IBRIColloquium, 22 Jan 1991

Dr.Robert C. Newman

BiblicalTheological Seminary

 

                                   COMPUTERSIMULATIONS OF EVOLUTION

 

Introduction

 

            Nota literature search

            Notcovering origin of life question

                        tho2 programs on diskette are self-reproducing automata

                                    REPRO- Langton's automaton in my JASA Spr 88 paper

                                    BYL- Byl's in his JASA Spr 89 paper

                        boththese can be "mutated" by manipulating data for             structure& transition rules

            Notdealing with competition & spread of varieties

                        gooddeal has been done on ecology/population genetics

            Rather,a description & investigation of three programs that relate to the mechanism of evolution:

                        --two described by Richard Dawkins in his Blind Watchmaker (1986), 46-75

                        --one devised by self

                        these3 programs also on diskette available from IBRI for $5

 

Program BIOMORPH

 

            Describe: 

                        program,slightly simplified from Dawkins, for building "organisms" fromgenetic information, selecting among mutants

                        geneis sequence of eight small integers

                        generates"tree" controlling branch length, angles,

                                    #of levels of branching, with mirror symmetry

                        givenoriginal gene, program constructs all "one-step" mutations, displayson screen

                        operatorselects which mutant to succeed parent

 

            Lessonsfrom BIOMORPH:

                        showshow:

                                    mutationoperates on DNA

                                    selectionoperates on developed form, not on DNA

                        seethat:

                                    identicalforms can conceal diff genetics

                                    leavingroom for neutral mutation

 

Program SHAKES

 

            Describe: 

                        Dawkinsseeking to circumvent "monkeys typing Shakespeare" problem ofenormous times involved

                        choosetarget sentence/phrase

                        startwith gibberish of same length

                        mutategibberish, selecting mutant/parent which is closer to target to be new parent

                        gibberishconverges to target much faster than if monkeys were typing randomly

                        Dawkinsgets convergence in typically 40-70 generations

 

            Dawkins'version:

                        Notdescribed in detail, so can't tell how he generated mutants, how many mutationsper generation

            Myversion:

                        Onemutant each generation, compared w/ parent

                        Betterof mutant/parent survives

                        Iget much slower convergence, taking over 1000 generations

 

            Lessonsfrom SHAKES:

                        showsthat a "rachet mechanism" does work

                                    importantreason why many convinced evolution must

                                                becorrect

                        butthis is "guided evolution,"

                                    whichis considerably more efficient than even artificial selection,

                                    tosay nothing of natural selection!

                        doesnot solve time question

                                    whichversion is more realistic?

                                                mutationrate in eukaryotes is 10-8 per             replication

                                    bothignore time involved for mutant to take over

                                                population!

                        myversion suggests a problem

                                    formutating into complex or optimal structures:

                                    lastpieces of puzzle are highly constrained

 

Program MUNSEL

 

            Describe:

                        simulatemutation & natural selection by analogy with human language

                        letterstring is both gene and organism

                        mutationis random change in content and/or length

                        selectionis "naturalized" by requiring that each

                                    groupingin string be an English word

                        currentversion has operator do selecting,

                                    butcomparing with a spell-checker would be more objective

                        generateswords of 1-4 letters rather easily

                        relativefrequency of space character (and nature of selection) tends to keep wordsshort

                        littlesuccess in getting intelligibility in 100s of steps

 

            Lessonsfrom MUNSEL:

                        complexorganisms involve hierarchies of structure

                                    somewhatlike intelligible writing

                                    letters> words > phrases/sentences > paragraphs

                        mutationonly works at lowest level

                                    nucleotides<=> letters

                                    sobecomes tougher to get anything acceptable as we move up hierarchy

                        non-selectedmutation => gibberish

                        neutralmutations spread only by random walk

                        functionalisolation seen here (as in terrain analogy)

                                    manycombinations cannot be reached by single mutations from acceptable smallergroups

                                    whatis the relative size of islands of intelligibility vs oceans of gibberish

                                                foreach level of hierarchy?

                                    canyou really get there from here?

                                                complexorgans/organisms

                                                crossinghigher levels of bio classification