovered numbers rather than "imaginary" numbers. We could even add "real" and "imaginary" numbers to create "complex" numbers. It all made sense (it didn't lead to contradictions), and it opened up a new world of mathematical discovery. At first, it appeared that this branch of mathematics was an abstract, theoretical-only world unrelated to the physical world; but scientists and engineers found that it could precisely describe the behaviour of resonant electronic circuits. Our modern electronic devices could not be designed without the aid of this out-of-the-box mathematics.
Finite Numbers
I'll give one more example from mathematics and engineering, but I'll keep it extremely simple. We are all familiar with doing arithmetic with integers (whole numbers), and we know that we have an infinite supply of integers, because no matter how large an integer we might be given, we can always add one to it and get a larger integer. Wouldn't it be weird if we had a finite supply of numbers, and no matter what arithmetic operation (add, subtract, multiply, or divide) we did with any two of them, the result would be found in our finite supply of numbers? Well, mathemeticians have discovered how to construct a finite set (of any size) of 'numbers' with associated arithmetic operations that operate like this. (They are called finite fields, a kind of finite algebras. Math geeks, see http://mathworld.wolfram.com/Ring.html) The arithmetic is weird, but easy to learn to do in most cases.
Like the "imaginary" numbers, these weird systems of arithmetic seem like mathematical toys or games that bear no resemblance or relationship to the real world. Why don't we stick to normal math that describes how the real world operates? But engineers have used this weird math to construct codes used to detect and correct errors that occur in the communication or storage of digital data. For example, CDs and DVDs would not function without Reed-Solomon error-correction codes, which are based on these "finite fields".
The Box of Materialism
Evolutionists don't want to think outside the box of materialism. One of the definitions of science (from dictionary.com), is "systematic knowledge of the physical or material world gained through observation and experimentation", which limits the scope of science to the material world.
Of course, evolutionists have a problem with the "observation" part, because nobody has observed genetic changes from one kind of creature to a completely different kind (such as dog to horse, rather than dog to another kind of dog). So they mostly content themselves with inferring (by theory) past events from current observations.
And of course, evolutionists also have a problem with the "experimentation" part, because a true experiment requires control over the conditions of the experiment. Their experiments only show things such as that one can breed flies just like one can breed dogs, not major changes of kind. Even by broadening the concept of experiment so that "predictions" can be made and tested regarding past events fails. For example, evolution predicts that there should be millions of intermediate forms ("missing links"), but none are found.
But the evolutionist argues, and rightly so, that the creationist also has similar problems with the "observation" and "experimentation" parts of the definition of science, and thus feels that he is on a 'level playing field' with his game of 'my story is more plausible than your story'. (He considers this story-telling to be "science", but if it weren't based on a theological / philosophical battle, it would be called "science fiction" instead.)
But the evolutionist thinks that the "physical or material world" part of the definition of science works to his advantage, because it rules out the supernatural, which is the essential part of the creationist's "theory". And, after all, his main objective is to rule out God. But is it honest to 'win' an argument by virtue of a definition?
Vibrating String Example
Consider, for example, the vibration of a guitar string. If we know certain characteristics of the string, we can apply the laws of physics by means of a branch of mathematics called differential calculus to determine how the string will vibrate, and the nature of the sound that it will produce. We need to know:
(1) the weight (such as ounces per foot) of the string
(2) the tension (such as pounds of force) of the string
(3) the length (between fixed points: a fret and the bridge) of the string
These, which can all be measured, suffice to compute the 'steady state' vibration of the string. To determine the initial 'transient' component of the vibration that quickly fades before settling into the 'steady state', we would also need to know if the string were plucked or struck, and where on the string. To know the intial amplitude and how quickly the vibration will fade, we would also need to know how hard the string was plucked or struck, and other details.
So by observing (measuring) a guitar string, we can use science to predict precisely what will happen when we pluck the string. But what if we are not in control?
First, we have a 'future' problem. We might observe the string vibrating and make a prediction, only to find that the guitarist (the one in control) stops the vibration, without our permission, before our prediction can be fulfilled.
Second, we have a 'past' problem. If our observation of the string began after the 'transient' component of the vibration has faded, we will have insufficient data to determine when the vibration started, and we will be unable to determine if it were plucked or struck, or were given its inital energy some other way.
Note that our problem, essentially, is not that the guitarist is a material object too complex for us to analyze, but rather that the guitarist has a mind outside of the scope of our observation and control. If the guitarist were a robot, our problem would be difficult, but not impossible.
So what do we learn from this vibrating string parable?
We realize that the size and age of the material world makes nearly all of it beyond the scope of our observation and control. And science that is limited by definition to apply only to the material world is, by definition, limited in its application. Defining this box does not prove that there is nothing outside the box. If there is a Creator outside the box who is ultimately in control, and if you want to be the one in control, you may be inclined to hide inside your box, but you can't make God go away.
A Broader Definition of Science
Dictionary.com gives a broader definition of science, one that precedes the definition that we quoted earlier: "a branch of knowledge or study dealing with a body of facts or truths systematically arranged and showing the operation of general laws: the mathematical sciences." So more generally, science is not limited to material things, but anything that can be studied "systematically" such that it can be explained in terms of "the operation of general laws". For greater clarity, "mathematical sciences" is mentioned, indicating that there should be sufficient precision that the language and methods of mathematics can be applied.
So what is immaterial that can be included in this broader definition of science? Information is immaterial, and is studied systematically and operates in accordance with specific laws with sufficient precision so that the language and methods of mathematics are applied. This area of science consists of information theory and related theories of formal languages, algorithms, etc., and the corresponding applied science consists of the technologies of information storage, communication, and processing.
In a previous blog, ALL Things, I made the case that the material world consists of four interrelated elements: matter, energy, space, and time. Then in a later blog, Is Encoded Information an Essential Part of the Universe?, I made the case that encoded information is an optional fifth element, not required by the laws of physics, but nonetheless present where (and only where) life is present. The reason why information is found only where life is found is that the design paradigm of life is chemistry guided by DNA information, as I explained in the blog, Life is more than chemistry. Without the guiding information, chemistry can only make inorganic molecules. DNA information is needed to make organic molecules, which are much larger and more complex. (Some simple molecules such as certain amino acids are traditionally classified as 'organic' if they are used as components of large organic molecules, but that is like listing raw iron as a machine part, along with the nuts, bolts, and cotter pins.)
Information Outside the Box
Before the functions of DNA and RNA, and the genetic code were discovered, evolutionists could take advantage of the mystery of genetics to tell imaginative stories of how evolution might operate. But these discoveries irreversibly brought information theory into the scientific arena of the creationism / evolutionism debate. Just as someone caught in a lie feels forced to tell new lies or to modify the first lie to maintain credibilty, the evolutionists felt compelled to change their story; and just as a gang of liars are not likely to agree except on their innocence, the evolutionists don't agree except that God is not involved.
Some evolutionists insist that there is no information in DNA and RNA, as though closing their eyes will make the information bogeyman go away. Some insist that information theory is not a valid science (because information is not material). Some claim that information can come from nothing, or from randomness (which is zero information according to information theory), attempting to prove this by equating patterns and information. And some claim that new information can be generated by random re-arrangements of scraps of information, as though it were possible that if you scrambled parts of the Koran long enough, you might end up with the Bible. Even those that admit that information always originates from intelligence deny that God is a plausible source of that intelligence, but prefer an "extraterrestrial" source, replacing the question "How did life information originate on earth?" with the question "How did life information originate on planet X?" (Do I need to explain the fallacy of that logic?)
So the evolutionists that dare to wander outside the box of materialism either flounder like someone diving into water without learning to swim first, or they retreat to the more comfortable zone of materialism.
But that is only the beginning of the problems for the materialists. The information in DNA is not just information, but more specificly, DESIGN information. And here the evolutionists are completely lost in an unfamiliar world. Most of the contributors to the science of intelligent design have a background in the applied science of engineering, because this is familiar territory that they understand.
Structured Information (Top-Down Design) Outside the Box
The key to understanding the evolutionary problem is that design information is structured information. Let me give a simple example to make this clear. In a book, whether fiction or nonfiction, letters are arranged to make words, words arranged to make phrases, phrases arranged to make clauses, clauses arranged to make sentences, sentences arranged to make paragraphs, and paragraphs arranged to make make chapters. Does any author start with letters and play with different sequences to make words, etc., finally making chapters? No, the author starts with an array of related concepts, and starts at some high level of organization and works downward, finally working out the details of how best to arrange a sentence and how to spell the words. Rarely does the author accomplish the final work in one pass, but each revision starts with a new concept at some level and works downward.
Does a designer start with an assortment of parts, like a box of legos, and wonder what he might do with them? No, he starts with a concept, such as using suction to remove household dirt, and designs a vaccuum cleaner "top down", as designers like to say. He may begin with a simple set of features, and add features such as exchangeable attachments, but additions and revisions are always "top down". For example, when he decides that he needs a hose to connect attachments to the vaccuum pump, he first determines its desired properties (lightweight, flexible, does not collapse like a fire hose, etc.) and then works out the details, such as using a wire coil to keep the hose from collapsing.
Unlike the book example, a design typically mixes different technologies. For example, the engineer needs to choose appropriate materials, and so depends on experts in metallurgy, plastics, etc. Or he needs a motor, and orders one meeting his specications designed by a specialist.
Living things, even single-celled organisms, are likewise complex designs, systems made of subsystems that are made of sub-subsystems, etc. And they mix mechanical, chemical, electrical, communication, etc. 'technologies' to acheive coordinated purposes.
So, the evolutionist, in re-telling his story to adapt to the undeniable presence of design, imagines that accidental genetic changes can modify designs to make new designs. But experienced designers recognize this as "bottom-up" design: that is, a foolish, unworkable strategy. Fiddling with the details never makes a truly new design; it only 'tunes up' or adjusts a design.
For example, the first television sets had about a dozen adjustment knobs in front, which was dangerous, because people that had no clue about the internal technology would fiddle with them with disasterous results. It took a while for the engineers to design automatic adjustment mechanisms to replace all of those knobs except for the channel selector and the volume control. But note that a billion adjustment knobs on the television will never suffice to make it function like a cell phone or a vaccuum cleaner.
Complex systems generally require many automatic adjustment mechanisms. And that is exactly what scientists observe in biology. Genetic adjustment (adaptation) is just one category of these mechanisms. So species are designed to adapt to environmental changes, and we can influence the process by breeding (outside intelligence). But breeding dogs to make horses takes a leap of imagination, and supposing that inorganic matter can turn into human beings with no outside intelligence in something less than an eternity takes a enormous leap of faith.
So if and when evolutionists dare to wander outside the box of materialism, they are likely to discover that evolution is a religion, after all, not a science. That is, if they are willing to be honest with themselves.
Friday, April 08, 2011
Advice for DIY Irreducible Complexity
This is an addendum to the previous blog, "Do-It-Yourself Irreducible Complexity". Here I give advice to anyone who wants to construct, demonstrate, or experiment with the 4-stick weaving illustrated in the previous blog.
(1) To construct the 4-stick weaving, begin by holding a V in each hand, with the left-leaning stick on top for each V, as shown in the next photo. Keep your index fingers free, because you will need them later. That is, use the thumb and the lower three fingers to hold each V.
(2) Next, make a W by overlapping the two V's a bit, with the left side of the right V underneath the right side of the left V, as shown in the next photo.
(3) Next, pivot the V's, bringing the tip of the left side of the right V over the left side of the left V, and the left side of the right V over the right side of the left V, as shown in the next photo. The basic principle is that each stick will have an alternating over-under-over or under-over-under pattern.
(4a) Next, pivot the V's some more, bringing the two tips at the top of the configuration closer together. The tip coming from the right will naturally be on top, but you will need to reverse this. Here is where you need your index fingers. With your left index finger, push up on the middle of the left-most stick, and with your right index finger, push down on the middle of the right-most stick. Now, as you pivot the V's, the tip coming from the right can go under the tip coming from the left, as shown in the next photo.
(4b) BUT BEFORE letting go or putting it down, check that all six overlap 'joints' are secure and equally spaced. Because you can't let go yet, you need to use whatever fingers are closest to the joint that needs adjusting.
Extra Challenge
Those practiced with crafts such as origami will feel more comfortable using all fingers individually like this. If you have this kind of dexterity, you may want to accept the challenge of 'evolving' the design into the 5-stick weaving shown in the next photo. Or you can get a partner so that four hands can be used together. To truly emulate evolution, you must add the fifth stick without the configuration 'dying' (coming apart). And strictly speaking, you must do this without a plan (so I'm not giving you one), because evolution is supposed to be mindless and without even a goal, no less a plan. (So partners are not allowed to talk.)
If you succeed in assembling the 4-stick weaving, and especially if you could assemble the 5-stick weaving, you will have noticed that there is absolutely no way for the sticks to fall together this way. In fact, many simultaneous forces at very specific positions and directions and sequence were needed -- in other words, INFORMATION was needed.
An Abstract Analogy
This exercise also provides a rather abstract analogy of a problem encountered in biology. Proteins are made of peptide chains that are folded in specific ways, and often multiple folded chains are assembled into a working unit. Often, proteins cannot fold correctly without the help of a tool to guide or to correct the folding. Also, tools are often needed to assemble multiple-chain protein units. These tools are called chaperone proteins; and they are also used to disassemble and unfold proteins (for digestion, for example). So the DNA information defines not only the 'parts' but also the 'tools', with built-in 'assembly instructions'. When constructing a stick weaving, your hands are acting (abstractly) 'like' chaperone proteins, but the details are very different, of course.
(1) To construct the 4-stick weaving, begin by holding a V in each hand, with the left-leaning stick on top for each V, as shown in the next photo. Keep your index fingers free, because you will need them later. That is, use the thumb and the lower three fingers to hold each V.
(2) Next, make a W by overlapping the two V's a bit, with the left side of the right V underneath the right side of the left V, as shown in the next photo.
(3) Next, pivot the V's, bringing the tip of the left side of the right V over the left side of the left V, and the left side of the right V over the right side of the left V, as shown in the next photo. The basic principle is that each stick will have an alternating over-under-over or under-over-under pattern.
(4a) Next, pivot the V's some more, bringing the two tips at the top of the configuration closer together. The tip coming from the right will naturally be on top, but you will need to reverse this. Here is where you need your index fingers. With your left index finger, push up on the middle of the left-most stick, and with your right index finger, push down on the middle of the right-most stick. Now, as you pivot the V's, the tip coming from the right can go under the tip coming from the left, as shown in the next photo.
(4b) BUT BEFORE letting go or putting it down, check that all six overlap 'joints' are secure and equally spaced. Because you can't let go yet, you need to use whatever fingers are closest to the joint that needs adjusting.
Extra Challenge
Those practiced with crafts such as origami will feel more comfortable using all fingers individually like this. If you have this kind of dexterity, you may want to accept the challenge of 'evolving' the design into the 5-stick weaving shown in the next photo. Or you can get a partner so that four hands can be used together. To truly emulate evolution, you must add the fifth stick without the configuration 'dying' (coming apart). And strictly speaking, you must do this without a plan (so I'm not giving you one), because evolution is supposed to be mindless and without even a goal, no less a plan. (So partners are not allowed to talk.)
If you succeed in assembling the 4-stick weaving, and especially if you could assemble the 5-stick weaving, you will have noticed that there is absolutely no way for the sticks to fall together this way. In fact, many simultaneous forces at very specific positions and directions and sequence were needed -- in other words, INFORMATION was needed.
An Abstract Analogy
This exercise also provides a rather abstract analogy of a problem encountered in biology. Proteins are made of peptide chains that are folded in specific ways, and often multiple folded chains are assembled into a working unit. Often, proteins cannot fold correctly without the help of a tool to guide or to correct the folding. Also, tools are often needed to assemble multiple-chain protein units. These tools are called chaperone proteins; and they are also used to disassemble and unfold proteins (for digestion, for example). So the DNA information defines not only the 'parts' but also the 'tools', with built-in 'assembly instructions'. When constructing a stick weaving, your hands are acting (abstractly) 'like' chaperone proteins, but the details are very different, of course.
Sunday, September 05, 2010
Can Artificial Intelligence be Evolved?
Now and then we see a claim that an evolutionary program has advanced the pursuit of artificial intelligence (AI). Because the degree of 'intelligence' is invariably minuscule compared to advances in AI using non-evolutionary methods, the report will typically use words such as "..evolved to produce basic intelligence" and "it is hoped that the discovery may in future.." That is, even though the 'intelligence' could be demonstrated by a pre-schooler, there is great hope of super-human intelligence down the road.
Usually the reports lack the detail required for critical review. Partly, this is justified because there is so much detail involved that it is not practical to publish everything. But usually there is not even complete disclosure at a functional level.
For example, suppose it is claimed that the 'artificial life-form' developed the use of memory. It could be that the simulated system was simply given the opportunity to do a task with or without memory, and it found that using memory led to greater success. Well, you don't need an evolutionary algorithm to do that. But without disclosing a functional description, the reader can be left with the impression that a memory mechanism was 'evolved'.
For many readers, AI is a great mystery, and the reader has no way to judge whether such reports are overly optimistic or not. So here I will try to remove much of the mystery by providing an overview without getting too deeply into the math and logic.
An Overview of AI
AI is a broad field of study, and not all researchers or developers have the same goals, nor use the same methods.
The process of intelligent thinking is generally described as having two parts: analysis (taking apart) and synthesis (putting together). Closely related to these, the terms induction (logically proceeding from the specific to the generic) and deduction (from generic to specific) are also used. Some AI efforts focus on analysis, some on synthesis, and some on both.
For example, one project focused on using highly abstract formal language to build a data base of 'expert knowledge' garnered from doctors (who did most of the analysis) to synthesize an 'artificial expert' to diagnose diseases, thus simulating a team of medical experts.
Fields of practical science generally have two sub-fields of endeavor: research and development. Research seeks to discover new principles and methods, and development seeks to find effective ways to use the new principles and methods to accomplish practical purposes. Some AI efforts focus on research, some on development, and some on both.
There is a wide variety of methods used to try to create artificial intelligence. Usually an AI project focuses on one method, but sometimes methods are combined. Some methods are attempts to mimic natural patterns or structures.
The 'evolutionary' (selective adaptation) algorithms are in this category. Some model biological selective adaptation closely, and some more loosely, using the 'evolution' concept more as inspiration. It seems to depend on the motive. The motive may be theoretical -- to prove evolution -- and they may talk of "intelligent agents". Or the motive may be practical -- to provide better computing -- and they may talk of "intelligent machines" instead.
Another AI method that mimics nature is neural networks. I remember that the early research in this area focused closely on modelling the operation of actual neurons, trying to understand how they worked. Some used software models, and others built circuits that mimicked neurons. But these early models were very complex, so they chose simpler models so that they could build larger networks.
Other AI methods seek to borrow and adapt the mental methods that people use to reason and solve problems. These AI systems are primarily rule-based -- instead of just handling data that represent facts, they use lists of rules, including rules for choosing rules, or making new rules from other rules, etc. They use category theory, means-end analysis, and planning strategies to try to construct a logical network connecting known facts to a target question. These rule-based systems depend heavily on very abstract formal languages to describe relationships, categories, and attributes of objects.
As an engineer and programmer who has seen up close the development of computing from the days when transistors were first used, I see the rule-based AI methods as a natural extension of the development of computing.
For example, suppose the solution of a problem requires us to determine the length H of the hypotenuse (longest side) of a right triangle when we know the lengths A and B of the shorter sides. To find the answer for a particular case, all we need to do is arithmetic. (I'm including finding square roots as arithmetic.) A machine that can do arithmetic for us is called a calculator.
But to express how to solve all such problems, we use an algebraic expression to say that H is the square root of the sum of A squared and B squared. We have gone to a higher level of abstraction -- from describing the solution of one problem to describing the solution of a class of similar problems. A machine that can do arithmetic for us, guided by an algebraic expression (a formula) is called a programmable calculator.
Now suppose that we need to know how to compute length B when we know length H and length A. This requires a different algebraic expression, which can be derived from the expression that we described earlier. A programmer that knows algebra can manipulate the first expression to derive the second expression, then write another program to solve this new kind of problem. But suppose that we require that the computer should do this algebraic manipulation? This is a different matter. Instead of merely writing software that can interpret an algebraic expression to do the correct arithmetic procedure, the programmer must write software that "knows how to do algebra", that is, to manipulate algebraic expressions. Now we have stepped up to an even higher level of abstraction.
Years ago, I bought a program called MathCad (from Mathsoft) that "knows how to do algebra" -- and calculus, statistics, matrix algebra, graphs, and many other mathematical techniques. It is so good at this that the program taught me math that I hadn't learned in college. There is a similar program named Mathematica produced by Wolfram Research, which is more powerful (and expensive).
Now, Wolfram Research is developing an even 'smarter' program, making the current "Wolfram Alpha" available on the Internet. It has access to a wide variety of scientific data. For example, you can enter "amino acids" and it will list the 20 kinds. Enter "weights of amino acids", and it will assume you meant atomic weights, and tell you the highest, lowest and median values. Better yet, it knows how to interpret these facts. Enter "distance from Venus to Mars", and it will consult its data about the planetary system, and report that right NOW, the distance is 148.7 million miles (and in other units) and that it takes 13 minutes for light to travel that distance in empty space. It's an even higher level of abstraction, without evolution, just more abstract rules.
Conclusion
In summary, the rule-based style of AI has been far more successful in a practical way (accomplishing smarter computing than ever before) than the neural networks and evolutionary algorithms. The neural and evolutionary strategies are pursued not for near-term practical benefit, but on theoretical grounds.
The neural networks are pursued to try to demonstrate that brain-like structures can produce artificial 'thought', in contrast to philosophers who see the brain as not the producer of thought, but more like the soul's keyboard. After decades of research, progress has been painstakingly slow, and results very limited.
The evolutionary strategies are pursued to try to demonstrate that evolution can produce design. But so far, the results only demonstrate what selective adaptation does in the biological world -- namely, to adjust and adapt a design within the confines of the resources already provided within the design.
To designers, such as myself, the reason why the rule-based systems are far more successful is obvious: they are compatible with the top-down principles of design, which works from well-defined purposes toward increasingly more-detailed design. The other methods attempt to achieve design bottom-up, starting with the details and working toward a goal that is not defined, with no strategy as to how to get there. It's implicitly based on the myth that randomness magically produces information, or on the concept of a 'learning machine'. When a design IS 'found', AND the researchers allow you to look at their software, it becomes evident that the result was actually designed into the software. When you hide Easter eggs and search randomly, you might actually find Easter eggs.
But 'learning machines' are inherently complex, and must themselves be designed. Also a 'learning machine' is just an optimizer that finds the 'best' within some domain that is limited by the design. And the principle of irreducible complexity is a huge hurdle that blocks the bottom-up approach. When probabilities are computed for achieving complex designs by random methods, they invariably turn out to be practically zero.
What is "practically zero"? I will define it as 1 divided by a very large number. So what is "a very large number"? In the physical world, it is hard to get numbers larger than about 100 digits. For example, if you estimate the ratio of the mass of the observable universe to the mass of the electron, you get only an 84-digit number. But when you compute the probability of getting some irreducibly complex design by a random method, and express it as 1 divided by X, then X is typically thousands of digits long.
That generally means that the universe doesn't have enough material and enough time for the random experiment to succeed. That's practically zero.
Usually the reports lack the detail required for critical review. Partly, this is justified because there is so much detail involved that it is not practical to publish everything. But usually there is not even complete disclosure at a functional level.
For example, suppose it is claimed that the 'artificial life-form' developed the use of memory. It could be that the simulated system was simply given the opportunity to do a task with or without memory, and it found that using memory led to greater success. Well, you don't need an evolutionary algorithm to do that. But without disclosing a functional description, the reader can be left with the impression that a memory mechanism was 'evolved'.
For many readers, AI is a great mystery, and the reader has no way to judge whether such reports are overly optimistic or not. So here I will try to remove much of the mystery by providing an overview without getting too deeply into the math and logic.
An Overview of AI
AI is a broad field of study, and not all researchers or developers have the same goals, nor use the same methods.
The process of intelligent thinking is generally described as having two parts: analysis (taking apart) and synthesis (putting together). Closely related to these, the terms induction (logically proceeding from the specific to the generic) and deduction (from generic to specific) are also used. Some AI efforts focus on analysis, some on synthesis, and some on both.
For example, one project focused on using highly abstract formal language to build a data base of 'expert knowledge' garnered from doctors (who did most of the analysis) to synthesize an 'artificial expert' to diagnose diseases, thus simulating a team of medical experts.
Fields of practical science generally have two sub-fields of endeavor: research and development. Research seeks to discover new principles and methods, and development seeks to find effective ways to use the new principles and methods to accomplish practical purposes. Some AI efforts focus on research, some on development, and some on both.
There is a wide variety of methods used to try to create artificial intelligence. Usually an AI project focuses on one method, but sometimes methods are combined. Some methods are attempts to mimic natural patterns or structures.
The 'evolutionary' (selective adaptation) algorithms are in this category. Some model biological selective adaptation closely, and some more loosely, using the 'evolution' concept more as inspiration. It seems to depend on the motive. The motive may be theoretical -- to prove evolution -- and they may talk of "intelligent agents". Or the motive may be practical -- to provide better computing -- and they may talk of "intelligent machines" instead.
Another AI method that mimics nature is neural networks. I remember that the early research in this area focused closely on modelling the operation of actual neurons, trying to understand how they worked. Some used software models, and others built circuits that mimicked neurons. But these early models were very complex, so they chose simpler models so that they could build larger networks.
Other AI methods seek to borrow and adapt the mental methods that people use to reason and solve problems. These AI systems are primarily rule-based -- instead of just handling data that represent facts, they use lists of rules, including rules for choosing rules, or making new rules from other rules, etc. They use category theory, means-end analysis, and planning strategies to try to construct a logical network connecting known facts to a target question. These rule-based systems depend heavily on very abstract formal languages to describe relationships, categories, and attributes of objects.
As an engineer and programmer who has seen up close the development of computing from the days when transistors were first used, I see the rule-based AI methods as a natural extension of the development of computing.
For example, suppose the solution of a problem requires us to determine the length H of the hypotenuse (longest side) of a right triangle when we know the lengths A and B of the shorter sides. To find the answer for a particular case, all we need to do is arithmetic. (I'm including finding square roots as arithmetic.) A machine that can do arithmetic for us is called a calculator.
But to express how to solve all such problems, we use an algebraic expression to say that H is the square root of the sum of A squared and B squared. We have gone to a higher level of abstraction -- from describing the solution of one problem to describing the solution of a class of similar problems. A machine that can do arithmetic for us, guided by an algebraic expression (a formula) is called a programmable calculator.
Now suppose that we need to know how to compute length B when we know length H and length A. This requires a different algebraic expression, which can be derived from the expression that we described earlier. A programmer that knows algebra can manipulate the first expression to derive the second expression, then write another program to solve this new kind of problem. But suppose that we require that the computer should do this algebraic manipulation? This is a different matter. Instead of merely writing software that can interpret an algebraic expression to do the correct arithmetic procedure, the programmer must write software that "knows how to do algebra", that is, to manipulate algebraic expressions. Now we have stepped up to an even higher level of abstraction.
Years ago, I bought a program called MathCad (from Mathsoft) that "knows how to do algebra" -- and calculus, statistics, matrix algebra, graphs, and many other mathematical techniques. It is so good at this that the program taught me math that I hadn't learned in college. There is a similar program named Mathematica produced by Wolfram Research, which is more powerful (and expensive).
Now, Wolfram Research is developing an even 'smarter' program, making the current "Wolfram Alpha" available on the Internet. It has access to a wide variety of scientific data. For example, you can enter "amino acids" and it will list the 20 kinds. Enter "weights of amino acids", and it will assume you meant atomic weights, and tell you the highest, lowest and median values. Better yet, it knows how to interpret these facts. Enter "distance from Venus to Mars", and it will consult its data about the planetary system, and report that right NOW, the distance is 148.7 million miles (and in other units) and that it takes 13 minutes for light to travel that distance in empty space. It's an even higher level of abstraction, without evolution, just more abstract rules.
Conclusion
In summary, the rule-based style of AI has been far more successful in a practical way (accomplishing smarter computing than ever before) than the neural networks and evolutionary algorithms. The neural and evolutionary strategies are pursued not for near-term practical benefit, but on theoretical grounds.
The neural networks are pursued to try to demonstrate that brain-like structures can produce artificial 'thought', in contrast to philosophers who see the brain as not the producer of thought, but more like the soul's keyboard. After decades of research, progress has been painstakingly slow, and results very limited.
The evolutionary strategies are pursued to try to demonstrate that evolution can produce design. But so far, the results only demonstrate what selective adaptation does in the biological world -- namely, to adjust and adapt a design within the confines of the resources already provided within the design.
To designers, such as myself, the reason why the rule-based systems are far more successful is obvious: they are compatible with the top-down principles of design, which works from well-defined purposes toward increasingly more-detailed design. The other methods attempt to achieve design bottom-up, starting with the details and working toward a goal that is not defined, with no strategy as to how to get there. It's implicitly based on the myth that randomness magically produces information, or on the concept of a 'learning machine'. When a design IS 'found', AND the researchers allow you to look at their software, it becomes evident that the result was actually designed into the software. When you hide Easter eggs and search randomly, you might actually find Easter eggs.
But 'learning machines' are inherently complex, and must themselves be designed. Also a 'learning machine' is just an optimizer that finds the 'best' within some domain that is limited by the design. And the principle of irreducible complexity is a huge hurdle that blocks the bottom-up approach. When probabilities are computed for achieving complex designs by random methods, they invariably turn out to be practically zero.
What is "practically zero"? I will define it as 1 divided by a very large number. So what is "a very large number"? In the physical world, it is hard to get numbers larger than about 100 digits. For example, if you estimate the ratio of the mass of the observable universe to the mass of the electron, you get only an 84-digit number. But when you compute the probability of getting some irreducibly complex design by a random method, and express it as 1 divided by X, then X is typically thousands of digits long.
That generally means that the universe doesn't have enough material and enough time for the random experiment to succeed. That's practically zero.
Saturday, June 26, 2010
The Digital Control of Life
In other blog articles such as Life is More Than Chemistry and Can Chemical Evolution Work? I point out how living things fundamentally differ from nonliving things. Both are controlled by the laws of chemistry, but in living things, the chemistry is guided by information from the DNA data source. That explains why organic molecules are generally much larger than inorganic molecules. To make such large molecules, the limitations of pure chemistry are overcome by 'helper' molecules such as chaperone molecules made according to the DNA design plan. If the DNA data source is cut off, the organic molecules decompose as the laws of pure chemistry take over.
In a recent blog article, The First Digitally Controlled Designs, I point out that each living organism is a digitally-controlled design, using the same design paradigm now commonly used in most household appliances, where an embedded controller uses symbolic digital codes (software) to control the functions of the appliance. Because the 'software' in these cases is stored in read-only memory (ROM), it is technically called 'firmware'.
The DNA is also firmware, because:
(1) It is digital: the digits are Adenine, Cytosine, Thymine, and Guanine, equivalent to a 2-bit code. The fact that genetic control uses 4-valued digits, and man-made controllers use 2-valued digits (bits) is a mere design detail.
(2) It is symbolic: Each codon, a sequence of three DNA digits, equivalent to a 6-bit code, is NOT an amino acid, but a symbol that represents an amino acid (or in one case, a stop signal). The fact that genetic control uses 6-bit codons, and man-made controllers use 8-bit bytes is a mere design detail.
(3) It is stored in read-only memory. There is no information flow from polypeptides to mRNA to DNA, or any writing process.
(4) In the reading process, selected information from the DNA is copied to mRNA (temporary copies) and then interpreted: that is, translated to polypeptides (the basic form of proteins). In man-made digital controllers, selected information from the read-only memory is copied to temporary memory and then interpreted: that is, translated to signals that produce desired actions.
In addition to the temporary (mRNA) copying, in cells there are two other copying processes. There is a copying process that occurs during cell mitosis for growth and repair, and a rearrangement/copying process that occurs during cell meiosis for sexual reproduction. Neither process creates new information. Man-made digital controllers are not designed to grow and reproduce by themselves, so similar copying is not provided. Instead, there is copying in the manufacturing process.
(5) There is a higher structure typical of digital control languages. These specialized languages have data units that operate somewhat like the verbs, nouns, and modifiers of 'natural' (human) languages. Some, like a noun, specify an object or subject; some, like a verb, specify an action; and others (modifiers) specify a condition or selection or limitation, etc. The DNA information is used not only to create the basic structures (nouns) of life, but also specialized molecules (modifiers) that control the operations (verbs) of these structures.
If you want to appreciate the complexity of life designs at the cellular level, consider the process of extracting energy from food molecules like glucose. Simply put, the process is a "controlled burn" of the food-fuel, producing energy, carbon dioxide and water. The released energy is transported by ATP molecules (like rechargeable batteries) to the sites of all the energy-consuming activities of the cell.
This process, called Cellular Respiration, involves 4 stages:
Glycolysis (10 steps),
the Citric Acid Cycle (8 stages),
the Kreb's Cycle (8 stages), and
the Electron Transport Chain (4 steps).
If you click on each of the above links, you will see what organic chemists call a "simplified" or "summary" diagram of each part of the process. Unless you are an organic chemist or a student of organic chemistry, you will not understand these diagrams, but one glance will give you a good idea of the level of complexity of so-called 'primitive' life. These diagrams represent only some of the cell processes, and they are only summaries! There are diagrams for other complex processes, such as Photosynthesis, which captures the energy of sunlight and stores it by making glucose (food-fuel).
The process of reading and interpreting the DNA information creates all the chemical 'machinery' (such as enzymes) and chemical 'factories' (such as mitochondria) for these and many other complex processes of living things.
In a recent blog article, The First Digitally Controlled Designs, I point out that each living organism is a digitally-controlled design, using the same design paradigm now commonly used in most household appliances, where an embedded controller uses symbolic digital codes (software) to control the functions of the appliance. Because the 'software' in these cases is stored in read-only memory (ROM), it is technically called 'firmware'.
The DNA is also firmware, because:
(1) It is digital: the digits are Adenine, Cytosine, Thymine, and Guanine, equivalent to a 2-bit code. The fact that genetic control uses 4-valued digits, and man-made controllers use 2-valued digits (bits) is a mere design detail.
(2) It is symbolic: Each codon, a sequence of three DNA digits, equivalent to a 6-bit code, is NOT an amino acid, but a symbol that represents an amino acid (or in one case, a stop signal). The fact that genetic control uses 6-bit codons, and man-made controllers use 8-bit bytes is a mere design detail.
(3) It is stored in read-only memory. There is no information flow from polypeptides to mRNA to DNA, or any writing process.
(4) In the reading process, selected information from the DNA is copied to mRNA (temporary copies) and then interpreted: that is, translated to polypeptides (the basic form of proteins). In man-made digital controllers, selected information from the read-only memory is copied to temporary memory and then interpreted: that is, translated to signals that produce desired actions.
In addition to the temporary (mRNA) copying, in cells there are two other copying processes. There is a copying process that occurs during cell mitosis for growth and repair, and a rearrangement/copying process that occurs during cell meiosis for sexual reproduction. Neither process creates new information. Man-made digital controllers are not designed to grow and reproduce by themselves, so similar copying is not provided. Instead, there is copying in the manufacturing process.
(5) There is a higher structure typical of digital control languages. These specialized languages have data units that operate somewhat like the verbs, nouns, and modifiers of 'natural' (human) languages. Some, like a noun, specify an object or subject; some, like a verb, specify an action; and others (modifiers) specify a condition or selection or limitation, etc. The DNA information is used not only to create the basic structures (nouns) of life, but also specialized molecules (modifiers) that control the operations (verbs) of these structures.
If you want to appreciate the complexity of life designs at the cellular level, consider the process of extracting energy from food molecules like glucose. Simply put, the process is a "controlled burn" of the food-fuel, producing energy, carbon dioxide and water. The released energy is transported by ATP molecules (like rechargeable batteries) to the sites of all the energy-consuming activities of the cell.
This process, called Cellular Respiration, involves 4 stages:
If you click on each of the above links, you will see what organic chemists call a "simplified" or "summary" diagram of each part of the process. Unless you are an organic chemist or a student of organic chemistry, you will not understand these diagrams, but one glance will give you a good idea of the level of complexity of so-called 'primitive' life. These diagrams represent only some of the cell processes, and they are only summaries! There are diagrams for other complex processes, such as Photosynthesis, which captures the energy of sunlight and stores it by making glucose (food-fuel).
The process of reading and interpreting the DNA information creates all the chemical 'machinery' (such as enzymes) and chemical 'factories' (such as mitochondria) for these and many other complex processes of living things.
Thursday, March 18, 2010
The First Digitally-Controlled Designs
Since the discovery of DNA and RNA and the Genetic Code, it is indisputably clear to biologists that the structure and function of all living things is determined by the information stored in the DNA. The interpretation of the DNA information according to the Genetic Code creates a enormous set of specific proteins and other complex organic molecules that implement the structure and function of a particular organism. (See The Genetic Code - how to read the DNA record and More About the Genetic Code.) Some of these complex molecules are building blocks of the living structure; some are the tools or 'workmen' that build the structure; other organic molecules function more like supervisors that control when and where and how this work of construction is done. Still others supervise various functions of the living structure, such as digestion, breathing. sight, growth, etc. All of these complex functions are guided not exclusively by chemical laws, but also by the information from the DNA. (See Life is more than chemistry and Can Chemical Evolution Work?) This is true of all living things, whether a single-celled organism or a much larger plant or animal such as an oak tree or an elephant. Such a complex, coordinated interplay of material and function at multiple levels is clearly DESIGN.
I ought to explain that I understand and appreciate this from experience. I worked for 43 years as a designer and inventor of computers and other digital systems, acquiring 45 patents in that time; and in my retirement years, I have been studying organic chemistry. When I started my career, a typical computer was a roomful of refrigerator-sized cabinets. but less powerful than today's pocket calculator; and I have seen the technology grow functionally and shrink physically since then. In between then and now, the Quintrel computer that I designed, one of the first to do speech processing (like speech recognition) in real time (that is, as fast as you can talk) was the size of a cookie baking pan. Inside all GPS satellites, the computer system that controls all the signals is my design. So I know a design when I see one.
I especially appreciate the advantages of a digitally-controlled design over a design that is just digital. The old-fashioned mechanical adding machines did digital calculation, but the control was manual; that is, the operator had to select the sequence of operations as well as the input data. I remember the company used to have one with a typewriter-like shifting carriage so that it could do multiplication and division; but it was still manually controlled.
In human history, digitally controlled designs started with things like the 'player piano', where the keyboard was controlled by a roll of paper with punched holes to specify the sequence and timing of the notes, and the Jacquard loom, where punched holes caused threads to be raised or lowered to create intricate designs such as brocade and damask. Herman Hollerith adapted the punched cards of the weaving industry for data input for his Tabulating Machines, and Charles Babbage planned to use punched cards for his Analytical Engine, which began the age of computers. (See The Development of Information Processing.)
Let me tell a story that illustrates how "I especially appreciate the advantages of a digitally-controlled design" as I said earlier.
There was a period in my career when we designed digital devices for communication of digital messages. No calculation in the ordinary sense of the word was needed, but the digital logic needed to be 'smart'. For example, before sending a piece of a message (called a packet), an error-checking code needed to be generated and attached to the message, along with a packet number. When receiving a packet, the error-checking code needed to be checked to see if the packet had any errors. (Most errors were detectable.) If the packet had no errors, an 'ack' (acknowledgement) message was returned to the sender; but if errors were detected, a 'nak' (no-acknowledgement) message was returned. Both ack and nak messages included the number of the good or bad packet that had been checked. A nak message was a request to resend the packet (hoping to get it right on the next try), and an ack message told the sender that it no longer needed to keep a copy of the packet. A communication protocol like this was controlled by logic hardware similar to that used to construct a computer, but there was no computer and no software involved. The designs were digital, but not digitally-controlled as computers are controlled by software.
A major problem with this style of design was that if a design error needed to be corrected, or a new design feature added, new parts would need to be added, and the layout and wiring of the parts modified. The parts might not fit, so even the mechanical design might need to be redone.
An obvious solution to this problem is to include an 'embedded' computer in the design, so that software can define the functions of the design, because software is far more easily changed than the hardware. Once the software is thoroughly tested and no longer needs to be changed, it is typically embedded in read-only memory (ROM) and is called 'firmware'. This tactic is commonplace today, with embedded computers in automobiles and in nearly every electrical household appliance. That's easy today, because electronic circuits have shrunk enough for small computers, including all memory and other supporting logic, to be placed in one small, low-cost chip. But back then, electronics had shrunk only enough for simple circuits such a counter to fit in one chip. An embedded computer would require at least several chips.
We couldn't buy a general-purpose computer chip (they didn't exist then), but had to design a computer made of several chips. But this gave us the freedom to design a smaller 'custom' computer with only the functions actually needed. For example, we didn't need to add, subtract, multiply or divide; the only 'arithmetic' needed was to count the bits of a packet. Mostly, the computer needed to make decisions based on a specialized set of conditions. If such a design primarily controlled not a sequence of calculations, but a sequence of other operations (such as those needed for a communications protocol), it was usually called a 'controller' rather than a computer. Often, such a simplified computer / controller could be made with only a half-dozen parts. This 'custom' controller would thus have a 'custom' set of instructions that it could execute. (Each instruction is a group of binary codes and data that tell the computer / controller what to do for each step of its actions.)
Theoretically, a programmer (software writer) could write out the sequence of instructions (the software, or program) in the form of the ones and zeros that the hardware actually reads. But this would be very error-prone, because it is hard for people to memorize these codes, or even to copy them from a list without making mistakes. So, instead, equivalent codes that look more like English are invented, thus creating a special language that is much easier to learn and understand. Then a program called an 'assembler' is used to translate the semi-English to the ones and zeros that the hardware uses. (Also, decimal numbers are translated to binary numbers.)
Thus, almost every computer / controller design would have a different instruction set, and a correspondingly different 'assembly language', and a different assembler program. The assembler is what connected the software design to the hardware design.
Mostly, there were two kinds of designers: hardware logic designers that knew at least how to design parts of the computer hardware, and software designers that knew how to write software. A third kind of designer was a relative minority: the 'system designer', who understood both hardware and software -- the whole system, or the 'big picture'. (See The Start of System Engineering.) A few of these, who also knew the theory of formal languages, were able to write assembler programs, and even 'compilers', which can translate more abstract software languages. With my insatiable curiosity and willingness to self-educate myself in related fields on my own time, I became part of that minority.
The engineering supervisors resisted the idea of embedding computers in a design. Their reasoning was that we had hardware designers and software designers, but nobody that knew how to make a custom assembler. We would have to give such a job to outside specialists, which would be too expensive and troublesome.
It irked me that this judgement was hindering us from making compact and flexible designs. So, on my own time, I designed what I called the "General-Purpose Assembler". It was a step beyond a custom assembler, because before assembling a program, it first read a "language table", which defined the custom assembly language. So, the next time that a supervisor tried to veto a proposal for a design with an embedded computer / controller, I explained that I "happened to have" an assembler that could do the job. I did the extra work on my own time because I knew that digital control of a design was an optimum design paradigm.
I wrote an instruction manual for how to construct a "language table" and how to use the "General-Purpose Assembler", and soon other departments and projects were using it. A few years later, I estimated that about two dozen language tables had been written, creating that many custom assemblers for that many different embedded controllers. The "General-Purpose Assembler" also became a component of the assembler for the Quintrel processor that I mentioned earlier. These were all digitally-controlled designs.
Now, this story may seem like an utter digression from my initial discussion of DNA and RNA and the Genetic Code, but it was all to underscore and emphasize the following point:
I used to think of digitally-controlled designs as a modern phenomena -- but this is true only if you are limited to designs made by humans. But when I started to study organic chemistry and the workings of the Genetic Code, I soon realized that the greatest Engineer of all, God, got there first. For indeed, all living things of all kinds are digitally-controlled designs. The DNA is the read-only memory (ROM) that holds the genome, which is the software (firmware) that controls the chemistry that plays the role of 'hardware'. Each unit of DNA (nucleotide) is equivalent to two bits, having one of four values, and each codon (three DNA units) is equivalent to six bits, with one of 64 values. It compels one to ask "Where did all that DNA-software come from?" (See In The Beginning Was Information .) The reason why there is only one universal genetic code, and why so many life-forms share common design structures is not because all descended from a single common ancestor (unlikely if evolution is inevitable, as Richard Dawkins claims), but because all have a single Creator.
I know that some readers will dismiss my comparison of life designs to man-made designs as mere analogy. But my argument rests on more than analogy. It involves what in category theory is called isomorphisms. Rather than getting too technical, I will illustrate the principles involved by a simple example:
If two species have sufficient similarities (putting them in the same category), we can expect them to have similar locomotion. For example, cats and dogs both have four legs of nearly equal lengths, and the knees bend in the same directions; so we can expect them to walk and run in similar ways. Frogs, kangaroos, and apes also have four legs, but not all four of equal length, so the locomotion is different. There is greater similarity of function when there is greater similarity of structure.
With similar logic methods, we can show that DNA-controlled life forms are more similar to embedded controllers than personal computers. For example, in both, the completed design has no capability of loading new software (not true for PCs). In both computers and controllers, the same hardware with completely different software will have completely different functionality. In life, the same chemical laws, chemical resources (food, air, water, etc) and same genetic code with a completely different genome will have completely different functionality.
As an experienced designer, I not only know a design when I see one, I know a digitally-controlled design when I see one; and I appreciate that it is an optimum design paradigm. No wonder that people are using the term "Intelligent Design" to describe living things.
For more on this subject, see The Digital Control of Life.
I ought to explain that I understand and appreciate this from experience. I worked for 43 years as a designer and inventor of computers and other digital systems, acquiring 45 patents in that time; and in my retirement years, I have been studying organic chemistry. When I started my career, a typical computer was a roomful of refrigerator-sized cabinets. but less powerful than today's pocket calculator; and I have seen the technology grow functionally and shrink physically since then. In between then and now, the Quintrel computer that I designed, one of the first to do speech processing (like speech recognition) in real time (that is, as fast as you can talk) was the size of a cookie baking pan. Inside all GPS satellites, the computer system that controls all the signals is my design. So I know a design when I see one.
I especially appreciate the advantages of a digitally-controlled design over a design that is just digital. The old-fashioned mechanical adding machines did digital calculation, but the control was manual; that is, the operator had to select the sequence of operations as well as the input data. I remember the company used to have one with a typewriter-like shifting carriage so that it could do multiplication and division; but it was still manually controlled.
In human history, digitally controlled designs started with things like the 'player piano', where the keyboard was controlled by a roll of paper with punched holes to specify the sequence and timing of the notes, and the Jacquard loom, where punched holes caused threads to be raised or lowered to create intricate designs such as brocade and damask. Herman Hollerith adapted the punched cards of the weaving industry for data input for his Tabulating Machines, and Charles Babbage planned to use punched cards for his Analytical Engine, which began the age of computers. (See The Development of Information Processing.)
Let me tell a story that illustrates how "I especially appreciate the advantages of a digitally-controlled design" as I said earlier.
There was a period in my career when we designed digital devices for communication of digital messages. No calculation in the ordinary sense of the word was needed, but the digital logic needed to be 'smart'. For example, before sending a piece of a message (called a packet), an error-checking code needed to be generated and attached to the message, along with a packet number. When receiving a packet, the error-checking code needed to be checked to see if the packet had any errors. (Most errors were detectable.) If the packet had no errors, an 'ack' (acknowledgement) message was returned to the sender; but if errors were detected, a 'nak' (no-acknowledgement) message was returned. Both ack and nak messages included the number of the good or bad packet that had been checked. A nak message was a request to resend the packet (hoping to get it right on the next try), and an ack message told the sender that it no longer needed to keep a copy of the packet. A communication protocol like this was controlled by logic hardware similar to that used to construct a computer, but there was no computer and no software involved. The designs were digital, but not digitally-controlled as computers are controlled by software.
A major problem with this style of design was that if a design error needed to be corrected, or a new design feature added, new parts would need to be added, and the layout and wiring of the parts modified. The parts might not fit, so even the mechanical design might need to be redone.
An obvious solution to this problem is to include an 'embedded' computer in the design, so that software can define the functions of the design, because software is far more easily changed than the hardware. Once the software is thoroughly tested and no longer needs to be changed, it is typically embedded in read-only memory (ROM) and is called 'firmware'. This tactic is commonplace today, with embedded computers in automobiles and in nearly every electrical household appliance. That's easy today, because electronic circuits have shrunk enough for small computers, including all memory and other supporting logic, to be placed in one small, low-cost chip. But back then, electronics had shrunk only enough for simple circuits such a counter to fit in one chip. An embedded computer would require at least several chips.
We couldn't buy a general-purpose computer chip (they didn't exist then), but had to design a computer made of several chips. But this gave us the freedom to design a smaller 'custom' computer with only the functions actually needed. For example, we didn't need to add, subtract, multiply or divide; the only 'arithmetic' needed was to count the bits of a packet. Mostly, the computer needed to make decisions based on a specialized set of conditions. If such a design primarily controlled not a sequence of calculations, but a sequence of other operations (such as those needed for a communications protocol), it was usually called a 'controller' rather than a computer. Often, such a simplified computer / controller could be made with only a half-dozen parts. This 'custom' controller would thus have a 'custom' set of instructions that it could execute. (Each instruction is a group of binary codes and data that tell the computer / controller what to do for each step of its actions.)
Theoretically, a programmer (software writer) could write out the sequence of instructions (the software, or program) in the form of the ones and zeros that the hardware actually reads. But this would be very error-prone, because it is hard for people to memorize these codes, or even to copy them from a list without making mistakes. So, instead, equivalent codes that look more like English are invented, thus creating a special language that is much easier to learn and understand. Then a program called an 'assembler' is used to translate the semi-English to the ones and zeros that the hardware uses. (Also, decimal numbers are translated to binary numbers.)
Thus, almost every computer / controller design would have a different instruction set, and a correspondingly different 'assembly language', and a different assembler program. The assembler is what connected the software design to the hardware design.
Mostly, there were two kinds of designers: hardware logic designers that knew at least how to design parts of the computer hardware, and software designers that knew how to write software. A third kind of designer was a relative minority: the 'system designer', who understood both hardware and software -- the whole system, or the 'big picture'. (See The Start of System Engineering.) A few of these, who also knew the theory of formal languages, were able to write assembler programs, and even 'compilers', which can translate more abstract software languages. With my insatiable curiosity and willingness to self-educate myself in related fields on my own time, I became part of that minority.
The engineering supervisors resisted the idea of embedding computers in a design. Their reasoning was that we had hardware designers and software designers, but nobody that knew how to make a custom assembler. We would have to give such a job to outside specialists, which would be too expensive and troublesome.
It irked me that this judgement was hindering us from making compact and flexible designs. So, on my own time, I designed what I called the "General-Purpose Assembler". It was a step beyond a custom assembler, because before assembling a program, it first read a "language table", which defined the custom assembly language. So, the next time that a supervisor tried to veto a proposal for a design with an embedded computer / controller, I explained that I "happened to have" an assembler that could do the job. I did the extra work on my own time because I knew that digital control of a design was an optimum design paradigm.
I wrote an instruction manual for how to construct a "language table" and how to use the "General-Purpose Assembler", and soon other departments and projects were using it. A few years later, I estimated that about two dozen language tables had been written, creating that many custom assemblers for that many different embedded controllers. The "General-Purpose Assembler" also became a component of the assembler for the Quintrel processor that I mentioned earlier. These were all digitally-controlled designs.
Now, this story may seem like an utter digression from my initial discussion of DNA and RNA and the Genetic Code, but it was all to underscore and emphasize the following point:
I used to think of digitally-controlled designs as a modern phenomena -- but this is true only if you are limited to designs made by humans. But when I started to study organic chemistry and the workings of the Genetic Code, I soon realized that the greatest Engineer of all, God, got there first. For indeed, all living things of all kinds are digitally-controlled designs. The DNA is the read-only memory (ROM) that holds the genome, which is the software (firmware) that controls the chemistry that plays the role of 'hardware'. Each unit of DNA (nucleotide) is equivalent to two bits, having one of four values, and each codon (three DNA units) is equivalent to six bits, with one of 64 values. It compels one to ask "Where did all that DNA-software come from?" (See In The Beginning Was Information .) The reason why there is only one universal genetic code, and why so many life-forms share common design structures is not because all descended from a single common ancestor (unlikely if evolution is inevitable, as Richard Dawkins claims), but because all have a single Creator.
I know that some readers will dismiss my comparison of life designs to man-made designs as mere analogy. But my argument rests on more than analogy. It involves what in category theory is called isomorphisms. Rather than getting too technical, I will illustrate the principles involved by a simple example:
If two species have sufficient similarities (putting them in the same category), we can expect them to have similar locomotion. For example, cats and dogs both have four legs of nearly equal lengths, and the knees bend in the same directions; so we can expect them to walk and run in similar ways. Frogs, kangaroos, and apes also have four legs, but not all four of equal length, so the locomotion is different. There is greater similarity of function when there is greater similarity of structure.
With similar logic methods, we can show that DNA-controlled life forms are more similar to embedded controllers than personal computers. For example, in both, the completed design has no capability of loading new software (not true for PCs). In both computers and controllers, the same hardware with completely different software will have completely different functionality. In life, the same chemical laws, chemical resources (food, air, water, etc) and same genetic code with a completely different genome will have completely different functionality.
As an experienced designer, I not only know a design when I see one, I know a digitally-controlled design when I see one; and I appreciate that it is an optimum design paradigm. No wonder that people are using the term "Intelligent Design" to describe living things.
For more on this subject, see The Digital Control of Life.
Saturday, March 06, 2010
More About the Genetic Code
Sometimes I will go back to one of my blog articles to correct minor errors; and a few times I have made major additions. But a disadvantage of this is that people that have already read the original article will probably not go back to read it again.
A little more than a year ago, I wrote The Genetic Code - how to read the DNA record, and recently added some details to a paragraph and expanded the conclusion of the article. So here is the amended paragraph and the expanded conclusion.
The original article gave the impression that only the transfer RNA (tRNA) molecules define the genetic code. Actually, other, larger, molecules are also involved. So the amended paragraph clarifies this:
===
The key elements of translation are small transfer RNA (tRNA) molecules. Each kind of tRNA molecule has a region called the anticodon that can recognize and attach to a particular codon of a messenger RNA (mRNA) molecule. The tRNA molecule has another region called the "3' terminal" that attaches to a particular amino acid. This attachment is aided by molecules called aminoacyl-tRNA synthetases, of which there is generally one kind for each kind of amino acid. There are even helper molecules that provide a proofreading function to detect and correct any translation errors.
===
(Actually, there are some variations of this, but discussing these would be distracting. There are also many other types of complex molecules that control the code-translation process but do not define the genetic code -- another subject.)
Then I expanded the conclusion:
===
Where does the genetic code come from? It is not the result of chemistry or any laws of physics. It is determined by the set of tRNA molecule types, and aminoacyl-tRNA synthetase types, which are constructed according to DNA information, which encodes not only the building materials and the building plans, but also the building tools and the building methods. In other words, the genetic code is just information that has always been there since life began.
The number of possible genetic codes is a huge number, 85 digits long:
1,510,109,515,792,918,244,116,781,339,315,785,081,841,294, 607,960,614,956,302,330,123,544,242,628,820,336,640,000
and all of these many codes would work equally well. But all of life uses just one genetic code, about 280 bits of information, the one that scientists Watson and Crick discovered in 1953, but was there since creation. The theory of evolution has no explanation for how the genetic code began, because it can't explain how information can arise from no information. Nor can it explain why there is only one genetic code (out of such a huge number of equally workable codes), even though there is extreme variation of everything else. The mechanism of the present genetic code is very complex; and evolutionary theory supposes that it randomly evolved from a simpler, smaller code. But because there are so many equally viable genetic codes, random evolution should have produced species with many different codes. The evolutionary explanation is far more unlikely than dumping a bucketful of dice on the floor and expecting them to all land with the same number up.
The creationist explanation is that the universal genetic code is like a signature of the creator, who chose a uniform code for all of the designs of life. A short story will illustrate the principle:
During the Cold War, Russia was suspected of stealing American technology. Proof came when some Russian war equipment given to a third country was captured and examined. It contained an integrated circuit that was identical to an American design. It is theoretically possible that the Russians had the same design concept, leading to a similar design. But digital circuits have thousands of component parts connected by thousands of wires. There trillions of ways to position the parts on the chip and trillions of ways to route the connecting wires that work equally well. It would be impossible for the Russians to independently produce the same positions and routings even if the logical design were identical. But examination showed the details were identical, even details left over from correcting wiring errors. In effect, there was an American 'signature' in the copied design.
===
For the Math fans, I'll add a footnote on how that 85-digit number was calculated:
That big number counts the number of ways that the 64 codons can be mapped to 21 interpretations, or interpreted as 21 'messages'. One message is to start with a Methionine (or add a Methionine if already started); one is to stop, and the other 19 messages are to add one of the other 19 amino acids [to the peptide chain that will fold into a protein molecule]. This 64-to-21 mapping can be enumerated in two steps:
First, we count the number of partitions of a set of 64 items into 21 non-empty, pair-wise disjoint subsets. In plain language, this means that:
Second, we need to count the number of ways that the 21 subsets can be mapped to the 21 messages. This the number of permutations of 21 things, which is 21 factorial, written 21!
So the desired number is S(64, 21) times 21! But typical computer hardware cannot directly compute numbers that large. Special software that partitions a big number into slices small enough for the hardware is needed. When I was designing special hardware for very large integers (for public key cryptography; I have two patents, #4,658,094 and #5,289,397, for that), I wrote such software so that I could test and verify my designs. So I used my 'BigInt' software to do the arithmetic.
A little more than a year ago, I wrote The Genetic Code - how to read the DNA record, and recently added some details to a paragraph and expanded the conclusion of the article. So here is the amended paragraph and the expanded conclusion.
The original article gave the impression that only the transfer RNA (tRNA) molecules define the genetic code. Actually, other, larger, molecules are also involved. So the amended paragraph clarifies this:
===
The key elements of translation are small transfer RNA (tRNA) molecules. Each kind of tRNA molecule has a region called the anticodon that can recognize and attach to a particular codon of a messenger RNA (mRNA) molecule. The tRNA molecule has another region called the "3' terminal" that attaches to a particular amino acid. This attachment is aided by molecules called aminoacyl-tRNA synthetases, of which there is generally one kind for each kind of amino acid. There are even helper molecules that provide a proofreading function to detect and correct any translation errors.
===
(Actually, there are some variations of this, but discussing these would be distracting. There are also many other types of complex molecules that control the code-translation process but do not define the genetic code -- another subject.)
Then I expanded the conclusion:
===
Where does the genetic code come from? It is not the result of chemistry or any laws of physics. It is determined by the set of tRNA molecule types, and aminoacyl-tRNA synthetase types, which are constructed according to DNA information, which encodes not only the building materials and the building plans, but also the building tools and the building methods. In other words, the genetic code is just information that has always been there since life began.
The number of possible genetic codes is a huge number, 85 digits long:
1,510,109,515,792,918,244,116,781,339,315,785,081,841,294, 607,960,614,956,302,330,123,544,242,628,820,336,640,000
and all of these many codes would work equally well. But all of life uses just one genetic code, about 280 bits of information, the one that scientists Watson and Crick discovered in 1953, but was there since creation. The theory of evolution has no explanation for how the genetic code began, because it can't explain how information can arise from no information. Nor can it explain why there is only one genetic code (out of such a huge number of equally workable codes), even though there is extreme variation of everything else. The mechanism of the present genetic code is very complex; and evolutionary theory supposes that it randomly evolved from a simpler, smaller code. But because there are so many equally viable genetic codes, random evolution should have produced species with many different codes. The evolutionary explanation is far more unlikely than dumping a bucketful of dice on the floor and expecting them to all land with the same number up.
The creationist explanation is that the universal genetic code is like a signature of the creator, who chose a uniform code for all of the designs of life. A short story will illustrate the principle:
During the Cold War, Russia was suspected of stealing American technology. Proof came when some Russian war equipment given to a third country was captured and examined. It contained an integrated circuit that was identical to an American design. It is theoretically possible that the Russians had the same design concept, leading to a similar design. But digital circuits have thousands of component parts connected by thousands of wires. There trillions of ways to position the parts on the chip and trillions of ways to route the connecting wires that work equally well. It would be impossible for the Russians to independently produce the same positions and routings even if the logical design were identical. But examination showed the details were identical, even details left over from correcting wiring errors. In effect, there was an American 'signature' in the copied design.
===
For the Math fans, I'll add a footnote on how that 85-digit number was calculated:
That big number counts the number of ways that the 64 codons can be mapped to 21 interpretations, or interpreted as 21 'messages'. One message is to start with a Methionine (or add a Methionine if already started); one is to stop, and the other 19 messages are to add one of the other 19 amino acids [to the peptide chain that will fold into a protein molecule]. This 64-to-21 mapping can be enumerated in two steps:
First, we count the number of partitions of a set of 64 items into 21 non-empty, pair-wise disjoint subsets. In plain language, this means that:
- Together, the 21 subsets must contain all of the 64 codons.
- Each codon must be assigned to only one subset.
- None of the subsets can be empty; each must contain at least one codon.
Second, we need to count the number of ways that the 21 subsets can be mapped to the 21 messages. This the number of permutations of 21 things, which is 21 factorial, written 21!
So the desired number is S(64, 21) times 21! But typical computer hardware cannot directly compute numbers that large. Special software that partitions a big number into slices small enough for the hardware is needed. When I was designing special hardware for very large integers (for public key cryptography; I have two patents, #4,658,094 and #5,289,397, for that), I wrote such software so that I could test and verify my designs. So I used my 'BigInt' software to do the arithmetic.
Monday, January 25, 2010
Dawkins' Confession
I found this video showing Gary DeMar of The American Vision discussing Richard Dawkins' new book, The Greatest Show on Earth:
DeMar points out some interesting quotes from Dawkins' book which I have reproduced below, and will comment on each.
Many churches, and even parachurch organizations, each have a 'statement of faith', or 'confession of faith' whereby they define their core beliefs. It seems that in the beginning of his book, Dawkins gives his 'confession of faith', beginning with:
He also promises that his book will demonstrate the 'fact' of evolution, but no real demonstration of this is possible. There is experimental demonstration, where one sets up initial conditions, controls, and measurements on real physical objects, living or not. But the evolution that relates man to turnips is an interpretation of the past, and no part of it has been experimentally demonstrated in modern times. Parenthetically --
His statement "No reputable scientist disputes it" is a tautology in disguise. There are many reputable scientists that dispute evolution, but to evolutionists like Dawkins, that defines them as not reputable.
As though to illustrate the fervency of Dawkins' faith, the next quote sounds like an enthusiastic description of a miracle:
His description of dust giving "birth to time and space" contradicts physics as we now know it. According to modern physics, matter cannot exists separately from time and space, and vice versa.
Again he uses the word 'fact' to refer to his interpretation of facts. But I would agree that the idea that "life evolved literally out of nothing" is staggering -- so much so that one would be mad to believe it.
If you still doubt that Dawkins' words are a 'confession of faith', read this quote:
Given the huge amount of information and artful design that we now observe in all living things, requiring enormous intelligence, I'd say it must have been God -- it took God to get natural selection started. And by God's account, He created various kinds of living things, so natural selection started on some collection of kinds, rather than one kind of life. And the experimental evidence is that even when we give natural selection an extra push, and the advantage of our intelligence, we can't change cats into dogs, or vice versa, let alone turning turnips into chimpanzees.
I think my faith fits the evidence better.
DeMar points out some interesting quotes from Dawkins' book which I have reproduced below, and will comment on each.
Many churches, and even parachurch organizations, each have a 'statement of faith', or 'confession of faith' whereby they define their core beliefs. It seems that in the beginning of his book, Dawkins gives his 'confession of faith', beginning with:
"It is the plain truth that we are cousins of chimpanzees, somewhat more distant cousins of monkeys, more distant cousins still of aardvarks and manatees, yet more distant cousins of bananas and turnips..."Notice that he speaks of cousins, not brothers, because brothers, mothers, and fathers are not to be found. He continues:
"Evolution is a fact, and [my book The Greatest Show on Earth] will demonstrate it. No reputable scientist disputes it..."Speaking of evolution as a fact doesn't sound, at first, like a statement of faith, but given his admission of lack of evidence (quoted later), it seems that what he really means by this is that he believes so fervently in evolution that it seems like a fact to him. Thinking of my own faith, I know the feeling.
He also promises that his book will demonstrate the 'fact' of evolution, but no real demonstration of this is possible. There is experimental demonstration, where one sets up initial conditions, controls, and measurements on real physical objects, living or not. But the evolution that relates man to turnips is an interpretation of the past, and no part of it has been experimentally demonstrated in modern times. Parenthetically --
We perhaps may need, at this point, to explain to some readers the distinction between macro-evolution, also called goo-to-you evolution, and micro-evolution, the kind that when guided by man breeds cats to get more kinds of cats, but never dogs, and breeds dogs to get more kinds of dogs, but never cats. Creationists believe in micro-evolution, and that's not debated here. The relevance here is that experimental demonstrations have been applied to micro-evolution, but not macro-evolution, which remains in the realm of story-telling.There is also logical demonstration, which in its most reliable form is a formal proof. But the lack of evidence, which Dawkins admits to, precludes logical demonstration of macro-evolution.
His statement "No reputable scientist disputes it" is a tautology in disguise. There are many reputable scientists that dispute evolution, but to evolutionists like Dawkins, that defines them as not reputable.
As though to illustrate the fervency of Dawkins' faith, the next quote sounds like an enthusiastic description of a miracle:
"The universe could so easily have remained lifeless and simple -- just physics and chemistry, just the scattered dust of the cosmic explosion that gave birth to time and space. The fact that it did not -- the fact that life evolved literally out of nothing -- is a fact so staggering that I would be mad to attempt words to do it justice. And even that is not the end of the matter. Not only did evolution happen: it eventually led to beings capable of comprehending the process by which they comprehend it."His phrase "lifeless and simple" is similar to Genesis 1:2, where the earth is described as "formless and empty" before God gives it form and fills it with life; but in Dawkins' account, God gets no credit.
His description of dust giving "birth to time and space" contradicts physics as we now know it. According to modern physics, matter cannot exists separately from time and space, and vice versa.
Again he uses the word 'fact' to refer to his interpretation of facts. But I would agree that the idea that "life evolved literally out of nothing" is staggering -- so much so that one would be mad to believe it.
If you still doubt that Dawkins' words are a 'confession of faith', read this quote:
"We have no evidence about what the first step on making life was, but we do know the kind of step it must have been. It must have been whatever it took to get natural selection started."In other words, Dawkins knows that there must have been an event when life began, there must have a 'first cause' that caused it to begin, and he knows that he has no evidence of how it began. He is unwilling to believe that God was that cause, so he resorts to a tautology: "It must have been whatever it took".
Given the huge amount of information and artful design that we now observe in all living things, requiring enormous intelligence, I'd say it must have been God -- it took God to get natural selection started. And by God's account, He created various kinds of living things, so natural selection started on some collection of kinds, rather than one kind of life. And the experimental evidence is that even when we give natural selection an extra push, and the advantage of our intelligence, we can't change cats into dogs, or vice versa, let alone turning turnips into chimpanzees.
I think my faith fits the evidence better.
Sunday, September 06, 2009
Creation vs. Evolution -- an Overview of my blogs
I've worked as an engineer for 43 years (getting about the same number of patents) designing computers and similar electronic devices that are controlled by information (that we call software) and/or that process information (that we call data). I've even written software that creates other software, and software that creates hardware designs.
In my retirement years, I've been studying the basics of biology and applying my expertise in information systems to investigate the fundamentals of the creation/evolution debate. I look at how living things work from the molecular level on up, and as a systems engineer I recognize a system design when I see one. Living organisms are also controlled by information and process information. Chemistry does the 'hardware' function, and DNA (with its derivatives) does the 'software' function.
I have published my findings, as well as common-language interpretations of other technical sources, on my blog. My blog talks about many other subjects, too, so if you are only interested in the creation/evolution/information stuff, go to the "Find by Subject" section on the right and click on one of those key words. Or you can start with the following overview of a few basic subjects:
Information From Randomness?
In this blog, I discuss the myth that information can somehow arise out of randomness, and discuss Dawkins' Weasel Algorithm in particular. In information theory, pure randomness is zero information. All systems that process information have a tendency to lose information, like the way they lose useful energy. So information always drifts toward randomness, not the other way around.
In The Beginning Was Information
Back in Darwin's day, evolution seemed somewhat plausible, just as the ether and phlogiston were once plausible. But more modern findings have unraveled the claims of evolution (macro-evolution, to be more precise), primarily the discovery that biology is chemistry guided by information. Since we know that information doesn't come from nothing, it begs the question: where did the information come from?
Is Encoded Information an Essential Part of the Universe?
In a previous blog, I had explained that space, time, matter, and energy are inseparable aspects of the universe. Here I argue that information is transcendent to all these. The transport of information across space (communication) and across time (storage) uses various forms of matter and energy for conveyance; yet none of the physical laws that govern space, time, matter, and energy require information to exist. Indeed, in vast regions of the universe where there is no life, there is no [encoded] information.
Can Chemical Evolution Work?
Here I discuss Miller’s Experiment and related issues. The outcome of these experiments is like making jumbled piles of bricks, but no houses. The fundamental reason why experiments such as Miller’s don't make life out of non-life is that the chemistry isn't getting the informational guidance that it needs.
Life is more than chemistry
This expands on the previous blog. Life isn't just chemistry, but chemistry guided by the information stored in the DNA.
The Genetic Code - how to read the DNA record
Here I try to explain, in plain language as much as possible, how the DNA information is read and interpreted by the Genetic Code to construct the peptide chains that are the basis for all organic molecules. It is fascinating that there are potentially a vast number of possible genetic codes, or 'DNA languages', that would each work equally well; yet all living things on earth use the same 'language', and there is no evidence that there ever was any other 'language'.
The First Digitally-Controlled Designs
Here I observe that "The interpretation of the DNA information according to the Genetic Code creates a enormous set of specific proteins and other complex organic molecules that implement the structure and function of a particular organism" and that "All of these complex functions are guided not exclusively by chemical laws, but also by the information from the DNA." I point out that this is not only design, but digitally-controlled design; and I tell how in my engineering experience, I learned to appreciate that this is an optimum design paradigm. The first digitally-controlled designs were not computers, or the Jacquard looms and player pianos that preceeded computers; but were the living things that God created.
The Digital Control of Life
Here I provide further evidence of the similarity of the design of life and that of digital controllers.
In my retirement years, I've been studying the basics of biology and applying my expertise in information systems to investigate the fundamentals of the creation/evolution debate. I look at how living things work from the molecular level on up, and as a systems engineer I recognize a system design when I see one. Living organisms are also controlled by information and process information. Chemistry does the 'hardware' function, and DNA (with its derivatives) does the 'software' function.
I have published my findings, as well as common-language interpretations of other technical sources, on my blog. My blog talks about many other subjects, too, so if you are only interested in the creation/evolution/information stuff, go to the "Find by Subject" section on the right and click on one of those key words. Or you can start with the following overview of a few basic subjects:
Information From Randomness?
In this blog, I discuss the myth that information can somehow arise out of randomness, and discuss Dawkins' Weasel Algorithm in particular. In information theory, pure randomness is zero information. All systems that process information have a tendency to lose information, like the way they lose useful energy. So information always drifts toward randomness, not the other way around.
In The Beginning Was Information
Back in Darwin's day, evolution seemed somewhat plausible, just as the ether and phlogiston were once plausible. But more modern findings have unraveled the claims of evolution (macro-evolution, to be more precise), primarily the discovery that biology is chemistry guided by information. Since we know that information doesn't come from nothing, it begs the question: where did the information come from?
Is Encoded Information an Essential Part of the Universe?
In a previous blog, I had explained that space, time, matter, and energy are inseparable aspects of the universe. Here I argue that information is transcendent to all these. The transport of information across space (communication) and across time (storage) uses various forms of matter and energy for conveyance; yet none of the physical laws that govern space, time, matter, and energy require information to exist. Indeed, in vast regions of the universe where there is no life, there is no [encoded] information.
Can Chemical Evolution Work?
Here I discuss Miller’s Experiment and related issues. The outcome of these experiments is like making jumbled piles of bricks, but no houses. The fundamental reason why experiments such as Miller’s don't make life out of non-life is that the chemistry isn't getting the informational guidance that it needs.
Life is more than chemistry
This expands on the previous blog. Life isn't just chemistry, but chemistry guided by the information stored in the DNA.
The Genetic Code - how to read the DNA record
Here I try to explain, in plain language as much as possible, how the DNA information is read and interpreted by the Genetic Code to construct the peptide chains that are the basis for all organic molecules. It is fascinating that there are potentially a vast number of possible genetic codes, or 'DNA languages', that would each work equally well; yet all living things on earth use the same 'language', and there is no evidence that there ever was any other 'language'.
The First Digitally-Controlled Designs
Here I observe that "The interpretation of the DNA information according to the Genetic Code creates a enormous set of specific proteins and other complex organic molecules that implement the structure and function of a particular organism" and that "All of these complex functions are guided not exclusively by chemical laws, but also by the information from the DNA." I point out that this is not only design, but digitally-controlled design; and I tell how in my engineering experience, I learned to appreciate that this is an optimum design paradigm. The first digitally-controlled designs were not computers, or the Jacquard looms and player pianos that preceeded computers; but were the living things that God created.
The Digital Control of Life
Here I provide further evidence of the similarity of the design of life and that of digital controllers.
Saturday, July 18, 2009
Comparing Technologies
I heard that Wolfram Alpha was finally available, and I wanted to try it out. Wolfram Alpha is designed to be more than a search engine -- it's an answer engine. A search engine tries to find Web documents that contain information you want. But Wolfram Alpha will try to calculate an answer for you from data that it can access.
For example, if you want to know the "weight of the earth in pounds", it figures that (1) by "weight" you really meant mass, (2) the earth mass is available in a table of data about the planets of the solar system (although in metric units), (3) a table of conversion factors is available, and (4) a formula for converting units is available. Moreover, it has the 'smarts' to know that this is the data needed to get the answer, and it knows how to find and combine the details to get the answer.
Now, what problem would I use to try out this new answer engine? Well, I recall reading that DNA is an incredibly dense data storage and retrieval system, but I didn't have any number for the data density in, say, bytes per pound. So, I tried to get the number from Wolfram Alpha. But "DNA in pounds" was not precise enough. How much DNA? Just one 'base pair' (one unit of the chain), or an entire chromosome? And if a chromosome, which kind? (because they have different lengths)
DNA is a chain of information units called nucleotides. The chain is shaped like a twisted ladder, with each rung a pair of nucleotides that encodes two bits of information. There are four kinds of the nucleotides, so I began by asking for the mass of each kind, using their chemical names:
adenine mass in pounds: 4.9468*10-25 lb
guanine mass in pounds: 5.53252*10-25 lb
thymine mass in pounds: 4.51683*10-25 lb
cytosine mass in pounds: 4.06729*10-25 lb
I also needed the mass of the 'backbone' unit, for the 'sides' of the ladder:
deoxyribose mass in pounds: 4.45458*10-25 lb
Then, assuming that the four nucleotide types are used equally, I could now compute the data density of DNA:
1.084547*1024 bytes per pound
(That's about a one followed by 24 zeros.)
Now, what man-made data storage and retrieval system could I compare this to? I have an 8 GB thumb drive that weighs a quarter of an ounce, which may not be the most dense, but it's denser than a DVD or a hard drive. I calculated it's data density to be:
5.5*1011 bytes per pound
That means that DNA is about two trillion times more dense than the thumb drive. That is, the data capacity of a quarter of an ounce of DNA is equal to about two trillion 8 GB thumb drives! Engineers would love to be able to design a data storage and retrieval system with the density of DNA, but they don't know how.
Yet there are atheistic scientists that believe that mindless evolution accidentally created DNA millions of years ago. I have two reactions to this evolutionary belief:
First, as an engineer, I feel insulted that people actually think that a random process can out-do what none of my engineering colleagues can accomplish.
Second, it is clear to me that I don't have enough faith to be an atheist.
For example, if you want to know the "weight of the earth in pounds", it figures that (1) by "weight" you really meant mass, (2) the earth mass is available in a table of data about the planets of the solar system (although in metric units), (3) a table of conversion factors is available, and (4) a formula for converting units is available. Moreover, it has the 'smarts' to know that this is the data needed to get the answer, and it knows how to find and combine the details to get the answer.
Now, what problem would I use to try out this new answer engine? Well, I recall reading that DNA is an incredibly dense data storage and retrieval system, but I didn't have any number for the data density in, say, bytes per pound. So, I tried to get the number from Wolfram Alpha. But "DNA in pounds" was not precise enough. How much DNA? Just one 'base pair' (one unit of the chain), or an entire chromosome? And if a chromosome, which kind? (because they have different lengths)
DNA is a chain of information units called nucleotides. The chain is shaped like a twisted ladder, with each rung a pair of nucleotides that encodes two bits of information. There are four kinds of the nucleotides, so I began by asking for the mass of each kind, using their chemical names:
adenine mass in pounds: 4.9468*10-25 lb
guanine mass in pounds: 5.53252*10-25 lb
thymine mass in pounds: 4.51683*10-25 lb
cytosine mass in pounds: 4.06729*10-25 lb
I also needed the mass of the 'backbone' unit, for the 'sides' of the ladder:
deoxyribose mass in pounds: 4.45458*10-25 lb
Then, assuming that the four nucleotide types are used equally, I could now compute the data density of DNA:
1.084547*1024 bytes per pound
(That's about a one followed by 24 zeros.)
Now, what man-made data storage and retrieval system could I compare this to? I have an 8 GB thumb drive that weighs a quarter of an ounce, which may not be the most dense, but it's denser than a DVD or a hard drive. I calculated it's data density to be:
5.5*1011 bytes per pound
That means that DNA is about two trillion times more dense than the thumb drive. That is, the data capacity of a quarter of an ounce of DNA is equal to about two trillion 8 GB thumb drives! Engineers would love to be able to design a data storage and retrieval system with the density of DNA, but they don't know how.
Yet there are atheistic scientists that believe that mindless evolution accidentally created DNA millions of years ago. I have two reactions to this evolutionary belief:
First, as an engineer, I feel insulted that people actually think that a random process can out-do what none of my engineering colleagues can accomplish.
Second, it is clear to me that I don't have enough faith to be an atheist.
Tuesday, June 02, 2009
A Disingenuous Argument
In Steve Mirsky's article An Immodest Proposal in the Opinion section of the June 2009 Scientific American (p. 37), Mirsky quotes from Jonathan Wells' article Darwin's Straw God Argument on the Discovery Institute web site (http://www.discovery.org/a/8101) without the courtesy of naming the article and with the discourtesy of insulting the name of the web site. The quote:
Darwinism depends on the splitting of one species into two, which then diverge and split and diverge and split, over and over again, to produce the branching-tree pattern required by Darwin’s theory. And this sort of speciation has never been observed.
Then, apparently pretending to be ignorant of the fact that most creationists, and Wells in particular, make a distinction between macroevolution and microevolution, Mirsky goes on to waste an entire page of ink to propose that the breeding of dogs is proof that the sort of speciation that created all of the species has indeed been observed.
The first part of Wells' paragraph from which Mirsky quotes reads:
The best way to find “evolution’s smoking gun” would be to observe speciation in action. There actually are some confirmed cases of observed speciation in plants -- all of them due to an increase in the number of chromosomes, or “polyploidy.” But observed cases of speciation by polyploidy are limited to flowering plants, and polyploidy does not produce the major changes required for Darwinian evolution.
Later in Wells' article, he writes:
So although Darwinists believe that all species have descended from a common ancestor through variation and selection, they cannot point to a single observed instance in which even one species has originated in this way. Evolution's smoking gun is still missing, and Dobzhansky’s working assumption that macroevolution equals microevolution remains nothing more than an assumption.
So it is obvious that Wells makes a distinction between macroevolution and microevolution. For the sake of readers not familiar with these terms, I will briefly explain: Microevolution refers to the small genetic changes as observed within the various 'kinds' of life. Macroevolution assumes that larger genetic changes or an accumulation of small genetic changes has produced all the species from a common ancestor. Microevolution postulates many genetic trees, and macroevolution postulates one tree. In both cases, the details of the tree branching are only estimates, and for microevolution the division of 'kinds' is also estimated.
Microevolution, creationists admit, has been observed. (So has the continual breaking of world records. But does that even suggest, let alone prove, that one day athletes will jump across the Hudson River from Nyack to Tarrytown?)
So Mirsky's disingenuous proposal does not disprove Wells' statement. His line of argument needs an observation that breeding of dogs has produced cats or lizards or anything other than more dogs.
Saturday, February 21, 2009
The Genetic Code - how to read the DNA record
(NOTE: The end of this article has been revised and expanded from the original.)
DNA is the kind of molecule that stores genetic information in every living cell. It describes how our bodies are made, and to a degree, how they operate. The translation of DNA, a sequence of nucleotides, to a sequence of amino acids (protein units) is a complex but fascinating process. Here's a simplified account of the essentials:
A selected portion of the DNA is copied in complementary form, making a messenger RNA (mRNA) chain molecule. There are four kinds of nucleotide in the DNA, abbreviated G, T, A, and C; and four kinds in the RNA, called C, A, U, and G. When copying from DNA to RNA, the correspondence is:
G -> C
T -> A
A -> U
C -> G
So, for example the DNA sequence
GTACCATG..
when copied to RNA, makes the RNA sequence
CAUGGUAC..
A sequence of three nucleotides, such as GCC, is called a codon. Each codon sequence encodes for one of 20 amino acids, or else is a stop codon. The genetic code is a scheme that translates the 64 (4 x 4 x 4) types of codon to the 20 amino acids and the stop signal. The codon for the amino acid Methionine also functions as a start signal. There are three codons that mean 'stop', and there are one to six codons representing each amino acid. Here's the complete genetic code:
[START], Methionine <-- AUG
Alanine <-------- GCU, GCC, GCA, GCG
Leucine <-------- UUA, UUG, CUU, CUC, CUA, CUG
Arginine <------- CGU, CGC, CGA, CGG, AGA, AGG
Lysine <--------- AAA, AAG
Asparagine <----- AAU, AAC
Aspartic acid <-- GAU, GAC
Phenylalanine <-- UUU, UUC
Cysteine <------- UGU, UGC
Proline <-------- CCU, CCC, CCA, CCG
Glutamine <------ CAA, CAG
Serine <--------- UCU, UCC, UCA, UCG, AGU, AGC
Glutamic acid <-- GAA, GAG
Threonine <------ ACU, ACC, ACA, ACG
Glycine <-------- GGU, GGC, GGA, GGG
Tryptophan <----- UGG
Histidine <------ CAU, CAC
Tyrosine <------- UAU, UAC
Isoleucine <----- AUU, AUC, AUA
Valine <--------- GUU, GUC, GUA, GUG
[STOP] <--------- UAG, UGA, UAA
The key elements of translation are small transfer RNA (tRNA) molecules. Each kind of tRNA molecule has a region called the anticodon that can recognize and attach to a particular codon of a messenger RNA (mRNA) molecule. The tRNA molecule has another region called the "3' terminal" that attaches to a particular amino acid. This attachment is aided by molecules called aminoacyl-tRNA synthetases, of which there is generally one kind for each kind of amino acid. There are even helper molecules that provide a proofreading function to detect and correct any translation errors.
Each kind of tRNA molecule associates one kind (sometimes a few kinds) of codon with a particular amino acid, so there are one or more kinds of tRNA for each row of the above genetic code table. For example, there is a kind of tRNA with a region that attaches to Tryptophan (with the help of a specific kind of aminoacyl-tRNA synthetase), and with another region that recognizes and attaches to any part of mRNA with a UGC codon.
So if the RNA sequence is
AUGUUCUUAUACUCCUAG
we can divide it into codons as
AUG UUC UUA UAC UCC UAG
Five tRNA molecules will attach to the first five codons, and five amino acids will attach to the tRNA molecules, something like this (with abbreviated names for the amino acids):
No tRNA molecule will attach to the last codon, because it is a stop codon, and the translation will stop.
The amino acids connect into a chain in this sequence, like this, which detach from the tRNA molecules:
Met-Phe-Leu-Tyr-Ser
Each tRNA molecule detaches from the mRNA and from the chain of amino acids, to be 'loaded' with another amino acid and used again. The detached chain of amino acids, a protein, folds into a three-dimensional shape to function as a protein. (This folding is another complex process, often needing the aid of specialized helper molecules.)
These are the basics of the translation, but it is actually more complex than this, because other molecular machinery is needed to make everything happen in the right sequence. The 'work bench' of the mRNA reading machinery is a collection of tiny particles called ribosomes that look like tiny dots in the center of a living cell (but huge compared to the tRNA molecules). There are also other tools such as initiation factors, releasing factors, and various enzymes that control the process.
Each ribosome has a small and large unit that link together on either side of the mRNA ribbon, forming a bead that can slide along the mRNA, reading it. Many ribosomes typically read one mRNA strand at one time, producing proteins. Each ribosome has three sites on one side of the hole through the 'bead' that hold tRNA molecules in position to attach to, and detach from, the mRNA as it passes through the hole. The ribosome 'workbench' has other sites to hold the various other 'tools' in position to operate on the various stages of the process.
Where does the genetic code come from? It is not the result of chemistry or any laws of physics. It is determined by the set of tRNA molecule types, and aminoacyl-tRNA synthetase types, which are constructed according to DNA information, which encodes not only the building materials and the building plans, but also the building tools and the building methods. In other words, the genetic code is just information that has always been there since life began.
The number of possible genetic codes is a huge number, 85 digits long:
1,510,109,515,792,918,244,116,781,339,315,785,081,841,294, 607,960,614,956,302,330,123,544,242,628,820,336,640,000
and all of these many codes would work equally well. But all of life uses just one genetic code, about 280 bits of information, the one that scientists Watson and Crick discovered in 1953, but was there since creation. The theory of evolution has no explanation for how the genetic code began, because it can't explain how information can arise from no information. Nor can it explain why there is only one genetic code (out of such a huge number of equally workable codes), even though there is extreme variation of everything else. The mechanism of the present genetic code is very complex; and evolutionary theory supposes that it randomly evolved from a simpler, smaller code. But because there are so many equally viable genetic codes, random evolution should have produced species with many different codes. The evolutionary explanation is far more unlikely than dumping a bucketful of dice on the floor and expecting them to all land with the same number up.
The creationist explanation is that the universal genetic code is like a signature of the creator, who chose a uniform code for all of the designs of life. A short story will illustrate the principle:
During the Cold War, Russia was suspected of stealing American technology. Proof came when some Russian war equipment given to a third country was captured and examined. It contained an integrated circuit that was identical to an American design. It is theoretically possible that the Russians had the same design concept, leading to a similar design. But digital circuits have thousands of component parts connected by thousands of wires. There trillions of ways to position the parts on the chip and trillions of ways to route the connecting wires that work equally well. It would be impossible for the Russians to independantly produce the same positions and routings even if the logical design were identical. But examination showed the details were identical, even details left over from correcting wiring errors. In effect, there was an American 'signature' in the copied design.
DNA is the kind of molecule that stores genetic information in every living cell. It describes how our bodies are made, and to a degree, how they operate. The translation of DNA, a sequence of nucleotides, to a sequence of amino acids (protein units) is a complex but fascinating process. Here's a simplified account of the essentials:
A selected portion of the DNA is copied in complementary form, making a messenger RNA (mRNA) chain molecule. There are four kinds of nucleotide in the DNA, abbreviated G, T, A, and C; and four kinds in the RNA, called C, A, U, and G. When copying from DNA to RNA, the correspondence is:
G -> C
T -> A
A -> U
C -> G
So, for example the DNA sequence
GTACCATG..
when copied to RNA, makes the RNA sequence
CAUGGUAC..
A sequence of three nucleotides, such as GCC, is called a codon. Each codon sequence encodes for one of 20 amino acids, or else is a stop codon. The genetic code is a scheme that translates the 64 (4 x 4 x 4) types of codon to the 20 amino acids and the stop signal. The codon for the amino acid Methionine also functions as a start signal. There are three codons that mean 'stop', and there are one to six codons representing each amino acid. Here's the complete genetic code:
[START], Methionine <-- AUG
Alanine <-------- GCU, GCC, GCA, GCG
Leucine <-------- UUA, UUG, CUU, CUC, CUA, CUG
Arginine <------- CGU, CGC, CGA, CGG, AGA, AGG
Lysine <--------- AAA, AAG
Asparagine <----- AAU, AAC
Aspartic acid <-- GAU, GAC
Phenylalanine <-- UUU, UUC
Cysteine <------- UGU, UGC
Proline <-------- CCU, CCC, CCA, CCG
Glutamine <------ CAA, CAG
Serine <--------- UCU, UCC, UCA, UCG, AGU, AGC
Glutamic acid <-- GAA, GAG
Threonine <------ ACU, ACC, ACA, ACG
Glycine <-------- GGU, GGC, GGA, GGG
Tryptophan <----- UGG
Histidine <------ CAU, CAC
Tyrosine <------- UAU, UAC
Isoleucine <----- AUU, AUC, AUA
Valine <--------- GUU, GUC, GUA, GUG
[STOP] <--------- UAG, UGA, UAA
The key elements of translation are small transfer RNA (tRNA) molecules. Each kind of tRNA molecule has a region called the anticodon that can recognize and attach to a particular codon of a messenger RNA (mRNA) molecule. The tRNA molecule has another region called the "3' terminal" that attaches to a particular amino acid. This attachment is aided by molecules called aminoacyl-tRNA synthetases, of which there is generally one kind for each kind of amino acid. There are even helper molecules that provide a proofreading function to detect and correct any translation errors.
Each kind of tRNA molecule associates one kind (sometimes a few kinds) of codon with a particular amino acid, so there are one or more kinds of tRNA for each row of the above genetic code table. For example, there is a kind of tRNA with a region that attaches to Tryptophan (with the help of a specific kind of aminoacyl-tRNA synthetase), and with another region that recognizes and attaches to any part of mRNA with a UGC codon.
So if the RNA sequence is
AUGUUCUUAUACUCCUAG
we can divide it into codons as
AUG UUC UUA UAC UCC UAG
Five tRNA molecules will attach to the first five codons, and five amino acids will attach to the tRNA molecules, something like this (with abbreviated names for the amino acids):
No tRNA molecule will attach to the last codon, because it is a stop codon, and the translation will stop.
The amino acids connect into a chain in this sequence, like this, which detach from the tRNA molecules:
Met-Phe-Leu-Tyr-Ser
Each tRNA molecule detaches from the mRNA and from the chain of amino acids, to be 'loaded' with another amino acid and used again. The detached chain of amino acids, a protein, folds into a three-dimensional shape to function as a protein. (This folding is another complex process, often needing the aid of specialized helper molecules.)
These are the basics of the translation, but it is actually more complex than this, because other molecular machinery is needed to make everything happen in the right sequence. The 'work bench' of the mRNA reading machinery is a collection of tiny particles called ribosomes that look like tiny dots in the center of a living cell (but huge compared to the tRNA molecules). There are also other tools such as initiation factors, releasing factors, and various enzymes that control the process.
Each ribosome has a small and large unit that link together on either side of the mRNA ribbon, forming a bead that can slide along the mRNA, reading it. Many ribosomes typically read one mRNA strand at one time, producing proteins. Each ribosome has three sites on one side of the hole through the 'bead' that hold tRNA molecules in position to attach to, and detach from, the mRNA as it passes through the hole. The ribosome 'workbench' has other sites to hold the various other 'tools' in position to operate on the various stages of the process.
Where does the genetic code come from? It is not the result of chemistry or any laws of physics. It is determined by the set of tRNA molecule types, and aminoacyl-tRNA synthetase types, which are constructed according to DNA information, which encodes not only the building materials and the building plans, but also the building tools and the building methods. In other words, the genetic code is just information that has always been there since life began.
The number of possible genetic codes is a huge number, 85 digits long:
1,510,109,515,792,918,244,116,781,339,315,785,081,841,294, 607,960,614,956,302,330,123,544,242,628,820,336,640,000
and all of these many codes would work equally well. But all of life uses just one genetic code, about 280 bits of information, the one that scientists Watson and Crick discovered in 1953, but was there since creation. The theory of evolution has no explanation for how the genetic code began, because it can't explain how information can arise from no information. Nor can it explain why there is only one genetic code (out of such a huge number of equally workable codes), even though there is extreme variation of everything else. The mechanism of the present genetic code is very complex; and evolutionary theory supposes that it randomly evolved from a simpler, smaller code. But because there are so many equally viable genetic codes, random evolution should have produced species with many different codes. The evolutionary explanation is far more unlikely than dumping a bucketful of dice on the floor and expecting them to all land with the same number up.
The creationist explanation is that the universal genetic code is like a signature of the creator, who chose a uniform code for all of the designs of life. A short story will illustrate the principle:
During the Cold War, Russia was suspected of stealing American technology. Proof came when some Russian war equipment given to a third country was captured and examined. It contained an integrated circuit that was identical to an American design. It is theoretically possible that the Russians had the same design concept, leading to a similar design. But digital circuits have thousands of component parts connected by thousands of wires. There trillions of ways to position the parts on the chip and trillions of ways to route the connecting wires that work equally well. It would be impossible for the Russians to independantly produce the same positions and routings even if the logical design were identical. But examination showed the details were identical, even details left over from correcting wiring errors. In effect, there was an American 'signature' in the copied design.
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