?_`l!*b<Weasel+Copyright (C) 2000-2001 Answers in GenesisZpagewin4ZdefwinqZpopupwnm  /&;)z4\|CONTEXT$0|CTXOMAP|FONT|KWBTREE |KWDATA|KWMAP |SYSTEM|TOPIC|TTLBTREE#|VIOLA+|bm0S8|bm1<|bm2B|bm3D 7C1CDGospel 4 6KThe reason for this messageSome may be wondering why this Gospel message has been added to this help file. One of the things most valued by a western society is freedom, freedom of speech and the freedom to choose. This freedom comes from the Christian heritage of these countries. This message has been added to make sure that users of this program have the opportunity, or freedom, to follow Jesus Christ. eC( Do you know that there is a Creator-God who made us and will hold us accountable for how we live?'$  % We believe the simulations in this program demonstrate beyond all reasonable doubt that living creatures could not have arisen by a stepwise sequence of lucky accidentsit follows that a super-intelligent engineer, a Creator, must have made them.The Bible claims to be the written revelation of the Creator, Almighty God, to man the Creator's message to us, His creatures. As His creatures, He owns us and we are accountable to Him for how we live our lives (Romans 14:12, Hebrews 9:27). The Bible tells us that we all, like Adam, the first man, have departed from God's ways; we have gone our own way, living life as if we were God, in effect. This, the Bible calls 'sin'. We have all sinned (Romans 3:23).[6% mThe Bible also tells us that God will hold us accountable for our sin. Like Adam, we all deserve God's judgment for our sin. As descendants of Adam, we all suffer physical death at the end of this earthly life. The Bible calls this death a curse and 'the last enemy' (Genesis 3:19, 1 Corinthians 15:26). It came about because of Adam's sin, when he by his actions effectively told God that He was not neededAdam was going to be his own god. However, each one of us has effectively endorsed Adam's action, in ourselves rejecting God's rule over us (Romans 5:12). - (Good news!The Good News is that God has provided a way of escape from the curse of death and the judgment to come. 'For God so loved the world that He gave his only begotten Son, that whoever believes in Him should not perish, but have everlasting life' (John 3:16). Jesus Christ came into the world, born of a woman, to take upon Himself the curse and penalty for our sins. As God in the flesh (Colossians 2:9), the God-man Jesus lived a sinless life (Hebrews 4:15) and willingly gave Himself to suffer death for us, in our place (Romans 5:8, 1 Peter 3:18). He took upon himself the punishment for our sins. As He was God (as well as man), His life was of sufficient value to pay for the sins of any number of people.>Y' /God offers this free gift of salvation to all who will receive it. He calls upon all to turn away from their sinful ways and trust in what Christ has done for us. There is nothing we can do to remove our guilt before God. Doing good things does not remove our sin, and since we are all sinners, nothing we can do can undo that; it is only by the mercy of God that we can be saved through what He has done (Ephesians 2:8,9).On the other hand, whoever spurns God's offer will suffer His wrath in the judgment to come, which the Bible clearly warns. This is a terrifying prospect (2 Thessalonians 1:8-9). Jesus spoke much of this, warning people of their fate. The Bibles book of Revelation uses graphic imagery to depict the dreadful future of those who reject God's mercy here and now. aA- (How can I be saved?If God has shown you that you are an unworthy sinner, deserving of God's condemnation, in need of His forgiveness, then the Bible says that you must have 'repentance toward God and faith toward our Lord Jesus Christ' (Acts 20:21). Repentance means a complete change of heart and mind regarding sinthat you agree with God about your sin and now want to YaA live a life pleasing to Him. Faith in Jesus Christ entails accepting who He is, 'the Son of the living God', that 'Christ died for the ungodly' and that He conquered death in His resurrection (1 Corinthians 15:1-4,21,22). You must believe that He is able to save you, and you must put your trust in Christ alone to make you right with God.oGYD( If God has shown you your need and given you the desire to be saved, then turn to Christ now. Speak to Him, admitting that you are a guilty, helpless sinner, and ask Him to save you and be Lord of your life, helping you to leave behind your sinful ways and live for Him. The Bible says, 'if you confess with your mouth the Lord Jesus, and believe in your heart that God raised Him from the dead, you shall be saved' (Romans 10:9). If you have prayed in this manner, then you should find some Christians who hold to the Bible as God's Word and ask them to help you as you learn to live as God wants you to live. Please let us know of your decision by emailing mail@answersingenesis.org . If you have unanswered questions, use the search function at http://www.answersingenesis.org to find the answers to many commonly asked questions.&aAD# 9D/E13/E)GTutorial+D)G lWHH,Tutorial Introduction How Dawkins program worked Error Catastrophe Realistic Mutation Rates DNA Model Irreducible Complexity ConclusionFor information on a wide range of topics visit www.answersingenesis.org and use the search facility.= /EfG1fGLIntroduction1)GLJ b H;Ή;Ή IntroductionThe astronomer, Fred Hoyle said the probability of the formation of just one of the many proteins on which life depends is comparable to that of the solar system packed full of blind people randomly shuffling Rubik's cubes all arriving at the solution at the same time. [see ref] . In response to statements like that, evolutionists try to avoid the issue by breaking evolutionary stages down into small and gradual steps. Richard Dawkins is a prominent atheist and author of the book, The Blind Watchmaker, [see ref] . Dawkins puts forward the idea that cumulative selection is able to produce complex proteins (such as haemoglobin), which are obviously too improbable to arise in a single step. Dawkins wrote a computer program to demonstrate cumulative selection and mentions this program in his book. The program would select a random sequence of letters and gradually mutate this sequence until it matched a phrase that Dawkins selected from Shakespeares Hamlet. However as you read this tutorial you will see that when Dawkins program is correctly understood, it not only fails to provide any real support for molecules-to-man evolution, it can in fact, be used to demonstrate problems with the theory of evolution.g;fGL, (vHTo continue with the tutorial press on the >> button.LLJM1uJMHow Dawkins program workedLiO0 .HHow Dawkins program workedTo begin with a target string of letters was chosen. Dawkins chose, "METHINKS IT IS LIKE A WEASEL". Next, the computer generated a sequence of random uppercase letters to represent the original organism. (This sequence always contained exactly the same number of letters as the target phrase).The parent sequence would be copied several times to represent reproduction. With each copy there would be a chance of a random error, a mutation, in the copying.S*JMȁ) UHNow for what was supposedly analogous to selection, each copy would now be tested to determine which copy waiOȁLs most like the target string METHINKS IT IS LIKE A WEASEL. A copy would be chosen even if only one letter matched the target in the correct place, so long as it happened to be the best match. The chosen copy would now be copied several times, again with introduced errors in the copying. In turn this 'progeny' was also tested to find the best match. This process would be repeated until a copy was found that matched the target exactly.iOC T;HTo see an active demonstration of a similar model, click on the Models menu and select Dawkins (default). Press the key to start the demonstration.By clicking on the options on the main form and pressing the key, you will be able to read a description and comments about each of the options, (this also includes the disabled options).To continue with the tutorial press on the >> button.Bȁ1 ;8Error Catastrophe^$H: BIH;ΉError CatastropheError catastrophe occurs when genetic information is destroyed by mutations at such a rate that on average over the generations all progeny are less fit than their parents so that selection cannot maintain the integrity of the genome and, in a Dawkinsian-type simulation, a target sequence cannot be achieved. To avoid error catastrophe the mutation rate (per letter or base per generation) has to be inversely proportional to the size of the genome. That is, the larger the genome, the lower the mutation rate. Once this is factored into the theory, 'evolution' slows down to such a slow pace that it could never account for the amount of biological information in existence (the basic point of "Haldanes Dilemma", which Walter ReMine spells out [see ref] Chapters 7-9. (  HThe maximum sustainable mutation rate depends on the length of the genome. For example the model will converge with a mutation rate of 1 in 28 with a target of 28 letters (with an offspring count of 100), but not on a genome just a little bit bigger and certainly not with a human-sized genome of 3x10^9 nucleotides. It becomes clear that with a small offspring number (e.g. humans), the substitution mutation rate cannot be much more than one in the length of the target sequence. E.g., if the target is 99 base pairs (33 amino acids), then with 8 offspring, a mutation rate of 1 in 50 produces error catastrophe. This is because the probability of getting two "information-adding" mutations occurring together becomes vanishingly small and furthermore, as the sequence progresses somewhat towards the target, the extra mutation is more and more likely to undo what has already been achieved. RH}5 8H;ΉIn effect the mutation rate cannot be much greater than one per genome per generation. This then severely limits the rate of progress from a chimp-like species to human, if this were possible, even with perfect selection and all the other assumptions.The bioinformation expert Spetner [see ref] p131 points out that no one has found a single point mutation that adds biological information (specified complexity). This is not to say that such a mutation cannot or does not happen, just that it cannot be the mechanism for generating the amount of biological information that we see.HB R H To see an active demonstration of error catastrophe, select the Models menu and then select Error Catastrophe and then press the key. Just a word of warning here, the model is extremely unlikely to finish regardless of how long you leave it running, so please note that the key will stop the run when you are ready to stop it. You can try your own variations, you can adjust the offspring count, target size and mutation rates. If you start with the Dawkins model and adjust the options, you should find that with an offspring count of about 8 that erro}r catastrophe will occur with mutation rates that are higher than 1 in the length of the target. As you increase the length of the target you will need to decrease the mutation rate accordingly. g;}8, (vHTo continue with the tutorial press on the >> button.I1. чfRealistic Mutation Rates*8i UH;Ή;Ή;Ή Realistic Mutation RatesHow does the mutation rate of Dawkins model compare to realistic mutation rates?Spetner in his book Not by Chance [see ref] p 92 refers to the figures of Fersht, Drake [see ref] and Grosse et. al. [see ref] .In bacteria the mutation rate per nucleotide is between 0.1 and 10 per billion transcriptions [Fersht 1981, Drake 1969, 1991]. But in all other forms of life the rate is smaller. For organisms other than bacteria, the mutation rate is between 0.01 and 1 per billion [Grosse et al. 1984].h3|5 8gHBiological replication is extremely accurate. This level of accuracy is due to the processes of proof reading and error correction. This is vital since mutations disorder existing functional DNA sequences, and are therefore overwhelmingly harmful or at best neutral.For a demonstration of a more realistic mutation rate, click on the Models menu and select Adjusted Mutation Rate. This model may take a long time to run. It could take a few weeks on a typical slower P.C. Press the key to start the demonstration. Press at any time to stop it. ' HThere is an option to speed up the running of a model with realistic mutation rates. This can be found under as Calc mutations by gaps?. This has been done using an exponential probability distribution, :|' &@Hf(T)=pexp(-pT), sM4& Hwhere p is the mutation probability per nucleotide copied and 1/p is the mean time (in nucleotides copied) until a mutation occurs. To generate the probabilistic number of nucleotides, T, until the next mutation, a random number z, from the uniform distribution from 0 to 1, is inserted into the transformed exponential equation,2f= HH T = -ln(z)/p.Of course the Adjusted Mutation Rate model is still extremely unrealistic but it does help to illustrate the point that real life is nothing like the Dawkins Model.To continue with the tutorial press on the >> button.: 41;DNA Model!fT vHoDNA ModelSee the Appendix for information on how DNA is used to build a peptide. As noted in the Using the DNA model section, an important difference between the DNA model and the Dawkins' Model or alphabet model is that the 'DNA' of an organism is not compared directly with the target as it is in Dawkins Model. Another important factor is redundancy, some of the amino acids can be coded for by different codons. In some codons only the first two base pairs are needed to determine which amino acid is produced. This gives the genetic code some resistance to change. In some cases you would require more than one mutation to convert the code of one expressed amino acid into the code of another.: BHWhen you run the DNA model you may notice that even though there are only four possible letters compared to 27 in the Dawkins Model that a target requiring 30 base pairs takes close to twice the number of generations to be reached as does the target METHINKS IT IS LIKE A WEASEL, which is 28 characters in length.The demonstration of the DNA model can be accessed by clicking on the Models menu and selecting DNA Model. Press the key to start the demonstration./ ,cHfA user-defined peptide can be entered in the window. The editing box to the right of the sequence window facilitates quick entry of amino acid sequences using short cuts. Redundancy at a particular locus can be entered by placing the range of amino acids possible within square brackets; for example, [LysAlaGly] will allow any of these three amino acids to be selected.To continue with the tutorial press on the >> button.G 1ч s Irreducible ComplexityA PkH;ΉIrreducible Complexity Michael Behe uses a mousetrap to illustrate the concept of irreducible complexity in his book Darwins Black Box [see ref] . He points out that the individual parts of a mousetrap cannot function independently of each other. In fact if you remove a single part or change its dimensions to a significant degree, the mousetrap will fail to function at all. This (among other issues) makes a mousetrap resistant to any step-wise explanation of its origin; that, of course, is without the aid of an intelligent designer. Dawkins weasel analogy fails to address this issue. It does not demonstrate how a suite of interdependent proteins can evolve in parallel.7 :' !H In part II of his book, Behe discusses several irreducibly complex systems such as blood clotting where there is no conceivable step wise build up of functionality. For example, the proteins involved in blood clotting are required to act in unison. Its not just a case of lacking a little functionality if an essential protein is missing because the whole system is finely balanced. On the one hand, if one component is missing an animal could bleed to death, on the other hand, all of the animals blood could become one massive blood clot. These kinds of systems are all or nothing systems. Dawkins weasel analogy assumes functionality for any and every step in the run of the model with the only requirement for selection being greater likeness to the pre-specified goal. 9s E XH;Ή Michael Behe addresses Dawkins response to Paley [see ref] page 213:Neither Darwin nor Dawkins, neither science nor philosophy has explained how an irreducibly complex system such as a watch might be produced without a designer. Dawkins concept of a slow build up of functionality is not valid for a system of proteins that have no function at all until all the proteins are present in the correct amounts and at the same time.To continue with the tutorial press on the >> button.; : 1} @Conclusion(s  % H2  % HConclusion( 0 % Hh & HDawkins weasel program does not generate any new informationthe information was completely specified by the target phrase plus the deterministic algorithm which ensures convergence. The target phrase is effectively a mould (or a funnel) that is used to shape the virtual species. Perfect selection that is goal based hammers this species until it is forged into the likeness of the predetermined target. There is no mould that natural selection can use. The program uses many such unrealistic assumptions that favour evolutionary theory. When the parameters of Dawkins weasel analogy are modified, it can be seen how carefully Dawkins chose the values for the parameters. Far from demonstrating how inevitable evolution is, the program can be used to show that error catastrophe is a major hindrance to the speed at which evolution could occur (even when ignoring all the other unrealistic assumptions). Added to that, the issue of irreducible complexity makes it clear that the vast amount of biological information that we see in life today could not have arisen from random processes, even with selection aiding it.(0  @% H @s @) H If you value the research and effort that has produced this program, you might like to make a donation to Answers in Genesis.7 @@1 @CHow to!6@C mHN  Z # * 22 k9 @ G How to Run a demonstration Play a sound when finished Enable logging of statistics to a file Edit the target Edit a target peptide Select a preset model Use custom models Control the use of random numbers Use the options on the main form7@CL hnHO $k Use the DNA model Use the main window DCC1A CIRun a demonstrationECFU xH" Running a demonstrationThe demonstration actions are available through the Demonstration Menu or the toolbar.Go Step Pause Stop TutorialFor shortcut keystrokes you can use the F2 - F5 keys as listed next to the option headings below. For example, press the F2 key to start a demonstration.Go or F2The button starts or resumes the demonstration. Step or F3The button steps through one generation. This is only available while the demonstration is paused.C!H7 <HPause or F4Temporarily pauses the demonstration. When a demonstration is paused you can press to resume, to apply one generation at a time or to permanently stop the active demonstration.Stop or F5Stops the current demonstration. Note: you will not be able to resume a demonstration after you select . If you run a model using a long target or a large offspring count it may take some time before the stop command takes effect.GFIj H+G 22 TutorialClick on the tutorial button for information on how the program works, what the models demonstrate and a discussion on cumulative selection.For information on changing models, see Changing the options on the Main Form . For a guide on running demonstrations, see Selecting a preset model. c2!H5J1 5JLPlaying a sound when the demonstration is finishedLIL5 8/HPlaying a sound when the demonstration is finishedIf you have a sound card and speakers setup on your computer then you can have a sound played to alert you when the program has finished running a model.This plays the default sound wave that can be configured in windows via Start - Settings - Control Panel - Sounds.To enable this option click on the Edit menu on the main form and then click on the options item.A window will appear with a check box on it. Click on the check box to turn the play sound setting on or off.(5JL% H FLL1 LfEnabling the log fileO L>OF ZH  Enabling the log fileA log file can be generated if you wish to log items such as the statistics for every generation, best individuals and each individual of every generation.This option can be found under within the log file panel.Configure the log file using the following setting.The Enable log file check box: Switches logging on/off.The Filename edit box: Allows you enter the file name of the log file. Alternately you can click on the button to select the log file.-Lw> JHThe Log Statistics check box: Enables the logging of Years, Mutations, Generations and Nucleotides for each generation. The r>OwLadio buttons Log best individual only and Log entire population allow you to choose between logging changed individuals only or logging every individual for each generation.Best individuals are logged only for generations where there is a change in the genome.The format of the log file is a standard text file which can be viewed in most text editors.>Of& HBe cautious with the settings for the log file. The logging will slow down the execution of the program and will use up disk space. Various settings may result in the generation of a large log file.Cw1~ Editing the target;fU xH * Editing the targetFor the Dawkins' model you can change the target from 'METHINKS IT IS LIKE A WEASEL' to a target of your own choice if you wish.Just click on the Target box on the main form and type in the new target.You should use only capital letters and spaces i.e. 'A' - 'Z' and ' '. (Don't enter the quotes).If the DNA Model? option is set to Yes then you will be more restricted in what you can enter. See Editing a target peptide for more information. I-1-Editing a target Peptide{ Ā-Hv)Ή)Ή j͉O΀o Editing a target peptide The are two approaches you can take to enter a peptide.1. The Target Box on the main form. (Advanced users) 2. The Target Peptide entry window.(See DNA model? , Using the DNA model and the appendix for more details)P-1The target box on the main form.<0 .H The target box on the main formIf you are an advanced user and you would like to enter a short peptide and do not need to save the peptide for later use, you can type a peptide straight into the target box on the main form.To do this, you enter the 3-letter abbreviation for every amino acid that you wish to add, spaces and commas are not needed. You may add a stop codon as 'STP' (without the quotes) on the end of the sequence. Do not enter more than one stop codon and make sure it is at the end._7( oHHere is an example of what you can type. ArgSerAsnThrSTP You can enter redundancy at a site or sites by enclosing the permissible amino acid abbreviations within square brackets within the sequence. E.g. [AlaAsnSer].Before you run the demonstration, on the main form set the DNA Model? option to Yes.F<17 Target Peptide window6 :HTarget peptide windowThe Target Peptide Window provides you with an edit window where you can view and edit larger peptides and save them for later use.To access the target peptide edit window, follow these steps.1. On the main form select DNA Model .2. Click on the button on the right hand side of the target Edit box or Click on the Edit menu and then select the Target Peptide optionHere are the features of this window ... J06 :)HThe Target Peptide Edit MemoThis is the large entry box on the form, and this is where you can enter the sequence of amino acids that make up the target peptide. You have three choices, you can enter the name of the amino acid in full, type in it's three letter abbreviation or by using the shortcut panel you can type in the single character short cut. Be sure to type at least one space or new line (Enter) between each amino acid.For example the sequence A, R, N, STP translates to Alanine Arginine Asparagine Stop.@* "HThe larger the target is, the longer it will take for the model to run. This will be especially noticeable when the offspring count is high.To account for redundan0@cy at a given site in the peptide, enter square brackets [ ] and place the abbreviations for the permissible amino acids at that site within the brackets. For example, [L P T W N] translates into [ Leu Pro Thr Trp Asn ]. Any one of these amino acids will be considered a match when the model is run.n/0? L_HShortcuts PanelThis panel lists the 20 amino acids that make up proteins, the stop codon and the corresponding three letter abbreviations and shortcut codes for each amino acid. Note: only these 20 amino acids and stop will be considered valid by the program. The Validate buttonClick on to check the sequence you have entered and to convert the shortcut codes into their three letter abbreviations.The Open buttonThe open button displays an open file dialog that allows you to open any sequence file that you have saved to disk.@9 @HThe Save buttonIf you have opened a sequence file the Save button will save the current sequence to that file, otherwise a 'save as' dialog will be displayed which will allow you to save the sequence to a file on your hard drive.The Save As buttonThe Save As button displays a dialog which allows you to save the sequence you have entered to a file on your hard drive. Just type in a name for the file and click the Save button on the dialog when you are ready.2: BH The OK buttonThis closes the Peptide edit window and inserts the edited peptide into the target box on the main form. The Cancel buttonThis closes the Peptide edit window without making any change to the target box on the main form.I=1w=kSelecting a preset modelCO loH Selecting a preset modelThere are four preset models that you can access from the Models menu on the main form.Click on the Models menu and then the model you wish to select.The options on the main form will be set automatically to suit the selected model.Press the button to start the demonstration.Here are the four preset models. Dawkins (default) Error Catastrophe Adjusted Mutation Rate DNA Model(=k% HDC1"Using custom modelss#k"P nGH Using custom models If you have modified the settings of the options on the main form and would like to reuse them at a latter date you can do this by using the following options on the Models menu.Save SettingsSave Settings AsOpen SettingsThe Save Settings options allow you to save the current settings to a file and the Open Settings option allows you to reload those settings. On opening a settings file, the settings on the main form will automatically be reset to the state they were in when you saved the settings.V%x1xControlling the use of random numbers?"V zH Controlling the use of random numbers The program seeds the random number generator with the same random seed every time the program starts up. This is done so that tests can be repeated.There are two options available on the Random numbers menu on the main form, they are Randomize and Use Random Seed.The Randomize option will seed the random number generator using the system clock. Use the Randomize option if you want to vary the results of the demonstrations.x- (HThe Use Random Seed option allows you to run tests that you can reproduce without having to restart the program. A test will produce the same results for the same option settings every time the same random seed is set. V%'17'Changing t'he options on the Main Form\-/ .ZH Changing the options on the Main Form('% H<+ &"HDNA Model? (% H& HThe DNA model when active, uses a representation of a peptide (protein) as the target instead of characters from the alphabet. & HA peptide is represented by a sequence of three letter abbreviations of the amino acids that build up the peptide. An example is AlaArgAsnAspGlnGluAlaCysAlaSTP where STP represents a Stop codon on the DNA that would code this peptide.& HThe virtual life form is represented by its 'DNA'. Its DNA is a sequence of letters limited to the set of letters A, C, G and T, representing the nucleic acid bases, triplets of which code for the amino acids. The translation and transcription processes that build proteins in real life are emulated by using a lookup table to produce a representation of the peptide that would be produced in a living cell. When a position in the DNA codes for a stop codon the peptide is truncated at that point. & 1HEach peptide made by each 'offspring' is then compared with the target peptide to determine which one is most like the target in amino acid sequence.(% H8( HComplexity (/% H & mHThe complexity value is used in calculating the selective value of an individual. This option has been included to add more realism to the program. In real life the chance that a single point mutation on its own would add a selective advantage is very slight. For a protein to gain functionality, multiple mutations would often be required. You can see the effect of this issue on cumulative selection by changing the Complexity value./ & HIf you type in 2 as the value for Complexity then selection will be calculated in groups of two. An entity with 0 matches is just as likely to be selected as an entity with 1 match. An individual with 3 matches has an advantage over an individual with 1 or 0 matches but is ranked on the same level as one with 2 matches and so it goes in increments of 2. So the selection value increases with matches that are multiples of the complexity value.(  % H  & oHNote that the time taken to converge on a target increases exponentially with complexity. This means that just a little bit of complexity makes a big difference to the time taken.(  % H8 V % &HNote two things:( ~ % HV M 8 >/H;ΉFirstly, do not confuse this complexity with the irreducible complexity that Michael Behe discusses in his book Darwin's Black Box [see ref]. 6~  & !HThe kind of complexity that Behe is referring to is at the level of systems that need multiple proteins to function at all. My computer model deals only with the production of a single protein and so it is much less complex than the systems that Michael Behe discusses.(M  % H & HSecondly, this model still favours evolution because it ignores real functionality. Any combination of matches that are a multiple of the complexity number will be considered to be progressive and therefore will be selected. The program cannot evaluate whether or not the peptide sequence /sequence of letters would be a viable sequence in real life. Evolutionary processes could only ever select for functionality, not a particular peptide sequence.( % HG@+ &8HPredetermined target?@ (:@% H<@vC& -HAs in Dawkins' original program this is not an optional setting. While real evolution can have no predetermined goals for protein design, there is no way of simulating such a process with a computer algorithm. By using this process for selection, issues like progressive and viable intermediates and irreducible complexity are completely swept aside (the complexity option deals with complexity at the level of proteins as opposed to irreducibly complex systems that involve many proteins that cannot function independently of each other). Note: this method strongly favours evolutionary theory since it rewards any change that is headed in a very specific direction and ignores harmful mutations, extinction and other issues that would normally prevent genetic change from occurring.(:@C% HBvCC+ &.HFitness Plateaus? (CD% HU/C]G& _HFitness landscapes are one way of representing the concept of how species (theoretically) progress. A species is thought to be located on a given position on a fitness terrain, the higher the position the fitter the species would be. Natural selection is thought to move a species in an upward direction over the fitness landscape. However, what would happen if a species were located on a plateau and there was a valley between the current location of a species and the next highest position on the terrain? For example, suppose that a species had to sacrifice the use of its forelimbs for a period of time while they supposedly became wings that could sustain flight. There is a problem here in explaining the intermediate downward trend in the fitness landscape to arrive at another position on this terrain.(DG% H:]GH& )HAs a result of the predetermined target combined with the artificial process of selection, this program (as with Dawkins' original) completely ignores the fitness terrain and assumes that any change in the right direction will be progressive and therefore will be selected.(GH% H?H&I+ &(HSingle Parent? (HNI% Ho&IM& HThis is another setting that is not an option, just like Dawkins' original model. This means that there is no genetic recombination occurring in this model. Consequently, any mutation that moves the sequence towards the target sequence will be passed down through successive generations. Except in locations in the DNA where the mutations occur, the virtual life form will pass on 100% of its DNA. Asexual organisms like bacteria operate in this manner. In sexual organisms such as mammals, the figure is only 50% for the father and 50% for the mother, for each offspring. So with such 'higher' organisms there is a high chance that a 'beneficial' mutation would be lost before it can be selected and ramify through the population. Organisms that reproduce sexually also have a disadvantage over asexual reproduction in that the males eat about half the food so that the females are less able to reproduce. Sex is supposed to be beneficial in getting rid of recessive deleterious genes when they come together through recombinationwhether this is adequate to explain the persistence of sex is still debated by evolutionists.(NI N% H@MKN+ &*HOffspring Count ( NsN% HiCKNO& HThis setting determines how many offspring the selected individual of each generation will reproduce. Note that to produce results similar to Dawkins' original model, the count would need to be about 100 if the 'Guarantee Mutation?' setting is on (see below). If it is off, the count would need to be around 200 to 240.(sN% HOEOU+ &4HAverage/Fixed Count? (}% H|U% HFor each time a model is run, the offspring count will be fixed throughout the run and will not vary between generations.(}F% HD+ &2HGuarantee Mutation? (F% H~& MHThis option determines the method used to produce mutations. With this setting switched on, every offspring will receive exactly one mutation in a random location.2, &HIf the setting is off then every position in each offspring's DNA is given a 1 in Mutation Rate chance of a mutation occurring at that specific location. This allows for the possibility that some individuals may have multiple mutations while others may have none. So even when the mutation rate is the same, switching this setting on or off will cause a large difference in the results. qL~% HThe default setting is on for the Weasel model and off for the DNA model.(2˄% HA + &,HMutation Rate 1/ (˄4% HgA & HThe value of the mutation rate determines the frequencies that 'mutations' will occur. When the Guarantee Mutation? setting is on this will be fixed at exactly one each for every 'offspring'. If Guarantee Mutation? is off, then for each and every letter copied there will be a 1/Mutation rate chance of mutation. *4ņ%  H J+ &>HAllow Inserts and Deletes (ņ7% HyN+ &HThis option is not available unless the Guarantee Mutation option is off.7k& +HWhen this setting is off the only kind of mutations emulated are substitutions. A substitution is where one letter is replaced by another letter. (% H0 kÈ% HExample:I$ % HH1: GCGTTACAATCGCGCAGTCTTACACCTCGAU'Èa. ,NH2: GCGTGACAATCGCGCAGTCTTACACCTCGA( % Ha5&  HWhen Allow inserts and deletes is on, two other kinds of mutations can be included in the simulation, namely inserts and deletes.(]% H^95% rHAn insert will insert a letter into a random location:(]% HI$,% HH1: GCGTTACAATCGCGCAGTCTTACACCTCGAN&z( LH2: GCGTTACAATCGCGCAGTCTTACACGCTCGA(,% H_:z% tHA delete will remove a single letter from the sequence.()% HI$r% HH0: ATTTTAATTGAAACTAGCGCTATCAGTTAGt.)F \\H1: TTTTAATTGAAACTAGCGCTATCAGTTAG(r% Hۍ& OHThis option is not present in Dawkins' original program. It has been added to demonstrate the effect that inserts and deletes can have on the coding of a peptide. *%  H a6ۍf+ &lH Insert Rate 1/ Delete Rate 1/ Substitutions 1/ (% HT.f & ]HThese values are used to determine the ratio of the three types of mutations used in a model. The substitution rate is calculated automatically after the insert and delete rates are entered, since the total of all three must add up to one. Do not enter insert or delete values that are less than 1. *6%  H G }+ &8H Max Reproductive Pop. (6% H}]& %HUnder the current model this value is fixed at one. Even if the offspring count is as high as 1000, only one individual survives to reproduce. (% HH ]( @HEliminate all but the best? (% H& AHThis is perfect selection, the selective value of the best individual is one (s=1) and the selective value of the rest is zero. Inferior life forms don't compete for resources in the world that this hypothetical organism lives in. With Dawkins' model there is no chance of harmful or fatal mutations occurring, there is no chance of extinction, progression toward the goal is certain and survival is guaranteed.)% H C'+ &0HAccidental Deaths? (O% H']& HIn real life having a selective advantage does not guarantee survival when unavoidable accidents such as flood and fire occur. This model assumes that the hypothetical life form will never be in the wrong place at the wrong time.(O% H@]+ &*HGeneration Time (% H# Enter a number of minutes, hours, days or years for the generation time to determine the total time in years elapsed for a run of a model. Changing this value will effect the Years: value in the status panel. D'1'Using the DNA model7; DUsing the DNA model For a quick demonstration of the DNA model, click on the Models menu and select the DNA model option and then click the button (or press the F2 Key).Alternately you can use the DNA model by first switching the DNA Model? option on the main form on by clicking on the Yes radio button. Edit the target peptide by clicking on the button that will appear next to the target edit box and enter the amino acid sequence you want.c'9 @)ΉSee Target Peptide Window for information.You can run the demonstration by clicking on the button, or pressing the F2 key.One important difference between the DNA model and the Dawkins' Model or alphabet model is that the 'DNA' of an organism is not compared directly with the target because selection has to work on protein functionality, not DNA base sequence. That is why there are two lines displayed for the each generation reported in the results window. A representation of the peptide is produced using the DNA of each individual and it is the peptide that is compared with the target.&7# F?1?Using the main windowNg OV΀ì i͉ Using the main window The target box, the results display, the options and the status panels are all visible on the main form. For details on using menu options, editing targets, changing option settings and running demonstrations see the How to section. The Results Display The Status PanelsD?818The Results DisplayX6 : The Results Display The Results Display reports every change as the program progresses toward or even away from its goal.Each line contains the generation number and the current 'genome' of the hypothetical organism.Not every generation is displayed, only generations where there has been a change in the 'genome' when comparing the surviving individual with its predecessor.Generation 0 is8X the original entity that every other supposed individual is derived from.8nX ~}The red letters in the display highlight where changes have occurred. For the DNA Model, green amino acids indicate that the DNA that coded this unit has changed but the amino acid coded for is unchanged (because of 'redundancy' in the triplets coding for the amino acids).For Dawkins' original model each generation has one entry only.For example generation 21 and 22 might look like this..21: OETTFNKOPIT CS LQKE AOWEASEPUX }H  22: OETTFNKOPIT CS LQKE AOWEASELThe program stops when an exact match is reached45: METHINKS IT IS LIKE A WEASELFor the DNA Model there are two entries per generation displayed.The first line represents the DNA of the selected individual. The second line displays the peptide that the DNA encodes.71: CGTGCACTTTGGGTCGCACGAACGCGGATCDNA sequence 71: AlaArgGluThrGlnArgAlaCysAlaSTPPeptide coded formn\ H  72: CGTGCACTTCGGGTCGCACGAACGCGGATCDNA sequence 72: AlaArgGluAlaGlnArgAlaCysAlaSTPPeptide coded forThe target is reached when the peptide coded for by an individuals DNA matches the peptide in the target window.There is an option under the main menu to only display the final result. This will speed up running a model.@1I BThe status barsns 9 @HThe status panelsThe status bar contains two clocks and the run status panel. The clocks are green if a demonstration is active or paused and turn red to indicate that the program is finished or has been halted.The main purpose of the clocks is to indicate that the program is still active and to give you an idea of how different numbers will effect the time that the program takes to reach its goal. You may notice that the DNA model takes more 'real time' to process the same number of generations as the English text model. This is not intentional, the program has more processing to do for the DNA model. {  ހH"  " "Clock1 Clock2 Clock 1This clock displays the total amount of time the demonstration has been running.Clock 2 This clock displays the time since the Matches value has changed.Status panelThe status panel, at the bottom right hand side, shows you the current state of a demonstration. The possible states are Active, Paused and Done.s s 0 .H MatchesMatches displays the number of matches achieved and the target number of matches. For the DNA model the target number of matches is counted as the number of amino acids in the peptide.7{  ( HMatches/Gen(s  % HnI @ % HThis is the number of matches achieved in each generation, on average.( h % H1 @  ( HYears(h  % H! & HThis panel gives you an idea of how a number of generations can very quickly add up to thousands or millions of years. To adjust the time taken per generation, edit the Generation Time box with the drop-down choice of minutes, hours, days or years.(  % H5 ?( HMutations( g% Hb? @% HThis panel displays how many mutations the program has generated for the current demonstration.g @(g4@% H7 @k@( HGenerations(4@@% HqLk@A% HThis is the current number of generations that the program has processed.(@,A% H7AcA( HNucleotides(,AA% HcAtB& HThis panel is provided so you can calculate the rate at which mutations have been generated. This allows you to compare the number of mutations generated with the number of mutations expected.W2AB% dHNucleotides / Mutations gives the current rate.(tBB% H@B3C153C(GAcknowledgmentsBBuEB RAcknowledgmentsThis program is based on the Weasel Analogy program written by Richard Dawkins as described in his book The Blind Watchmaker.I would like to thank the following Dr. Don Batten, B.Sc.Agr.(Hons), Ph.D. for advice on writing the program and for assistance in writing the tutorial.Dr. Lee Spetner (the author of Not by Chance) and Walter J ReMine (the author of The Biotic Message) for their work which inspired the writing of this program and a large portion of its functionality. 3C(G) Andrew N. Driazgov for the Delphi library of the Mersenne Twister Pseudo-Random Number Generator. (Uniformly distributed among 0 to 2^32-1 and has a period of 2^19937-1)Makoto Matsumoto and Takuji Nishimura for the C version of the Mersenne Twister on which the Delphi library was based.See: http://www.math.keio.ac.jp/~matumoto/emt.htmlFuEnG1_nGNCopyright and LicenseD (GI9 @Copyright and LicenseThis program is Copyright (C) 2000-2002 Answers In Genesis.Tutorial by Dr. Don Batten, B.Sc.Agr.(Hons), Ph.D. and Les Ey.Programming by Les Ey.The byte code is Copyright (C) 1983 - 1998 to Borland International Pty. Ltd.The Delphi Library of The Mersenne Twister Pseudo-Random Number Generator is Copyright (C) 2000, Andrew N. Driazgov The Mersenne Twister Pseudo-Random Number Generator algorithm is Copyright (C) 1996-1997 Makoto Matsumoto and Takuji Nishimura (alphabetical order).KnGK1 05This software is freeware. You are encouraged to freely distribute this software provided that you accept the terms and conditions of the license.By installing, copying, or otherwise using the software, you agree to be bound by all of the terms and conditions of the license agreement.Upon your acceptance of the terms and conditions of the License Agreement, Answers in Genesis grants you the right to use the software in the manner provided below.All of the files included when distributed must remain intact and unaltered. 'I$N) The byte code may not be reverse-engineered as required by the license agreement with Borland International Pty Ltd. NO WARRANTY.While care has been taken to produce an accurate and meaningful software package and it is hoped that you will find this package informative, no warranty is provided with it.This software package is provided as is and without any express or implied warranties, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.c5KN. ,jThis software is owned by Answers in Genesis.M$NN1NRAppendix: Building a peptideFN&6 :!Appendix: Building a peptide - transcription and translationThis is a basic description of how a peptide is produced. The actual processes involved are far more complex than described here, especially in eukaryotic cells (cells with a nucleuN&Ns), but this is a basic outline.There are two main stages to produce a peptide, transcription and translation. Transcription Transcription is the process whereby a segment of DNA (deoxyribonucleic acid) is used to produce a complementary segment of RNA (ribonucleic acid).[6N% mThe strand of DNA is like a negative of the RNA that is produced. The DNA and RNA are made up of nucleotide bases, which are like chemical 'letters'. The bases pair up in a complementary manner. The four bases in DNA are adenine (A), cytosine (C), guanine (G) and thymine (T). In the double helix of DNA, A pairs with T and C with G so that the two strands are complementary. With the complementary RNA copy, the nucleic acid base uracil (U) replaces T, so that RNA is made up of A, C, G and U, which pair with the T, G, C and A in the DNA, respectively. &/ ,HA suite of enzymes is involved in transcribing a segment of DNA code to RNA. The enzymes recognize where to start the transcription, unwind the DNA strands to allow the copying, 'zip' the RNA nucleotide bases together, and recognize the end of the segment to be copied. The resulting string of RNA is called messenger RNA or mRNA.Below is an example of the mRNA that would be produced from a section of DNA.Portion of DNA:CGA-GCG-CCT-CTG-GTT-CTT-CGC-ACG-CGT-ATC(Stop) &, &HmRNA produced:GCU-CGC-GGA-GAC-CAA-GAA-GCG-UGC-GCA-UAG(Stop) Each letter represents a nucleotide base; the hyphens do not represent anything that actually exists within the DNA, they are used only to highlight distinct codons within the DNA and RNA. Codons are groups of three nucleotide bases. Obviously, for a given sequence of bases, there are three different sets of codons, depending where the reading starts. The starting point is determined by the position of a starter or promoter sequence.7B R HTranslationTranslation produces a protein specified by the information in the base sequence in the mRNA. The triplets (codons) specify which amino acids are to be joined together to make the protein. For example:mRNA segment: GCU-CGC-GGA-GAC-CAA-GAA-GCG-UGC-GCA-UAG (Stop) Protein made:Ala-Arg-Gly-Asp-Gln-Glu-Ala-Cys-AlaYou can access a short cut panel from the shadowed button to the right of the target entry window for the full names of the amino acids. Note that there are 64 different triplets possible, but only 20 amino acids. Most amino acids have more than one triplet code that can code for them. Note the three different triplet codes for Ala (alanine) in the example above. This provides redundancy in the code, such that mutations that change a nucleotide base in the DNA often do not cause a change in the amino acid coded. For example, if a GCU mutated to a GCG or a GCA, it would still code for alanine. However, 'redundancy' is not a good word for this, because there is a pattern to the 'redundancy', and it is involved in 'transcription level control' of rates of protein synthesis. Nevertheless, the duplication of codes for many amino acids makes the protein outcome somewhat resistant to mutational errors in the code.) HA whole suite of transfer-RNA molecules and enzymes line up the amino acids in the order specified by the mRNA triplets and zips them together in the proper manner to form a protein.After folding into its predetermined shape (determined largely by the sequence of amino acids), each resulting protein makes up one of the various structures of the cell, a hormone or such, or it acts alone or with other proteins as an enzyme that performs a variety of complex functions within a cell.HR4 6)HMichael Behe's book Darwin's Black Box is worth reading if you would like to know more about some of the complex functions of life at the molecular level. Any standard biochemistry or even biology textRNbook has a description of how proteins are manufactured in cells.; 1UReferences3Rc  References1. Behe, M. (1996) Darwins Black Box, Touchstone, New York.2. Dawkins, R. (1985) The Blind Watchmaker, W.W. Norton, London.3. Remine, W. (1993) The Biotic Message, St Paul, Minesota.4. Spetner, L. (1997-1998) Not by Chance, The Judaica Press, New York.5. Spetner [4] quotes [Fersht 1981, Drake 1969, 1991]: Fersht, A. R. (1981). DNA replication fidelity, Proceedings of the Royal Society (London) B212:351-379~>d 5 Drake, J. W., (1969). Comparative rates of spontaneous mutation, Nature. 221:1132. Drake, J. W., (1991). Spontaneous mutation, Annual Reviews of Genetics 25:125-146.6. Spetner [4] cites [Grosse et al. 1984]: Grosse F., Krauss, G., Knill-Jones, J. W., and Fersht, A. R. (1984). Replication of fX174 DNA by calf thymus DNA polymerase a: Measurement of error rates at the amber-16 codon, in Advancements in Experimental Medicine and Biology 179, Proteins involved in DNA Replication, pp. 535-540.UE X7. Hoyle, F. (1981). The big bang in astronomy, New Scientist 92(1280):527.The books Darwins Black Box, The Biotic Message and Not by Chance are available online from www.answersingenesis.org.< >1Reference 1kBU) "1. Behe, M. (1996) Darwins Black Box, Touchstone, New York.< 818Reference 2oF) "2. Dawkins, R. (1985) The Blind Watchmaker, W.W. Norton, London.< 81MReference 3jAM) "3. Remine, W. (1993) The Biotic Message, St Paul, Minesota.< 1 Reference 4}NM/ .4. Spetner, L. (1997-1998) Not by Chance, The Judaica Press, New York.< B1!BReference 5pQ p;Ή5. Spetner [see ref 4] quotes [Fersht 1981, Drake 1969, 1991]: Fersht, A. R. (1981). DNA replication fidelity, Proceedings of the Royal Society (London) B212:351-379 Drake, J. W., (1969). Comparative rates of spontaneous mutation, Nature. 221:1132. Drake, J. W., (1991). Spontaneous mutation, Annual Reviews of Genetics 25:125-146.< B?1"?Reference 6iO l;Ή6. Spetner [see ref 4] cites [Grosse et al. 1984]: Grosse F., Krauss, G., Knill-Jones, J. W., and Fersht, A. R. (1984). Replication of fX174 DNA by calf thymus DNA polymerase a: Measurement of error rates at the amber-16 codon, in Advancements in Experimental Medicine and Biology 179, Proteins involved in DNA Replication, pp. 535-540.< ?31#3Reference 7T2 47. Hoyle, F. (1981). The big bang in astronomy, New Scientist 92(1280):527.131$:1Times New RomanMS Sans SerifArialSymbolWingdingsWingdings 2Courier NewArial Narrow  ; ~''''t'E'G'0$' .'8'B'L'V'`'5j'NuNLOO;PчP@Q NJ"y,yyuvu\vvvv v vv/vv vvv$www߈Pxxq0uABCDDEӅFCG@PPdn;PчPPqPчwP;wPqччqP;wчww;ƈP;;qчqPч;;PPqPw;wPq;чPqPPчwPPqw^/&;)F24u^Accidental DeathsAdamAdjusted Mutation Rate, modelAllow Inserts and DeletesAmino acidamino acids,Appendix0Average/Fixed Count4Bases8Behe, MichaelDbiological informationLblood clottingTCodonXcodons`Complexity Option/SettingdConclusionhCumulative SelectionlCustom models/settingspDarwin's Black BoxtDawkinsDawkins, modelDawkins, RicharddeathDeletesDemonstration, playing a sound at the end ofdemonstration, runningDNADNA ModelDNA, Model Eliminate all but the bestEnzymeerror catastropheError Catastrophe, modelerror correction Eternal lifeevolutionevolution, speed of(Fitness landscapes0Fitness, Plateaus4Forgiveness8functionality<Generation Time@GenerationsDgenetic codePGenetic recombinationTgenomeXGoddGood NewshGospellGuarantee MutationpHaldanes DilemmatHow Dawkins program workedxHoyle, Fred|Insert, Delete and Substitution rateInsertsIntermediates, progressive and viableIntroductionirreducible complexityJesusLee SpetnerLifelog file, optionsMain WindowMatchesMax Reproductive PopMichael BeheModels, Adjusted Mutation RateModels, customModels, DawkinsModels, DNA Models, Error Catastrophe$Mouse trap,Mutation0Mutation Rate@MutationsLMutations, harmfulTmutations, information-adding\Natural selection`Not by ChancehnucleotidepOffspring|Offspring countPeptidePeptide, abbreviationPredetermined targetProbabilityproof readingProteinProtein functionalityproteinsRealistic Mutation RatesredundancyReMine, WalterResults Display8RNASalvationSettings, customShortcuts Panel Sin(Single Parent,Spetner, Lee0Status8Status Bars<Substitutions@targetDTarget, as peptideLTarget, as wordsTTarget, editingXtarget, longhTarget, predeterminedlThe Biotic MessagepThe Blind WatchmakertTranscription|TranslationVirtual life formWeasel analogyYearsDawkins, RicharddeathDeletesDemonstration, playing a sound at the end ofdemonstration, runningDNADNA ModelDNA, Model Eliminate all but the bestEnzymeerror catastropheError Catastrophe, modelerror correction Eternal lifeevolutionevolution, speed of(Fitness landscapes0Fitness, Plateaus4Forgiveness8functionality<Generation Time@GenerationsDgenetic codePGenetic recombinationTgenomeXGoddGood NewshGospellGuarantee MutationpHaldanes DilemmatHow Dawkins program workedxHoyle, Fred|Insert, Delete and Substitution rateInsertsIntermediates, progressive and viableIntroductionirreducible complexityJesusLee SpetnerLifelog file, optionsMain WindowMatchesMax Reproductive PopMichael BeheModels, Adjusted Mutation RateModels, customModels, DawkinsModels, DNA Models, Error Catastrophe$Mouse trap,Mutation0Mutation Rate@MutationsLMutations, harmfulTmutations, information-adding\Natural selection`Not by ChancehnucleotidepOffspring|Offspring countPeptidePeptide, abbreviationPredetermined targetProbabilityproof readingProteinProtein functionalityproteinsRealistic Mutation RatesredundancyReMine, WalterResults DisplayRNA/&;)Lz%%GospelJTutorialuIntroductionHow Dawkins program workedError Catastrophe;Realistic Mutation RatesчDNA ModelIrreducible ComplexityConclusionHow toRun a demonstrationPlaying a sound when the demonstration is finishedEnabling the log fileEditing the targetEditing a target PeptideThe target box on the main formTarget Peptide windowSelecting a preset modelUsing custom models߈Controlling the use of random numbersChanging the options on the Main FormqUsing the DNA modelUsing the main windowThe Results DisplayThe status barsnAcknowledgmentsCopyright and LicensePAppendix: Building a peptideReferencesReference 1Reference 2DReference 3Reference 4ӅReference 5CReference 6Reference 7/&;)L4  u;ч/&;)L4; ;i͹̀꾞͆쿞EeGj0 Al͜5͔u͗͝H;ч,͹+Jii$kͬNZ#v)))ε ) )Φ)/)) )Έ)*ί22k9μ@߈GOqOV΁;μ;;D;΅;Ӆ;C;ά;oP%@t&tnjalp2 \>wpp-wwp p  ww p w wx w wxww wp w wx w wxww wp w wx w wxww wpwwwxwxwxww wxww wpwwwxxwxwxwwwwwxwwww{wwpwwwxxwxwxwwwxwxwwww{wwpwwwxxwwxwxwwwxwxwwww{wwpwwwxxwxwxwwwxwxwwww{wwpwwwwxxxwxwwwxwxwwww{wwpwwwxxxwxwwwxwxwwww{wwpwwwxxwxwxwwwxwxwwww{wwpwwwxxwwxwxwwwxwxwwww{wwpwwwxxwxwxwwwxwxwwww{wwpwwwxxwxwxwwwxwxwwww{wwpwwwxxwxwxww wxwwww{wwp w wx w wxww wp w wx w wxww wp w wx w wxww wp w wx w wxww wp w wx w wxww wp  ww p>wplp0:,| WTVVPPPPPPPP   3   0    3      0    3      0    0      0    0      0     -      0     -      0 *  0PPPPPPPPSSTT+"lp0,qnV P P P P     x      u        u    u    x  /  2  5 P P PSnnlp07vvvg a a a a a a a a <          O             L  !        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