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by: Renee Lehner


Renee Lehner
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Benjamin Kerr

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Benjamin Kerr
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This 30 page Class Notes was uploaded by Renee Lehner on Wednesday September 9, 2015. The Class Notes belongs to BIOL 481 at University of Washington taught by Benjamin Kerr in Fall. Since its upload, it has received 22 views. For similar materials see /class/192313/biol-481-university-of-washington in Biology at University of Washington.




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Date Created: 09/09/15
Correlation and Regression 7 Biol48172008 Page 1 Correlation and regression You ask questions of correlation and regression when you have measured two variables in each member of a sample correlation and regression are methods on paired data For example you may predict that the rate of growth in Fusarium will be dependent on the pungency or heat of the chilies the fungus was removed from There are many many questions that you can approach using correlation When two variables are correlated the magnitude of one changes with the magnitude of the other However we can establish no cause and effect relationships In questions of correlation we are simply interested in asking whether the two variables increase or decrease together Regression is a powerful statistical technique that allows you to estimate the mathematical form of the relationship between a dependent response variable y the rate of growth for a fungus and an independent control variable x the capsaicinoid concentration of the media Regression allows you to estimate how accurate your predictions will be It allows you to determine how well can you predict y from knowing x Correlation and regression are closely related but they are not the same Very often you will be confronted with situations in which you are interested in the relationship between 2 variables Then you will have to ask yourself two questions 1 am I interested simply in knowing if the variables are positively or negatively correlated or 2 am I interested in the functional mathematical form of the relationship between these two variables Sometimes you may be interested in answering 2 but your data set may not satisfy the assumptions of regression If that is the case you may have to simplify your question to one of correlation We hope to convince you that if your data set satis es the conditions for regression then you should use it Independently of whether the question that you have is one of correlation of regression always plot your data The characteristics of your data and the form of this plot should tell you a lot about whether to ask questions of correlation or of regression There are several situations in which you have no alternative but to ask a question of correlation A One or both of the variables is ordinal For example you may ask if small medium sized or large trees have 1 no ivy 2 some ivy or 3 lots of ivy B One or both of the variables is discrete or the independent variable is binary In the past 15 years a large number of methods have been developed that allow using regression methods on discrete and binary response variables they are called logistic and Poisson regression methods These methods are relatively new and therefore they are often not described in introductory statistics textbooks However they are tremendously important and their use is becoming widespread among ecologists Because they are a bit more advanced we cannot deal with them here We recommend Ramsey and Schafer 1997 The statistical Sleuth Duxbury Press as an introduction to these regression methods C The relationship between x and y is clearly nonlinear In a subsequent section we will describe a few methods that will allow you to diagnose linearity Sometimes if the relationship is nonlinear you can still fit a function You can use very simple regression methods to fit polynomials ie functions ofthe form y 30 3 1X 3 2X2 nXquot or you can use more complicated methods to fit other nonlinear functions Again these notes will not deal with non linear procedures We recommend Motulsky and Ransanas 1987 FASEB Journal 1 365374 as a friendly and nonmathematical introduction to nonlinear regression Correlation and Regression 7 Biol48172008 Page 2 Lets assume you have satis ed the conditions for regression analysis In regression analysis we are interested in two objectives First we are interested in nding out if there is a relationship between 2 variables or between a dependent variable and several independent variables Second we also would like to nd out given a model to describe the data what are the best possible estimates for the statistics in this model In the case of linear regression our model is a function of the form Y 51X 30 In which 51 is the slope and g is the intercept The meaning of the slope is the change in y when X increases by 1 unit Its units are units of yunits of X If l gt 0 y increases with X If l lt 0 y decreases as a a function of X The meaning of the intercept is the estimated value of y when X 0 1gt0 One of the purposes of linear regression analysis is to estimate the best value for the slope 5 and the intercept g The line that you derive using regression analysis is called the line of best The line of best t is obtained by nding the numbers a and b that minimize the following sum of squares SSE 20 J92 20 1x 5012 Where 3 is the predicted value of y for X Xi Al mxl b In words this means that the line of best t is that for which the sum of the squares of the distance between the points and the line is as small as possible Panel a describes a good t in which the distance between the points and Correlation and Regression 7 Biol48172008 Page 3 the regression line is small In contrast panel b describes a poor t between the points and the regression line These are the same points a b The assumptions of linear regression analysis are 1 Pairs of measurements X y are independent from each other 2 The value of X is measured without error or with a small relative error 3 The y scale must be continuous X can be discrete or continuous 4The test assumes that the variance around the regression line is the same ie that the scatter of points around the regression line is more or less the same for all values of X A very useful statistic in linear regression is the coe icient of determination r2 The coef cient of determination rZ varies from 0 to l and it tells you what fraction of the total variation in the dependent variable y is accounted for by the relationship between y and X The coef cient of determination r2 is a very important number because it tells you how well your linear model ts your data lfrZ 071 for example this means that 71 ofthe variation in y is accounted for by the relationship between X and y Lets illustrate what we have learned about linear regression with an example Suppose you wanted to test the hypothesis that fungal isolates that come from nonpungent plants in populations with fewer pungent plants in them would grow faster in media that did not contain capsaicin Lets suppose you had 12 populations from which to work and you averaged data from different isolates within each population Correlation and Regression 7 Biol48172008 Page 4 Pop Percent growth mm pungent in7 days m 1 1 75 E 90 2 1 82 E 80 3 5 77 C 12 4 7 736 7D o o 5 9 725 E 6 8 75 E 5 39 7 15 70 5 5D 8 20 68 g 9 23 704 5 4n 10 40 65 3 3 11 55 63 Percent pungent 12 78 56 The results of a regression analysis are Parameter estimates estimate t probability Bo 5 781 31 00027 88 lt0001 rz089 You conclude that Fusarium taken from chili populations where nonpungent plants dominate grow more rapidly in media without capsaicin than Fusarium from populations where pungent plants dominate This is a linear relationship and can be described as Growth in 7 days 077027percent of population pungent Note that variation in pungency explains about 90 of the variation in growth rate The slope tells you that growth rate declines by 03 for every 1 increase in the percentage of the home chili population that produces capsaicinoids We might report the basic relationship as follows The growth rate of Fusarium in media that lacked capsaicinoids was negatively related to the dominance of pungent plants in the chilie population from which the Fusarium was taken r2 089 P lt 0001 df 110 Note in reporting regression data at a minimum you need to include the r2 value the P evalue and the degrees of freedom dt 7 we always use one DF for the regression itself and the remaining are listed as residual degrees of freedom Correlation and Regression 7 Biol48172008 Page 5 If you cannot use regression because one of the caveats that I listed on page 1 apply for example you must conduct a correlation rather than a regression analysis The test that I recommend is the Spearman Rank Correlation It is a simple nonparametric test You can use this test for data that are ordinal discrete or continuous The individual data points must be independent The test statistic is indicated by rs And the null hypothesis is that there is no correlation between the two variables H0 rS 0 This test is a bit tedious to do by hand or in excel but quite easy in JMP If you are familiar with JMP Go to Analyzegt Multivariate Methods gt Multivariategt red arrow at Multivariate gt Nonparametric correlationsgt spearmans If you would like to do it by hand the method and a worked example follow 1 Rank the variables for each data point within the two groups Tied absolute values each get the average rank of those two values if they had not been tied If this is not clear see the example that follows 2 Calculate the difference between the ranks di and calculate its square diz N 3 Sum the square of the differences 26112 11 4 apply the following formula 5 Compare the calculated statistic rS with the critical value given in the following table for the appropriate sample size In the following example you are interested in finding out if the percent cover of two native species is correlated in park x Because it is unclear if the relationship is linear or not see figure you decide to conduct a Spearman rank correlation test Mahonia cover Sword fern Plot x cover 0 Rankx Ranky d d2 1 100 75 21 135 18225 2 60 15 21 12 9 81 3 45 15 16 12 4 16 4 35 0 105 35 7 49 5 40 30 12 175 5 5 3025 6 45 10 16 9 7 49 7 0 0 15 3 5 2 4 8 45 20 16 14 2 4 9 30 15 7 5 12 4 5 2025 10 0 0 15 35 2 4 11 45 40 16 19 3 9 12 45 70 16 20 4 16 L L Lquot N u O u V Lquot u N u Correlation and Regression 7 Biol48172008 Page 6 14 50 25 20 155 45 2025 15 45 30 16 175 15 225 16 45 0 16 35 125 15625 17 15 10 4 9 5 25 18 10 0 3 35 05 025 19 20 10 5 9 4 16 20 30 5 75 7 05 025 21 30 0 75 35 4 16 Note how you calculate the ranks In the Mahonia cover column 2 plots had no Mahonia Their 1 2 ranks would have been 1 and 2 and hence their rank is 15 In the visitsh column Vih 1 2 3 4 5 6 6 trees received no visits and hence their rank is T 35 This is exactly equivalent to what you do in a Mann Whitney U test N 2d 726 11 and therefore 6726 053 1213 21 i1 because 053 gt 0435 from the enclosed table you reject H0 and conclude that there is a positive correlation How would you report this result 125 In park x percent cover of Mahonia was I 10 positively correlated with percent cover of g Sword fern rS 053 p lt 005 N 21 Fig 0 G 1 O 75 39 C 5 5 We hope that you have noted that we always 39C 0 report results us1ng the past tense Ed1tors and I5 reviewers of your manuscripts expect you to 3 2 5 O do it as well D 0 O 3 0 o o g o o l l l l I 0 20 40 60 80 Mahonia cover 1 Note that this somewhat tedious procedure can be a lot speedier if you run this in JMP 7 Analyzegt Multivariate Methods gt Multivariategt red arrow at Multivariate gt Nonparametric correlationsgt spearmans See the stats examples and assignments for details Correlation and Regression 7 Biol48172008 Table of critical values or different sample sizes at e 05 alpha level to be used th11 the Sp Bank Correlation test earman39s n sample size l Adnpked from JJa39 Zn Biustmxucal Analysts PrenticeHall Englewood Cliffs N 1 1974 n 21231 n 212 n 53 n 531 5 100 27 0382 49 0282 92 0205 6 0886 28 0375 50 0279 94 0203 7 0786 29 0368 52 0274 96 0201 8 0738 30 0362 54 0268 98 0199 9 0700 31 0356 56 0264 100 0197 10 0648 32 0350 58 0259 11 0618 33 0345 60 0255 12 0587 34 0340 62 0250 13 0560 35 0835 64 0246 14 0538 36 0330 66 0243 15 0521 37 0325 68 0239 16 0503 38 0321 70 0235 17 0485 39 0317 72 0232 18 0472 40 0313 74 0229 19 0460 41 0309 76 0226 20 0447 42 0305 78 0221 21 0435 43 0301 80 0220 22 0425 44 0298 82 0217 23 0415 45 0294 84 0215 24 0406 46 0291 86 0212 25 0398 47 0288 88 0210 26 0390 48 0285 90 0207 Page 7 Correlation and Regression 7 Biol48172008 Page 8 Clicker Question Which ofthe following Is NOT true about the Rainey and Travisano ex eriment Tradeoffs and the Origin of Diversity A r Vii radiation in an inilially homogeneous bacterial population B occupied distinct niches within the vial wrinkly spreaders on top smooth morph in the middle fuzzy spreader on bottom c r 39 ui e ily V I morphologies such as the heavy lifterquot and the extroverled weaverquot strains could increase when rare E3 Lecture 5 EXOrCISIng Darwmlan demons From an eyolutlonary perspectlye an Tradeoffs and the Origin of Diver5ity extremelyfltorganlsm would 7 Llyeayerylungtlme e Pruduce many orrspnng frequently 7 Repruuuce asexually Lecture Outline From an ecologlcal perspectlye an extremely successrul organlsm would 7 o etelts nelgnoers Introduction to tradeoffs Tradeoffs and diversity 7 REslst predaturs and stress Why don twe see such supere Tradeoffs and the origin of diversity Specles The cllche a lack of all trades l5 a Tradeoffs and maintaining diversity master of none nlnts at art ofthe reason organlsms oeal Wltn pnyslcal ff constraints and tradeo s Summary 9 Tradeoffs and 1the Oi igin of Dwers y What are trade ff5 M d W h I Esue lallst ra e occurs w en improveme in l 1 Lean e ouu39ne performance eg tness under one set E m gm 5 of circumstances comes with a concomitant loss in performance in g E Aspmhst Introduction to quotidem another set of circumstances g E Q o Th 39 b39lii l h in b l of II Perform oe quotquot Tratleotfs and diversity 03 irquot eyveoW 3V5 me ie39hzm of a enwonmentA tradeoff r Tratleoffs and the origin of diversity Ranking Ranking The existence of a tradeoff leads to SW A m e 5 Tradeo s and maintaining diversity ranking eVersa39S arid may 0 Ce be t specialists and generalists or different functional types lrltermedate Suimiaiy What are the causes of tradeoffs Th d th tml Fundamental limitations In resource faring 2 rise to more compennve seooiinos i39MLlHEl39LZHdEU 2mm use eg its time energy etc Overlap between the functional Both x banainphage and maltose domains ui 39 39 sorroce DfE ooii oeiis Szmati39iian e Hufriuni 1975 lnflexibility ofthe phenotype across 39 erent contexts where th eddaeoresswe behavimal e m appropriateness ofthe phenotype varies syndmme in te ale 0 min Joiircnn t Sin zoos Flexibility ofthe phenotype across dtfere contexts where adaptive plasticity in one trait comes with non adaptive plasticity in another trait increases in oii niammtimotn in iesponseto nvooxia in p moscies afta riu feeding cionriian st ai 23ml Atoms and spandrels In a famous9pa er Gould and Lewontin 1 7 cautioned against unbridled ap lication ofthe adapt tionis Programme to every mponent 0 an organism One ofthe lines ofcriticism was that an organism cannot be atomized into a collection of independent parts rather dtferent parts interact non independence and single pa s have multiple roles pleiotropy Tradeoffs are a natural consequence ofthe interconnectedness of parts and the multifarious functions of parts Tradeoffs and the Origin of Diversity Lecture Outline Introduction to tradeo s Tradeu s and diversity r Tradeoffs and the origin of liversin 39 Tl39adeoffs and maintaining diversity Surrmary t o ssi ii Take 5 minutes to talk to your group about the following 1 Are tradeotts importantto tne origin and maintenance of biodiversity 2 if 50vvhat specific types oftradeoffscould be important 3 t Coexistence between multiple species 39 39 Is possible when th s competitive aoiii Tradeoffs and biodiversity ere are tradeoff in etticiencv using ditterent resources 5P Tilmari item snowed tnattwo cie between gmwth wheii phnsplutz isi demonstrating a tradeon growtn tinder iiminng pnospnate and iimiting siiicate cooid coexist under certain concertrations or ootn nutnerts gmwlh when smut is hmmng 7 Between toiererice to stress and competitive abiiitv Cunnell tact roundtnattwo spec es or oarnacies demonstrating a tradeotr oetween stresstoierance and competitive aoiiitv were aoie to coexist in tne rocxv intertidai tnlznlwz in osmium Between resistance to predators and Enhannan 3 Lensxi i997 snowedtnat two strains or E coii T4 resistant and m sensitive cooid coexist due to a resistancecompetitive tradeotr campmm MW oral and spatial heterogeneity Witn spatiai or temporai neterogeneitv in tne environment tnere can be a piace or time for types on eacn end or a tradeott curve to snine Witn persistaritspatiai heterogeneity each species strain variant etc nas a particular address wnere it does wellr coexistence occurs at a regionai ievei performance in environment 5 Yuri Witn persistanttemporai neterogeneitv as iong as tnere isa met o for persisting through bad times seed oanx restin 39 etc tnen eacn species wiii in ti e and tnere wiii be coexistence over proionged periods be Biodiversity Other distinctions C quot 9P aquotyvquotisimpmmmepara e he r I Tradeoffs and the Origin of Diversity emergence of diversity from the maintenance of 39 sity ultimately mutation is the generator of diverSlty r Physlcal separation and tradeorrs between vaflous pneno pic characteristicspaire Wl spatialtemporal heterogehelty can be important in Lecture Outline Introduction to tradeon s ate an e maintenance ofthis diversit new variants ersist y p Tradeuffs and diverslty is also important to separate external from COSXIStequot 0 type Tradeol39fs and the origin of diversity External causes of diversity include elements outside the biotic community of interestthat contribute to persis ence 7 internal causes of diversity attribute coexistence to properties of the biotic communi itselr external and internal causes or dlverslty can operate slmultaheously Tradeo s and maintaining diversity Summary A system for a Rainey day Paul Rainey and Michael Travisano 1998 used the bacterium Pseudomonas uorescens A few pieces of information on the microbe e Colonizes plant surraces and soil particles 7 Produces a green rluorescent pigment r it is motile by means or multiple rlagella These aulhors placed a single ancestral strain of this bacterium into two different types of environments Unshaken microcosm in which gradients of 02 could develop Shaken microcosm in which such gradients were destroyed By following the pattern of diversmcation between V32 02 treatments the authors studied the e ec structure and gradients on the origin of variants Shake Shake tratment tratment A microradiation The effect of structure When a single genotype is placed into an unshaken microcosm several different colony morphs evolve amon em 7 smoot morpn SM llke the ancestor e wrinle sprea 5 e fuzzy spreader F Each morph is associated with a particular 1 physical location within the tube H NIogNeZn lognN 7 SM throughout the broth 7 S at the alrebroth interface 7 F8 at the bottom orthetube Th same morphs reliably appear across experimental replicates and the sequence of appearance was also maintained This radiation occurred over the course of 7 days and diversity continued to remain high for as much as a month later What processes fuel this radiation ogtAnnm time abundance abundance towelrel Take 5 rrl39nutes to talk to your group one exceptlon F8 lnyadlrlg a populatlon of 7 about the following eyery rnorpn could lncrease wnen rare Name sorne potentlal lnternal and external factors ln tnls systern N E g eyldence do you naye for lnternal yersus external causes of dlyersl ernergence and rnalntenance ln tnls systern7 on How would you experlrnentally test for these nypotneucal causes Competitioncolonization tradeoff Tradeoffs and the Origin of Diversity 8 ecies may d erwilhtheir ability to disperse produce spring colonize new areas etc Lecture Outline A superior colonization strategy to establish in less favorable ll esquot Introduction to ti39aden s areas as fug39 iv Tilman 1994 suggests that n Tradeo s 3quotquot divergity there is a tradeoff betwee competillon for nitrogen with r r Tradeoffs and the origin of diversity C eek Tradeoffs and maintaining diversity yam u emu was Surrmary A taxing exercise Experimental system to explore cc tradeoff Wefleed avoluflteefffom the class a v dVlU P id H can be costly ln Escnencnla cull Strain aKaz Strain aKas untladdlated 5 59 llll lulu l Resuufces lnyested ln oulldlng tne flagellurn NEEDLEBi 7 Energy expended oyturnlngtne flagella E 5 When grown ln wellrnlxed enylronrnents cnernostatslflasxsetc ECUl utants ry systern tne rest oftne class can detect nowfartne lndlyldual ls hat delete partofmeyrn gena O Tero are gt r arm W vqu feget selected Thls yolunteer rncyes wltn yery srnall steps ln yery stralgnt llnes Hlshef senso away from tne goodles tne class wlll say you re gettlng w colder H r flagella T V lonlze ernpty areas and utlllze unexplolted resources ll ll lull l dlt l around a fewtrmes and cofltlflue ploddlflg anead Next Tuesday we are usan an 39 experlrnental set up not far from Ralney amp Traylsano s to snow a tradeoff between w a aldmulrzalm a A spatlally structured enyncrnrnert an agar w plate wltn super soft agar sp ll structured naoltat lzllqzu naununusun unstructured enylrdnrnerft a gt a snaxen tuoe An analogy lmaginetwo people out in a lake each in theirown canoes There are seeral anchored booeys tn the lake each with an I pple on top ach of the people would like to get to each of these apples but expend as little energy oorng so One otthe people has a paddle pink canoer and the other does not orange canoer On a shit day the pinkcanoer cleans up gettrng many apples to cornpensate for the extra efforto pa mg 0n atremendously windy rainy day the orange canoer gets aboutthesarne number of apples and saves energy Tradeoffs and the Origin of Diversity Lecture Outline Introduction to tradeon s Tradeoffs and divelsity Tradeoffs and the origin of diversity Tradeoffs and maintaining diversity Summary Summary No organism has been able to attain Darwinian demonquot status as organisms face inherent tradeoffs A tradeoffis de ned as compromised function under one set of circumstances that comes with improved function in another Tradeoffs may play important roles in the origin and maintenance of biodiversity especially in spatially and temporally heterogeneous environments Rainey and Travisano demonstrated that spatial heterogeneity could be crucial to the emergence ofa diverse system de novo The adaptive radiation found by these authors was partly supported by tradeoffs between bio lm formation and competition in a heterogeneous world Even without spatial heterogeneity tradeoffs between competition and colonization are predicted to contribute to biodiversity maintenance Deta Analysis Lecture Outline 0 Proceseing ete ueing Excel Visualizing Data using Excel Analyzing ate using Excel iffererice in meene tuteet ifference in dietributiene 36 test Worksheet Eile Edit mew insert Format Tools Data indow Help v 10 v H I U GMT gzv DEMO Graphing in Excel 233 l 100 v I39ll II Garamond US516 5E3quot ll GOALS A1 v r Getcomfortable using the A B i C D E F G H Chart Wizard 2 i i 5 i 7 El 9 Ngt bN l Picking the right graphical representation for your data Labeling your axes and adding a title Graph both yelrx and yesx on the same plot you can check your work with your calculator if it graphs Label your x axis x and your y axis y and title your graph Exponential Growth What happens when you change the value of r from 01 to 01 Picture Words x 1000 Grade Distribution We are visual animals and often can see patterns when data is presented visually Examples Piechart illustrates the distribution of values of a single variable X Y plot illustrates the form of the relationship between two variables Understanding the Black Box Paired histograms illustrate the relationship between the distributions of two variables Accuracy of Prediction The most appropriate 0 5 10 15 20 I NutnberofTiials picture Will often depend on the data Colony Distribution from the Luria Delbruck Experiment Categorical or 06 quantitative g 04 I Observed Frequencies counts Er 02 Expected or measurements LL 0 Relationship between 1 3 5 7 9 11 13 15 17 19 21 data points Colonies Analysis Lecture Qutlne 9 Processing ueing Excel 0 Visualizing ueing Excel Analyzing Data using Excel Difference in means ttest Difference in distributions 96 test Student s ttest Gossett published a paper using the pseudonym Student that dealt with distinguishing the differences between means of small data sets The ttest uses the statistics from two groups of data means and 3d to generate a third statistic the t statistic If the two groups of data come from populations with the same mean the t statistic has a characteristic distribution itself note the shape will depend on the sample sizes William Sealy Gossett If the computed t is extreme then the chance that there are equal means from the two groups is slim this is quantified by the pvalue from the test The means are significantly different if plt005 Assumptions 2T KC Each datum is independent t Data is normally distributed 3 nT quotc DEMO Performing a ttest Computing a pvalue from a ttest Distinguish the different types of ttests gt Paired versus Unpaired data gt Equal versus Unequal variance gt Onetailed versus Twotailed tests D JTv4439i21l39l12345 X Worksheet Click on the Tradeoff Data tab From the colony counts write functions that will give the cell counts Fb Nb Fe Ne Then write functions giving wFN After finding average fitnesses use the function AVERAGE graph the average fitnesses from the TUBE and DISH environments Label your graph Perform an unpaired and paired ttest on your data Which test should you use for this data What can you conclude TUESDAY I WEDNESDAY mo 110 ITHURSDAY I TUBEI I 10 110 a 11 mo A a a 106 100pL I a 104 105 50pL 10 The Evolution of Cooperation E3 Lecture 6 U Clicker Question In the Rainey amp Rainey study which ofthe following would be consistent with the idea that smooth m orph39 cells eats39 while wrinkly spreader39 cells functioned as cooperators39 Wrinkly spreader39 mats infiltrated by smoothmorph39 cells wer e weaker that is they collapsed under a lower weight of glass beads than mats with solely wrinkly spreader39 cells Smoothmorph39 cells reap bene ts in terms of numbers in the presence of wrinkly spreader39 cells w nkly gpmd Wrinkly spreader cells suffer costs in terms of numbers in the presence of smooth morph cells Wrinkly spreader39 mats in ltrated by smooth morph39 cells took longer to collapse than mats consisting of solely wrinkly spreader39 cells The Tragedy of the Commons In Wealth oflVations Adam Smith 1776 suggested that a collection of rational agents each acting in their own selfinterest would work forthe common good William Lloyd 1883 discussed how selfinterested parties might overexploit a common resource aper Garrett Hardin 1968 extended s gesting that many shared resources Selfishness and Cooperation a r The traditional evolutionary m Hume h perspective is hat sel shness trumps cooperativity However biological systems are characterized by substantial cooperat39vity Animals work together in social groups participate in interspeci c mutualisms and sacrmce future smooth operation oftheir group Biological Altruism Altrulsm l5 the central theoretlcal problem of Socloblology E o Wllson 1975 An altruist improves the tness of a recipient at a tness cost to itself Altruist Altruism has fueled many debates i n evolutionary biology eg group selection versus individual selection an orthodox evolutionary perspective a altruistic behavior is confusing 3 a Altruism is the very opposite of survival ofthe ttestquot Sober ampWilson 1998 sttme motd ceus Cooperation and the Major Transitions Evolutionary biologists are interested in the subject ofthe major transitionsquot s rGeheS to chromosomes pmkarymt Prokaryotes to Eukaiyotes 3mm Single cells to muitieceiiuiar Solitary inoiyiduais to societies What are the common themes for these transitions 7 Lowerievei units cooperate to ensure the functioning of the nigner ieyei unit 7 There is often division of labor between the lower level units 7 There is a sense of common fate the lower ieyei units go down togetherWith me nignerieyei snip The Evolution of Cooperation Lecture Outline Introduction to cooperation theory Examples of cooperation Cooperators in a sticky situation Slime mold with greenheards Summary The Evolution of Cooperation Lecture Outline Introduction to cooperainn theory Examples of cooperation r Cooperators in a sticky situation v Slime mold with greenbeards Summary The Prisoner s Dilemma 39A Mini Tragedy of the COmmons n c The pny 39mau i Putyourseif in Boos Shoes What snouio you do if you think Biii is going to cooperate then you snouio defect Bob does C if you think Biii is going to defect then you snouio defect Of course Bill is thihkihgthe sarne so you wiii end up With utuai defection Whereas rnutuai cooperation would have been rnucn better Bob does D Evolution of Strategies The payoff matrix payoff to the PLAYER Conditions for the PD R P gt S quotThe Prisoners Diismms gsme is an siegsnt smoouiment Hamilton s Rule An allrmsl rrnproyes the ntness of a recrprent at a ntness cost to itself 6 Between any two individuals a coef cient of relatedness r can be cornputed Thrs coerncrent rs basrcauy the sarne allele as the donor by descent 4 14 14 14 Q r12 donor recipient is shared between slbs 50 is shared between slbs 50 The ultnnate cntenon winch detenmnes behaviour is to a bene t othe behaver but A 1 HAMILTON S RULE gt 7 Evolution of Cooperation by Kin Selection I39d gladly give my Ire for three of my brothers ve of my nephews nine of my cousinsquot A srmpe asexua exampe The A type asexually sphts rnto W0 ottsp 9 Where one helps the other at a cost to 1 self I B s Haldane The B type asexually sphts rnto two ottspnng There is a base nurnber or second generatron orrspnng The condtron that allows A to rncrease m treouency s 17gt b 1 or 7gt7 Withri r An Example The Brain Worm Dtcvomtzltum dand rtcum The Evolution of Coopera tion Lecture Outline Introduction to cooperation theory Exampl of cooperation Conperators in a sticky situation 4 Slime mold with greenbeards gt Summary Microbial Cooperation Mrcrobes drsptay yanous torrns or cooperatryrty e Reproductwe sacnnce e g y as shrne rnutds and sacral bactena rurrn rrumng budlEs stalk cehs drsptay repreductrye sacnnce to huld u p spores 39 Producuon of publlc goods 2 g yeast and bactena eltude extracellularprutemsthat break down umplex sugars degrade armbmtms and gathercntrcat resources 7 competmve restramt e g r phage rnay drsptay restrarnt rn rts use or a curnrnun host bactena Turner and Chao 1998 evolved ayrrat strarn under wen mixed condrtrons Where the yrrus outnumbered ts bacterial hostethey round that rnean ntness eyentuauy decreasedl hese authors suspected that the eolyed virus was a defector ill a Pnsoner s Driernrna By mmng the ancestral Ahcahd eolyed Eyol phage strarns together at differehtfrequehcle r they estrrnated the payotr matrix and connrrned the PD PaulTumr Lin ha The Evolution of Cooperation Lecture Outline Introduction to cooperation theory Examples of cooperation Cooperators in a sticky situation Slime mold with greenbeards Summary A Model Organism for Diversity amp Cooperation Pseudomonas uorescens is a soil dwelling microbe often associated with plants When a single genotype is placed into an unshaken microcosm several d erent colony morphs evolve is an adaptive radiation see Rainey amp Travisano 1998 The same morphs repeatedly appear across replicates where morphology is based on the type of colony formed smooth wrinkly or fuzzy e39 Shaking the microcosm destroys the diversity But letting the microcosm sit unshaken restores 39 the diversity 1 q i One strain ofthis organism also produces a type of public good important for a group trail m at formation Specialists in the Adaptive Radiation sraae m o m an unshaken ask Fuzzy Spreader FS Conditions to Demonstrate In order to demonstrate that the Wrinkly Spreader is a cooperator and the A r 39 A ulelulluwillg 1 When together theernkly Spreader Smooth Morph SM WSl5 a costly morph relatrye to the 2 The WS group rs susceptrole to lnvaSlOn oy detectors 3 the SM types p hegatrye errect oh the WS type Being a Wrinkly Spreader is Costly Fitness Assay wWSSM 08 A WS Population is Susceptible to Defection the WS type quotVi 7 By Day 5 colonies that resemble the SM appear a de novoquot SM type The de novo SM type does not signricantly differ in tness in pairwise competition with original SIM type I The de novo SM type does not form mats or any type of aggregative structure 4212 mth defectorsquot SM Facilitation and Debilitatio n The authors compared the performance of each strain alone and in combination with the other The presence ofWS increases the density of SM mat hitchhiking defectors The presence of SM decreases the densIty ofWS as the mat collapses early a realization ofthe tragedy of t e commons Defecting SM types appear to weaken the integrity ofthe mat h They cnecked the lntegnty Wlth a glass bead techhlque a matvvlthout SM detectors holds about 5 tlrnes as much welght as a mlxed mat Cooperation in a Microbial World They claim the de novo generation ofthe Wrinkly Spreader morp is an evolutionary transition from individual cells to a bio lm group Production ofthe polymer is costly and thus susceptible to defection from withinquot however it may evotve over and over if stick toquot Le kin selection argument This is an example of cooperative behavior eg as laid out bythe Prisoner39s Dil mma an may be an ideal system to test theories about the evolution and maintenance of cooperation The Evolution of Cooperation Lecture Outline I Introduction to cooperation theory Examples of cooperation r Cooperators in a sticky situation Slime mold with greenbeards Summary Green beard Theory Conslder a haplold populatlon Wltn two alleles at a glyen locus c and N lhdlvlduals Wlth genotype 3 naye a green eard Whlle lndlylduals Wltn genotype N s does the follovvlrlg l The allele produces a perceptlble tralt a green beard 2 The allele alluws rorrecognltlon or tne tralt ln otners a The allele alluws rorprererentlal Take 5 minutes to talk to your neighbor about the following 1 Does such an allele sound plauslble to on 2 lfso howtrequehtly would you ekpectnnd sucn an allele 3 Would greenbeards be lmmuhe or susceptlble to cheaters A u you ekpenrnentally demonstrate lt7 Cooperation in Slime Mold chtyostellum dlscoldeum ls a protlst that rorages n the soll as slhgle cells l Thelte cycle can be broken dovvrl lntot ree sta es nggregatlon when cells starve ey corne togetner tgrauon The cullectluh or cells oye as a slu l a oulmlnanon Arrulun body ls rorrned ln WhlEh nonreproduclng cells rorrn a stalkto nold up reproductlye spores a mum luv N om w rquot M an l W Stalkcells are altrulsts sacnnclng ruture reproductlon to nelp dlsperse the spores ln thelr collectlorl Why don t cheats cells that prererentlally get lhto the spore head lhvade the system Discovery of Greenbeard Ouellelquot Strassmah amp Colleagues studled the effect of dlttereht alleles lh the CsA locus Tnls gene codes tor a cell adheslorl ractor lrnportant ln nornopnlllc blrldlrlg Dave Queu Er H E knockout Cells Wlth thls gene knocked outlacktnls adheslorl proteln Qt o 0 O K mo Zamp agoegzuurl 51 ln roraglng rnlktures Wlth equal proportlons or b b l u storm Wlth 82 Wlldtype due to the nornopnlllc blhdlhg csA is a greenbeard l The allele produces a perceptlbletralt an adneslon proteln 2 The allele alloys ror recognltlon or tnetralt ln otners tnrougn nornopnlllc blndlng a The allele alloys ror prer rertlal treatrnert based on pnenotype onlycells tnaterter tne slug naye a cnanceto becorne spores The Evolution of Cooperation Lecture Outline Introduction to cooperation theory Examples of cooperation v Cooperators in a sticky situation Slime mold with greenbeards Sumary Summary I The standard Darwinian picture is that sel sh variants should always displace their cooperative competitors individuals that provide a bene t b at a personal cost c This expectation is belied by many instances of cooperation and altruism in biological systems Such cooperation can be favored ifinteractors are related Hamilton s rule formalizes this relationship cooperation evolves if bcgt1lr More generally cooperation is favored if cooperators have ways to A k fromg cooperators Cases of cooperation have been discovered in organisms ranging from bacteria to turkeys to primates Experiments have been designed to explore the susceptibility of cooperators to cheats mat formation in Pseudomonas and the exclusion of cheats by cooperators greenbeards in Dictyostelium The Problem of Drug Resistance E3 Lecture 11 03gt Clicker Questions R4E3 nan rND tell you E Z r r 39 e 39 L E 33 KEV E Dstiam 0001551 3 SliamTE ia lt 7 57 ism r525 V6250 roast rm sm39r resin c t c mutatiun cunrerrrng resistance tn nrarnprcrn Resistance to ntarnbrcrn is generally costly in the absence or the drug The fitness errect or a mutation conrernng resistance to nrarnbrcrn can depend on the genetic background or the strain under investigation nlyafew or further evolull n most would revert to sensitivity in the absence or nrarnbrcrn Early Antibiotic Research Joseph Lister 13271912 in 1871 he noted that samples oturinewith mold drd not perrnrt bacterial growth He also broneered the introduction bran antiseptic phenol berore and during surgery drastically reducing the rate or rnrectron 7 41 t ampf t inseph Lista39 Ernest Duchesne 18741912 e in 1897 he demonstrated that E CUlWaS killed when cultured With F ehlclllumg r e also showedthatimection Ofthls mold into animals rntected with typhoid bacilli prevented the advent of the disease n c i Ernest Ducheme Alexander Flemnl39ng 18811955 eln1928 hoticed a mold contaminant on a bacterial plate that had been sitting out iii the lab 7 He isolated the F ethlHum hulalum and Gramepositive pathogens neurhonia gonorrhea meningitis diphteha Earlier Antibiotic Research have produced antibiotics for a number of As anticompetitor compounds eg bacteriocins As predatory compounds eg lysing enzymes As quorumsensing molecules ed nisin of chemical warfare The Problem of Drug Resistance Lecture Outline Antibiotics amp resistance Costs Reversion amp Conpensation Antibiotics amp Adaptive Landscapes Predicting Resistance SunInary The Problem of Drug Resistance Lecture Outline Antibiotics 8t resistance Costs Reversion amp Compensation Antibiotics amp Adaptive Landscapes Predicting Resistance Summary Resistance in the Intensive Care Unit HaUDHal musdcdrnlal lnlectlon Stlwelllance system Pepurl 2mm Klebslella neumomae Pseudomonas aeruinosa valnn llllu new lurlbllllzn Inx lr quotquot quot t 7 1 Enterococcus sp Staphylococcus aureus Resistance to Resistance is Futile Resistance Matters Causes of death USA Cause Deathsyear HIVAIDS 18000 Influenza 37000 Breast cancer 40000 ln nurnan dlSeaSe drugrreslstantbacterla can lead to e lncreased nsllt dr rndrtallty e lncreased lengtn dr ndspltal stay 7 Fuel rdrtne spread et all tnese problems to dtner patlents Thus drugresistance causes large nancial and health costs The Problem of Drug Resistance Lecture Outline Antibiotics amp resistance Costs Reversion amp Compensation Antil ties amp Adaptive Landscapes s Predicting Resistance Summary Tuberculosis Resistance in Tuberculosis Gagneux Dayls Long and colleagues explored the 5 ntness cost of drug reslstance ln TB ln yrtro From a fully grown culture of a cllnlcal lSOlate tney exposed tne bactena to tne antlolotlc ntarnplcln Reslstant colonles were lsolated and genotyped generally slngle base cnanges ln tne rpoB gene Tnese autnors wanted to gauge tne costs lt any of antlolotlc reslstance Tne antlblotlc reslstant straln and antlblothrSenSltlve ancestorwere place at roughly equal starttrlg frequency ln a llquld brotn and cornpetedtora growtn vvltn knowledge about tne startlng densltles and nnal densltles ottne reslstant straln R and the ancestor A a relatlyentness rneasure can be cornputed W A 1quot my 1quot Am of Resistance cases 7 streptennycin resistance in E Doll 7 Fusldlc acie resistance in s aweus e Fusldlc acie resistance in s typmnunum e Cuiicin rESl ance in E Doll 7 leamplcln resistance in E Coll Reversion and Tne initiai resistant inutant was costiy Tney dlSCOVered second site mutations in rpSL Eyeing the Landscape Let s extend the iandscape metaphor further rlmaglne tnat intne apsence urtne amblmlcy tne pupulatiun mustiy resiees an a nsiti ye wiiee pe peak rTheri an antipiutic is appiiee rThe sensitiye peak em 5 But rAriy resistant mutants are iinineeiateiy selected neiw tne pupulatiun mustiy resiees an a resistant peak 7 w tn nsitiy ak a e quotSm elfthere are inany ways tei umpensate tnen Wm tne lun t i tei cantakeseyerai pussipie patns rlt is pussipietnat reyeisiein uncurs it is pussipie cennpensatiein uncurs spine relevant question elsthe ianescape actua rAre umpensatury peaks nigner urluwer 5mm tnan reversiun peaks WWW eHuw In any ways are tnere tei resistant7 Huw many ways tei s iiy rugged pecurne umpensate7 cumpensatee iesistants The Problem of Drug Resistance Lecture Outline Anti ratios amp resistance casts Reversion amp Compensation Anti otics amp Adaptive Landscapes A Predicting Resistance S mmary Evolution Done Wright Wright s Shifting Balance Theory Two hasio assumptions 1 Some genetic epistasis leading to distinct peaks in tne landscape 2 A metapopulation of serniisoiated sparsely populated subpopuiations a Migration is low between subpopuiations but present b Genetic dntt occurs witnin dernes Phase 1 Subpopulations drift over the adaptive landscape Phase 2 Selection drives subpopulations to new peaks Phase 3 Competition between subpopulations where the most t pulls the metapopulation onto ils adaptive peak in e r Wright 1931 as quoted in Provine 1986 Phase1 Resistance in Ehe Balance Phasez Selectiun enyes eeines tn new peaks Extending the Metaphor I The Role of Population Structure Antibiotics as a Test Case tt woutd cieariy be desrrabte to conduct setectron expertmema tn subdwtded and ma n nuith n N treatment errects can be drscerned Coyne et at 1997 1 Obtain Severai rtfamptctn reststantstratns in E 00 2 Ptace each strarn tn an environmentwtm structure iocai drspersat and no structure gtobat drspersat wumut rrtamprcrn 3 Trackthe average fitness in each treatment Slow and Steady Wins the Race Not a Creature Was Stirring 39 39 Another group of researchers aHoWed antibiottcrreststant bacterta to evotve wer many Theorettcal Predtcttons m rrr rrr mt foHthng avmge tness II a 2391 0 m E G 55492 55495 5 3 ak 5 5 E Z E 5 5 I 6 g lMxxAi g 2 Mmd Take 5 mll39lL eS to talk about the following i a E U E U Pm e seme atterrratrve hypumeses te exptarrr the U m we was m 59 was errrererrees between me rates at reverstun rrr Wm versus vu HEM weutu yuu eltperrm entaHy urstrrrgursn yuur nesesv 22V Predicting Evolution m of Drug Resistance Miriam Bariow and Barry Haii are deyeioping Errur a technique to predict the iiilteiy paths of mm antibiotic resistance 0 w my They start with a gene that cu rrentiy does not Lecture oumne conrer high resistance e g TEAMB Ma Ma Miriam cephaiosporins MW A Antibiotics amp resistance Next they produce many mutantyersions or m 55 the gene through error prone PCP Costs Reversion 8 Compensation Then they introduce these mutant genes into bacterial CeiiS Antibiotics amp Adaptive Landscapes Next they grow up WE DODU atiOH 0t mutant in a gradient of antibiotic arid Select CeiiS from the highest drug concentration Predicting Resistance They isoiate the resistance gene and begin the process anew Arterarew rounds orthis gunman cycie t ey sometimes have a gene that conrers high levels or resistance The BarlowHall Method How well does this in yitro method predict natural The P mblem Of Drug Res IStanCe a tibiOtiC resistant mutations Using this method on TEM aiieies the rour most 39 common amino acid substitutions round in yitro Eimw RioziS 2383 and E240ilt were aiso the tour most LECTUFG OUtllne comm n ino acid substitutionsfound in naturaiiy occurring extendedespectrumTEti aiieies How does knowledge orthe likely eyoiutionary paths Antibiotics amp resistance heipus ations likely to As we design new drugs knowing the mut n tradeorrs Costs Reversion amp Compensation generate resistance may heip us discer between resistance to dirrerent drugs That is we may be abie to map those regions or genome 39 Am39bm39cs 8 Adapt39ve Landscapes space a HUGE s a ce that engender resistance to drug A these regions do not oye ap then we may be abie to Predicting Resistance r i trap our bacterium by simuitaneous drug use Summary ceturoxime Summary Antibiotic resistance is a serious public health problem Multidrug resistance is particularly worrying Understanding the ecological and evolutionary consequences of n s resistance eg lit ess costs probability of reversion versu compensation can actually inform epidemiological thinking eg in the L Hdilpwrsn igi case of TB i ntihintir 39 39 eal 39 J r39 critical issues within evolutionary biology issues quot vuiigiu irwag he and Fisher including the shape of adaptive landscapes the role of scariest population structure and the constraints on evolutionary trajectories costume i couidthirikot One exciting area 39om both practical and academic perspectives is L drug resi Stance and using this information to design more effective treatment The Nature of Mutation E3 Lecture 3 Clicker Quiz Question What is a jackpotquot in the context ofthe LuriaDelbriick experiment and what mutation model does it support A A r Jackpots support the directedmutation model B A a Jackpots support the spontaneous or random mutation model C Ajackpot is an abnormally large population of wildtype cells Jackpots support the spontaneous or random mutation model D Ajackpot is a population of cells taken from the toilet ofthe famous mi r hi I ni i Jack39s pom th Natural Selection Ricnard LeWoritiri i970 iaid outtnree conditions for tne operation of naturai seiection i There isvanatiun between individuais 2 There is neritaoiiitv across generatiuns 3 There is dinerertiai suivivairepruductiun Tnus conceived naturai seiection desirovsvanation ov Weeding outurifit individuais eise seiection grinds to a nait Natural Selection and Mutation In The Origin ofSpecies Darwin noted the following Some Writers nave even imagined tnat naturai seiection induces variaoiiitv Whereas it irnpiies oniv tne preservation of Such variations as arise arid are beneficial to the being under its conditions of life Darwin conceptually separated the process of variation generation from natural selection gt In the Arendt amp Reznick study on selection in guppies in order for selection to favor 39 erent heritable rowth patterns under deerent resource conditions there must a have been mutational changes that affected growth rate a int mducefastgr r It39ll sizenf sh p swam in quotany maximum The sievequot ufnatuml selectmn slow growing mam arises The gameteth ufmutatmn The Nature of Mutation Lecture Outline Introduction to genetic mutation The LuriaDelhriick experiment A nutation controversy Selection for mutation rate Sumnary The Natu re of Mutation Lecture Outline Introduction to genaic mutation The LuriaDelbriick experiment A A mutation controversy Selection for mutation rate Summary ctassttytng rnutatton The centrai dogrna suggests tnat tntorrnatton 39 PM 21mm d m ttows trorn nuctetc acid sequences DNAto Wmumuus cu mm a same aa RNA to protein but not in the reverse direction 39 NDN39SVNDNDWDUS Dquot se codestot new aa kamgm stated 7 NunsensEcudestut51up r tnsertton or bases or tonger stretches quotArmougn me details or tne ctasstttcatton proposed 7 Detetton or bases or tonger stretches nere are ptaustbte our Knowtedge or rnotecutar btotogy eyen tn one cell tet atone tor att tne organtsrns tn nature ts still tartoo incomplete to attow us to assert dogmattcatn tnat tt ts correct ctassttytng rnutatton by ettect on tunctton Lussrcrrrunctiun rnutatton due to missense nonsense urrrameshirt rnutattons e Gainrctr unctiun rnutatton e Lethai rnutatton e Reversetranscnptiun RNA to DNA 7 Priuns protetn rnedtated nange or protetns Otner ctasstttcattons e g coding ys regutatory Mutations are cnanges to tne genetic rnatertai to An example atquot z mutztlnn Exposure to radtatton 3 punntnls Expasureta notagens Saw y 5 Q ttrnutattons rernatn unrepatred tnen tne change can be tnnertted Fitness Effects of Mutation Most rnutattons are ettner r The Natu re of Mutation neutrai or deieterious This can be snown by I I performing a rnutatton quot accumuiattott expertrnertt Lecture Outline random ottsprtng tndtytouat ts pickedtu estabttsn tne nat generatton control treatment Severai tndtytduats orttnuatty W estabttsntne next generatton As an anatogy thinker tw yerstonsottne garne teiephone Introduction to genetic mutation The LuriaDelbn39ick experiment om A mutation controversy mum Selection for mutation rate tness Summary Inmafm aparn39er version geneau m Two Hypotheses agtne a stngte ceii piaced tn a test tube wttn rowth media This ceii doubies andthen its ottsprtng doubte and so on ts ca be represented as an tnyetted tree 3 Zooiogists and botanists are often abie to see mutants arise that do not bring fitness benefits to their bearers Earty rntcrobtotogtsts were tess tortunate rnutants orthe rntcroorgantsrns tney studted couid orten be reyeaied onty under setecttye condtttons tn Which tne rnutatton attowed rytyat Ata certain potnt tn tne poputatton growth tne setecttye pressure tan antibiotic ts apptted represented by tne red dasne ttne Extreme yerstons or our nypotneses Consider anttbtottc resistance sometntng we re tnterested tn 39 Dire ed quotMamquot Mutatiurts Uncut Univ after from a rnedtcat pgtnt or ytew appttcatton ottne setecttye pressure 7 Let s s a growtng utture or basterta tn atesttube 39 Rm quot quotMamquot WWW DEW a E spontaneoustyat any doubttng eyent 7 Van tnen add an anttbtott to tnts tube Vuur utture quickiy tears due to me deatn or a targe nurnber or etts r Huwever atterturtner incubatiun tne utture recuvers tne growtn or drugreststantcetts Th pr btern ts tnat reststant ceiis and sensitive ceiis dont took dttterent we can onty tett tnern apart by trnparttng tne setecttye pressure e g arter a red ttne due to But wnat was tne trtgger ottnese drugrresistantrnutantm 7 Did the presence cf the antibiuti stimuiate the resistance mutattuna e Dtd tne rnutattons occurrandornty e g eyen berure tne w s The stakes are bigr are rnutatton and seiectton iinked or independent This ouestton was addressed tn i943 by 39 Saiyador Luna and Max Detbructlt Salvaduriuna Max Delbde The LuriaDelbruck Experiment now mutant numbers were drstnbuted across repnoates quot x 1 m L ro or o f A mu m Wtoi r11 Tne Directed Mutation nypotnesrspredrcts a tarrty exen drstnbutron or mutants across repHcates repncates wrtn a rew repncates naxrng unusuaHy nrgn numbers or mutants tnese are cattedracxpots A Signal to Distinguish Hypotheses Atter growrng up sexerat rephcate poputatrons and tnen ptacrng eacn poputatron unu r mutants and tne Vanance m tne number of mutants 1 m mtWaa M average We 7 1VV5W1W111 WW1 gm m e number of mutants tn merage we can drstrngursn tnese hypotheses ans ratro rs expected to be one rortne drrected mutatron nypotnesrs tnan n r r An Exercise We39re gorng to stmuiate tne LunaeDetbrucx expenmentr ciass You er recewe a prece of paper wrtn two rnxerted trees on rtand a dre tn tne Random Mutatron Srmutatron start at tne top or tne tree and make yourway down atways dorng tne foHong 7 Record tne roH ofyour dre rn tne box 7 trtne roH was attnen nu n all boxes and crrctes to tne bottom ngnt ortne box 7 trtne roH was nota tnen donotn r Asyou move down thetree sxrp aHnHed rn boxes L0 tn tne Drrected Mutatron Srmutatron srmpty roH your dre strmes and record tne numbers rn tne 8 boxes tr tne roH was a mu rn tne crrcte to tne bottom ngnt or tne reieant box Crrctes represent bactenat ceus boxes represent repncatron events and fitted orrctes represent mutants TheIr Data me n u 4 u a v as i E 2 a 2 J s a mum u a u mm rm mu 14 ms a a u 5quot inaHofmn i An Independent Check in 1952 uosnua and Estner Lederberg prwtded 39 rndependent support tor tne Random Mutation nypotnesrs They grew up bacterra on an agarenued Petn drsn unm rt formed a con uent iawn Tnen they rephca piated tnrs popuiatton onto sexerat new agar ptates wrtn setectrxe medra e g tne presence or an antrbrotrc TherNatu re of Mutation Lecture Outline Introduction to gen ic mutation The Ll Delhriick experiment A muttion controvasy Setactinn for n39utation rate Summary DITECtEd MUtathquot Strlkes BaCk Data Consistent With Directed Mutation uonn Catrnst uuite Oyerbaugn and otners suggested tnat tor a r some executions or tne LurtaeDeibruck protocol constdertng otner mutations tne yartance was too low to be accounted for solely by spontaneous rnutatton An colt stratn was studted by Spencer Benson tnat lacked tne abtitty to take up large maltodextrins Dexr indeed tney argued tnattne dtstrtbutton or mutants tn repitcated popuiattons looked like a combination or tne dtstrtbuttons predtcted rrom tne spontaneous mutatton and trected rnutatton processes Two potnt mutattons can restore tnts abtitty tn E colt ettner tn tne Ompc locus or OmpF locus Benson 1988 round Dew mutants yta OmpF are generated spmceomsm at a mucn ntgner frequency tnan Dew mutants yta 0ran Funnert tney ciatmed to proytde tndependent eytdence tnat a dtrected rnutatton played at ieastsome role 7 ney saw E CDl unable to usetne sugariactose Lace mutate tnto Wm types oftu Lac bactena wttn dtrrerert cotony snapes e The nrst type rast cotonyrormer nad a ntgn yartance ror tts mean across repttcates 5 Ea wuss mates Take 5 minutes to talk to your group about the following 7 The autnors ctatmed tne second mutart wastne result or dtrected rnutatton mutattonto Lac tn tne presence or lactuse quotRNA mRNA 1 r Benson suggested tnattnts btas mtgntbe due to dtrected mutatton tnat causes tne OmpF cnange under approprtate seiecttye conditions but nottne Ompc cnange Tne mechanism Perhaps tne cell could produce a ntgniy yartabie set or mRNA molecules and tnen reyersetranscrtbe tne one tnat made tne best protetn mm mm 2 llnot wnatotner nypotneses would you Suggest 3 Howmtgnt you experimentally test yourhypotheaes cunnilinan he Return of Random Mutation t The LuriarDElbruck random mutatton nypotnest makes an tmpottan s ton em t n 5 Some Lessons from the Mutation Controversy utan w eneyer generated dupitcates attne same 39 rate as tts ancestor Experimentally a i Benson et al wail checked tne growtn rates or OmpF and om Dex pc your nypotneses mutants andround OmpF mutartsto naye a ntgner growtn rate Running appropriate controls ts tmportant onstderatton ts tnat Biologically tt ttattt tttt t tryirigout bierr pt p tuttu t tnetr growtn rates ntness under noneselective conditions Under seiecttye ondmonsv 7 interestingly tne coiontes productng an eyen dtstnbutton or mutants tn tne catms expertmert D0 Stregsm Comm 59 79 mmat o rate of Orgamgmg 7 5 mg 5 adapwe were slow rowlrl coiontes response or stmpiy a byproduct or poor organtsmai condttton Variation in Mutation Rate Mutatton rate yartes between and wttntn spectes instance rnutatton rate can be senstttye to enytronmental condtttons 7 Some organtsms expenence a ntgner rnutatton rate under Lecture Outline stressrutstaryatton condtttons e cnemtcat mutagens can tncrease tne rate or mutatton e Genettc cnanges can atrect mutatton rate The Natu l39e of Mutation Surveys or natural popuiattons or bacterta suggest tnat more tnan 1 or tsoiates are mutatorstratns Tnese stratns are orten dertctent tn some DNA repatrcomponent r Introduction to genetic mutation The LurkDeller experiment etyen nerttabie yartatton tn mutatton rate tt ts reasonab e to constder tne posstbtitty tnat mutatton rate could eyoiye tnrougn natural seiectton 5 col ademnz glycasyuse r A lrultatioil controversy Take 5 minutes to talk to your group about the following Selection for mutation rate 1 Underwnat ctrcumstances would you expect a ntgner rnutatton rate to be adaptive 2 Wnat are tne costs to betng a mutator Sulmlaly 3 Howrntgnt you experimentally test yourhypotheses Experimental Test for Adaptive Mutation Ahah de Visser Richard tehsw and colleagues 1999 explored the success or mutator strarhs under novel setectrye condrtrons Controhrhg tor gehetrc background they constructed three strarhs a WildenDe a mutY rhutator and a mutS utator They grew these three strarhs up separately under condrtrons to whrch the strarhs were not adapted They found that both mutator strarhs evolved a hrgher hthess arter 1000 geheratrons than the Wildetype were rhcreased OVhy rhrghtthrs be Thrs experiment gryes sorhe eyrdehce that there may be Shorteterm adyahtages to hrgher mutatron under novel setectrye condrtrons Potential traderoffs include longterm deleterious effects ofa highermutatlonal load The Results of Your Simulation The Nature of Mutation Lecture Outline Introduction to genetic mutation T39ne LunaDelbruck expa39inlenl r A mutation controversy Selection for Imitation rate Surrmary Summary The NeeDarwinian perspective separates the process of mutation 39om the process of selection that is mutations occur without respect to their selective consequences 39 quot quotL neutral or quot quot39 39 39 but rarely mutants are selectively favored 39 Luria and Delbruck designed a classic experiment using the variance in mutants between replicate populations to distinguish between the ra dom mutation hypothesis neeDarwinian perspective and the directed mutation hypothesis Cairnsian perspec ive Luria and Delbruck amp others found support for spontaneous mutation e evidence presented by some scientists in support of directed mutation illustrates the importance of proper controls and checking the assumptions of your hypotheses However the directed mutation controversy has led to greater exploration ofthe potentially adaptive changes in mutation rate Black BoxThe Revenge


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