Generative Entrenchment and Evolution

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Generative Entrenchment and Evolution

Jeffrey C. Schank and William C. Wimsatt1

The University of Chicago

1. Introduction

Interest n and detailed studies of development go back to Aristotle, and antedatedserious work on evolution. In the 19th century, heories of and information aboutdevelopment played a significant role in evolutionary hought. This wave reached a crestwith Ernst Haeckel's ate 19th century attempted usion of evolutionary and developmentalideas through he study of comparative mbryology via his 'Biogenetic law' that 'Ontogenyrecapitulates phylogeny.' With the rise of experimental methods for studying development,and the separation f the study of heredity rom that of development eading to the birth ofgenetics as a separate discipline, Haeckel's views fell out of favor by the first decade of the20th century as evolutionary hought took on new directions. (See, e.g., Allen 1979, Gould1977, and the essays by Hamburger nd Churchill n Mayr and Provine (1980)). As far asdevelopment s concerned, his was an unfortunate vent--one which Gould (1977) hascharacterized s 'throwing he baby [development] out with [Haeckel's] bathwater.' The'New Synthesis' which produced he neo-Darwinian heory of evolution was first andforemost a synthesis of evolutionary heory with genetics, through he intermediary f themathematical heory of population genetics. For the most part, studies of development wereleft behind to pursue their own productive paths in different directions.

Recent studies in evolution have shown a new interest n developmental processes, andattempted o integrate nsights from the study of development nto the study of evolution.(See, e.g., Gould 1977, Bonner 1982, Raff and Kaufmann 1983, and Arthur 1984 for avariety of quite different attempts). Since 1973, one of us (Wimsatt) has been working on a

model derived for the explanation of developmental onstraints Wimsatt 1986a, Glassmanand Wimsatt 1984), one which has significant mplications or the character f evolutionarychange. A similar model has been proposed and developed by Arthur 1982, 1984), whohas given what is far and away the most complete and extended defense and elaboration fthe basic view of the role of development n evolution which we share. This view is still notshared by most population geneticists, but seems more in sympathy with the views ofdevelopmental biologists and paleontologists. This divergence of opinion should be ofmore than passing interest o philosophers, historians, and especially to sociologists ofscience.)

PSA 1986, Volume 2, pp. 33-60Copyright ? 1987 by the Philosophy of Science Association

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In spite of the similarities n our views, our foci have been somewhat different. Thoughhe discusses many other things, Arthur has concentrated n 'mega-evolution'--theevolution of major body plans corresponding o the appearance f higher axa; on relations

of this view to classical population genetics, the mechanisms of micro-evolution, nd to therecent variety of theories of macro-evolution; n relations between ecological anddevelopmental onstraints n evolution; and on discussions of development or evidentialsupport. Wimsatt has discussed the implications of this model for the early plasticity anddevelopment of the nervous system (Glassman and Wimsatt 1984),argued or areconceptualization f the 'innate-acquired' istinction, he character f 'geneticinformation,' and the nature of the genotype-phenotype elation (Wimsatt 1986), andelaborated an analogous account of the nature of scientific change (Wimsatt 1987).

Another difference of emphasis s that Arthur has focused on a positive account of thepossibility and role of very rare macro- (or mega-) mutations n evolutionary hange,whereas Wimsatt has concentrated n the implications of the increasing arity of adaptivemutations of

increasingly argereffects. Rasmussen

1987)has in effect combined both

concerns n his important tudy. He has applied and further xtended Wimsatt's basicmodel in a detailed analysis of experimental ata on a large number of developmentalmutants n Drosophila, producing a scheme which has substantial onceptual anddescriptive advantages and significant explanatory nd predictive mport. With this, he hasproduced a 'flow chart' for the major eatures of the Drosophila developmental program,together with an analysis of the nature of the major mutations eading to the evolution of thediptera two winged insects) from primitive segmented worms.

In this paper, we wish to do two things. First we will outline the basic 'developmentallock' model and the correlative notion of 'generative ntrenchment,' nd develop a numberof their mplications or evolutionary hange. Some of these are new to this paper, andothers are developed n new detail. Many of these implications also emerge from Arthur'sdiscussion (1984), and virtually all of them have substantial vidential support. Second wewill draw on the work of Kauffman 1971, 1985) and work in progress by Schank andWimsatt o present models for the evolution of gene control networks which, for the firsttime, incorporate ssumptions rom Wimsatt's model in order o assess their mplications orthe evolution of developmental programs n models which are more closely related o thoseof traditional population genetics. Our results, together with those of Kauffman, uggest that'generative ntrenchment' nd the developmental onstraints which result rom it are broadlycharacteristic of developmental rograms n general, and may be essential to the evolution ofany significant degree of adaptive complexity. Furthermore, nalysis of the reason or thedifference between Kauffman's esults and our own suggest the existence of a heretoforeundiscovered volutionary onstraint on the evolution of complex adaptations.

2. The developmental ock modelIn his classic paper, 'The Architecture f Complexity' (1962, 1981), Herbert Simon

uses an analogy llustrating he advantage f decomposing a complex problem ntosub-problems ach of which can be solved independently. Consider wo cylindricalcombination ocks (e.g., bicycle locks), each having a row of 10 wheels, each of which has10 positions. There are thus 1010possible combinations, only 1 of which is correct. Inthe first or 'complex' lock, with no clues for partial olutions (see figure la), the expectednumber of trials before getting the correct one is one half of the total number of combi-nations, or 5x109. In the second, or 'simple' lock (figure b), we hear a 'click' when eachwheel is put into its right position, so that the expected number of trials s 5 per wheel, or50 for the lock. The advantage of being able to get the total combination as an aggregate of

independentolutions for the various wheels is the ratio of the

expectednumber of trials for

the two locks, or 108. This is a significant mprovement ndeed



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behavior, n which a system is decomposed nto parts, which are analyzed n isolation andthen strung ogether o explain the behavior of the system as a whole. Following Simon'slead, I have described (1980b, pp. 230-235) nine reductionistic problem-solving heuristics,

most of which can be seen as techniques or redescribing r reanalyzing a system to facilitatethis kind of problem-solving approach.

A model with quite different mplications an be derived by combining eatures ofSimon's 'simple' and 'complex' locks. This I call the 'developmental' ock, for reasonswhich will soon be apparent. It is a simple lock if worked from left to right, but a complexlock if worked from right to left. (See figure lc.) Suppose that n this third ock, thecorrect position of a wheel is indicated by a 'click' (as in the second lock), but what positionis correct or a given wheel is determined by the actual positions, whether correct or not, ofany wheels to the left of it. Thus, resetting a wheel randomly esets the combinations or anywheels to the right of it.

If this lock is worked from left toright,

the solution for the first wheel is asimpleproblem (with an expected number of 5 trials before solution) since there are no wheels to

the left of it. The solution of the next wheel is now also a simple problem: although tssolution s a function of the position of the first wheel, that position has already been set. Ifeach wheel is solved before one proceeds to the next right-most wheel, the solution to thislock is a simple decomposeable problem, with an expected number of 50 trials for the wholelock. Once a wheel is solved (working rom left to right) we never need to return o it inorder o get the 'compound' solution. Its solution s a serial multi-stage process like that ofmany (idealized) mathematical r logical deductions. In problems whose structure s likethis lock, there s a necessary order n which the sub-problems must be solved (because thesolution to later sub-problems equires data from the solution of earlier sub-problems), but ifthis order s followed, this complex problem s decomposeable nto independently olvablesub-problems, whose solutions together comprise the solution to the original problem.

If the developmental ock is worked from the other end, however, it is similar to thecomplex lock. One finds the 'correct' solution for the rightmost wheel in an average of 5trials, but the chance that this represents a correct solution for the whole lock is but 1 in109, the number of possible positions of the preceding 9 wheels. If one now turns to thesecond wheel from the right, and finds its 'correct' position (as determined by the actualpositions of the preceding 8 wheels), there are 9 chances out of 10 that this 'solution' willscramble he solution to the last wheel, and 1 chance in 108 that t is correct or thepreceeding 8 wheels. With the third wheel from the right, the chance of scrambling one ormore of the 'downstream' combinations by moving it is 99 out of 100, and so on. One hasmade essentially no progress This is true for any wheel. Only by working from left toright s cumulative progress possible. Thus, the appropriate mode of solution for this lock is

'developmental,' hough other reasons for this name will emerge in the next section.One more modification must be made to this lock if one is to apply t as a model for

developmental constraints on evolution. An n-wheel lock as described has but 1 solutionout of 10" possible combinations. This is a problem. Suppose that there were but onepossible adaptive phenotype--then here would be no adaptive radiations and no diversity oforganic forms Serial evolution would be possible by adding additional wheels to simplerlocks, but the evolutionary ree would have but a single branch. We must modify the lock ina way that preserves ts structure, ut allows for a branching multiplicity of adaptivesolutions. A simple way to do this is to assume that each wheel has 100 positions, 10 ofwhich are solutions (i.e. are adaptive) or that wheel. When the position of a wheel ischanged, the 10 adaptive solutions of each wheel downstream of it are randomly reassignedto the

digits0 thru 99. In this

case,the n-wheel lock has

(100)nor 102n

possiblesolutions, 10n of which are adaptive. We now have a large number of possible solutions,but the other properties of the lock are unchanged. (The lock is still simple if worked romleft to right and complex if worked from right to left. It is still true for the 10 wheel lock



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(D3)features which are expressed earlier in development have a higher probability of beingrequiredforfeatures which appear ater, and

(D4) will on the average have a larger number of 'downstream' eatures dependent uponthem.

I will hereafter peak of features as being 'generatively ntrenched' n proportion o thenumber of 'downstream' eatures which depend upon them.

I will now show that a case which is both of historical nterest and of current cientificrelevance can be analyzed, explained, and productively eformulated sing the simpledevelopmental ock model and the concept of generative ntrenchment. This is the case ofVon Baer's Law(s).

In 1828, the German embryologist, Karl Ernst von Baer formulated 4 laws of

development.(See

Ospovat,1976).

Theywere

ignoredfor many years (save for De Beer,

1930), but have more recently been resuscitated o a position of respectability ndimportance by Stephen Gould (1977). These laws are often summarized under he generalmaxim: 'Differentiation roceeds rom the general to the particular.' Von Baer's Law(s)can be given three distinct ormulations epending upon the interpretation f 'generality':

Taxonomic generality: Features which appear arlier n development end to applyto broader axonomic categories and to be more widely taxonomically distributedthan those which appear ater.

Morphological generality: Features which appear arlier n development aremorphologically more general more ike an archetypal Bauplan which is filled inby the more specialized and differentiated eatures which appear ater.

Functional generality: Features which appear arlier n development refunctionally more general (more 'general purpose,' more widely applicable, moreplastic, more polyfunctional) han those more 'finely tuned' and specialized thingswhich appear ater.

Of these three nterpretations, on Baer probably ntended he first two. The thirdinterpretation ollows from the second if one believes that unction follows structure, ut inany case is of particular nterest or behavioral ontogeny, the ontogeny of conceptualstructures, nd for the phylogeny of scientific theories (Wimsatt 1987). All three versionsare, under appropriate onditions, true for systems which are appropriately modelled by thedevelopmental ock, but I will not discuss them further here. (See Rasmussen 1986, and

Wimsatt 1987). The truth of von Baer's aw under he first interpretation ollows naturallyand directly from the developmental ock model: features which are slower to change willtend to be be found in more of the phylogenetic descendants of a given ancestral ype, andwill thus be more widely taxonomically distributed. But the developmental ock model (D2aabove) entails that earlier developmental eatures will be slower to change. Thus,

(D5) differentiation roceeds from the (taxonomically) general to the (taxonomically)particular.

Von Baer's aw, on this reading comes out as a probabilistic egularity, ather han auniversal aw. This is in accord with modern nterpretations f it, such as that of Gould,(1977). The greater onservatism of features at earlier developmental tages also implies

that,(D6) on the average, features which are expressed earlier n development are older.



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A further onsequence which follows from the developmental ock (but not from vonBaer's Law) is that

(D7) major developmental abnormalities re likely to result f deeply entrenched eatures aredisturbed or fail to appear. This follows directly from (D4) above, because deeplyentrenched eatures have, by definition, more dependent raits which are causallydownstream of them than ess deeply entrenched eatures. A change in a trait which hasmore dependent raits yields a greater xpectation of major change, and a greater hance thatsomething will go seriously wrong. This consequence reflects the neo-Darwinian prejudiceagainst evolution through macro-mutations.

(D8) Given that earlier eatures have a higher probability f being significantly generativelyentrenched, mutations which are expressed earlier n development are more likely to havelarger, more pervasive, and more deleterious effects.

Of course, a featuremay

occurvery early

indevelopment,

and have little ornothingwhich depends upon it, just as many mutations are 'silent,' due to synonymy in the genetic

code, or to other higher evel functional equivalencies, redundancies, homeostaticregulations, canalizations, and the like. The developmental ock model does not apply tosuch features. If we reconceptualize on Baer's Law in terms of generative entrenchmentrather han earliness n development, hen it is true (as a probabilistic eneralization), nd thetaxonomic data which serves to support he original formulation upports he newformulation ven more strongly, since mutations which are expressed early in developmentand are not generatively entrenched, r which are generatively ntrenched but occur late indevelopment would tend to undercut von Baer's aw, but not the version reformulated nterms of generative entrenchment. Alternatively put, the version formulated n terms ofgenerative entrenchment s more general han von Baer's aw(s) but has it (them) as aconsequence, given that the probability nd expected degree of generative entrenchment sgreater or events which occur earlier n development.

4. Further Consequences of the Developmental Lock model for Evolution

Evolution can proceed by adding urther developmental tages to an existing phenotype.If we allow the number of stages to vary, we get two other predictions rom the model. Themodel predicts irst of all that,

(D9) Ceteris paribus, simpler developmental programs hould be capable of fasterevolution, at least in their most fundamental eatures, han more complex ones. (After all,the earliest stages of more complex programs hould be more strongly generativelyentrenched han those of the simpler programs.) Secondly,

(D10) if we follow along a single track of an evolutionary ineage of entities of increasingphenotypic complexity thru ime, individual eatures which persist for a significant imeshould tend to become ever more resistant o change as more and more other features cometo depend upon them.

This principle plays a central role in a mechanism to be discussed n Wimsatt andSchank 1987) allowing the evolution of a multiplicity of increasingly complex adaptationsover longer evolutionary ime scales.

Werner Callebaut personal conversation) has pointed out that he developmental ockleads to an explanation of Dollo's law, that

(Dll) Evolution is irreversible. The phenomenon described n D10 suggests such anexplanation. When features irst occur and are incorporated hru selection, they presumably(from D2b) have little if any generative entrenchment. The longer that they persist, the



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greater s the chance that they will become increasingly generatively ntrenched D10). If thefeature hen becomes selectively disadvantageous, t becomes increasingly ikely that tspresence will be in some way modified or compensated or, since its straightforwardelimination would now be more

costly,due to its

greater generative entrenchment,han ts

modification. The resultant hange is thus not a reversion o the original state. Thisexplains the occurrence of 'frozen accidents' and 'vestigial traits' n evolution.

Perhaps most important or the analysis of developmental programs, he developmentallock model suggests that

(D12) comparative ross-phylogenetic analyses of development can generate significantinformation about he structure f developmental programs. On this account, a high degreeof taxonomic stability of features arlier n development mplies a high degree of generativeentrenchment. Actually this conclusion, the converse of that provided by the develop-mental ock, requires ndependent argument nd some subsidiary assumptions which I willnot discuss here.) Given either the acceptability f these additional assumptions, or arelatively ow frequency of early-acting genes which are not generatively ntrenched, we canin principle determine eatures of the causal structure f developmental programs by doingcross-phylogenetic omparisons, analyzing he relative stability of taxonomic eatures, andlooking for covariances n changes of features as clues to which later eatures may dependupon which earlier ones. This strategy s used to some extent by Rasmussen, 1987, thoughhis focus there s on the analysis of developmental mutants n Drosophila, rather han on theextensive use of cross-phylogenetic omparisons.

It should be noted that we have presented only the simplest possible model ofdevelopment, n which the whole developmental program omprises a single developmentallock, and any earlier change potentially affects all later eatures. It is far more realistic oassume that the developmental program has a number of subsystems within which there maybe strong roughly synchronic nterdependencies nd diachronic dependencies, which interactat most weakly with other. Such components, are thus free to change relativelyindependently of each other (See Glassman and Wimsatt 1984). Such a structure an bemodelled, at least to a first approximation, y a series of parallel and serially connecteddevelopmental ocks. Rasmussen 1987) has employed such a scheme to model and predictquite successfully the structure f interdependencies f developmental mutations nDrosophila, and his results will be discused further below.

In still more complex cases, where the interaction mong developmental ubsystems ssufficiently great, we would expect that the 'developmental ock' model should ose itsusefulness. In such cases, however, he concept of generative entrenchment an still beapplied, with generic consequences which are similar to those of the developmental ock

model. Such cases are endemic in models of the evolution of gene control networks, and nthe discussion of sections 7 through 12 below, it is generative entrenchment ather han thedevelopmental ock model which is the appropriate nalytical ool. It is worth pointing outthat generative entrenchment s not only the more generally applicable concept, but also themore undamental one. The developmental ock models work as they do because theymodel the effects of generative entrenchment. t is therefore not a sound criticism of thisapproach o point out that there are cases where the developmental ock models cannot beapplied, unless it is also true that the concept of generative ntrenchment annot be applied ntheir place.

A scheme like that discovered n Drosophila by Rasmussen 1987) with multiple weaklyinteracting parallel systems, has adaptive advantages over the simple lock as well, sinceparallel organization ends to decrease nteraction nd generative ntrenchment, nd makes tmore probable hat modifications will have relatively ocal effects and fewer consequences.A phenotype which is modifiable n this way represents an adaptive problem which is morelike Simon's 'simple' or decomposeable ock, which can be solved more quickly and for



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which alternative olutions can be found more rapidly, and also allows response tosituations n which modification s required n only one of the subsystems or even in a singletrait. Rasmussen (1987) applies this to the discussion of the independent volution of the

metamerization nd homeotic gene systems in hemimetabolous nd holometabolous nsects.This is closely related o a property which Lewontin (1978) calls 'quasi-independence'--theability to select for individual properties ndependently f one another, which, as he says, isessential for evolution to proceed at reasonable rates. (For further discussion, see Wimsatt198Ib, pp. 141-2.) Thus, we would expect cases of multiple serial and parallel ocks withan at least approximately modular organization f different parts of the developmentalprogram o be relatively common in evolution, and to find modes of organization n thestructure f at least the larger genomes which produce he same effects, either through hisor through different methods. One such method which may be of crucial mportance nevolution will be discussed further below. This is the occurrence n gene control networksof structural lements which we call 'integrator enes.'

5. The use ofgenerative

entrenchment n theanalysis

ofexperimental

data ondevelopment

Arthur 1984) provides short discussions of maternal-acting enes affecting shell coilingin two species of gastropods and of homeotic mutants n insects. While suggestive, thesediscussions (because of their brevity) are more illustrative han confirmatory f the view wewould defend. Recently, Rasmussen (1987) has reviewed a wealth of data fromexperiments on Drosophila development and the expression of a wide variety ofdevelopmental mutants acting at different stages of development. He has found strongconfirmation of the developmental ock model, using it to construct an organizational map ofthe dependencies and interdependencies mong genes affecting development. His modelinvolves a number of developmental ocks, operating n series and in parallel at differentstages of development, and provides a compact organizational cheme for describing a largenumber of interdependencies. See figure 2.) From this experimental data and the model, hewas able to make predictions about additional nterdependencies nd also about he relativeevolutionary age and conservatism of many of the controlling genes. Almost all of hispredictions or which data s now known were confirmed, and he was able to suggest newhypotheses about the interactions mong the controlling genes at both molecular andmacroscopic developmental evels, and new tests of the model.

A summary of what he found is presented n figure 2. The properties of the mutants andthe details of the arguments upporting is location of the action of these genes at theappropriate laces in his map of the developmental program are too complicated o go intohere. Particularly useful for present purposes, Rasmussen proposes and uses three criteriafor determining elative generative ntrenchment n experimental ontexts:

The three distinct criteria or entrenchment which will be employed here can perhapsbe best introduced with a familiar llustration. In an automobile, he activity of theengine is more generatively ntrenched han the activity of the drive wheels. We candetermine his in three ways. If the engine is inactivated, more of the auto's otalfunctions are lost (e.g. power generation, limate control) than f the wheels are frozen;this criterion #1) is that of overall scope of influence. Depending on the status of thetransmission, he engine may accelerate without he drive wheels accelerating, or even ifthey are frozen or absent, but the wheels can never accelerate without the engine doingso (excluding hills, etc.); this criterion #2) is that of asymmetric unctional dependence.Though the drive wheels normally accelerate imultaneously with the engine, they maysometimes accelerate ater, but never earlier; his criterion #3) is that of temporalasymmetry. Rasmussen 1987, pp. 1-2.)



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Larva-OnlyGenes

(predicted)

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Imago-OnlyGenes

s#gmentpolarity

Metamer- /. .erization f pir-rule

Gene genesSystem (inzL A )

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anteriordetrminants( $ica~Jal).

posteriord5trminastr

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N. Risa.ass - 1986

Figure 2. A summary f the generative ntrenchment elationsbetween hose genesand gene functions f the Drosophila developmental ystem which are discussed n thetext, with greater ntrenchment oward he bottom of the page. To the left of each

group of genes is the name of the major ubsystem o which theybelong.Arrowheads ndicatedirection f information low in "program." o the right arerepresentations f the forms generated y the indicated ene functions. Theseapproximate the sequence of fly evolution.

The use of multiple criteria or generative entrenchment nteresting or three reasons:(1) Sometimes data are available or classifying mutants using only one of these criteria.Having three criteria hus increases lexibility in exploratory pplications f the model, fordifferent mutants may be classified using different of the criteria. Rasmussen makesextensive and explicit use of this advantage. (2) Having multiple criteria provides a wealthof opportunities or new predictions n these cases, since the developmental ock model

suggeststhat traits should either meet all of the criteria

if theyare

generatively entrenched)or none of then (if they are not.) Rasmussen was able to provide many such predictions,and all of those for which he was able to get additional data were confirmed. (3) Theexistence of multiple criteria allows 'bootstrap testing' of individual hypotheses generated by

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the model (Glymour 1980), thereby ncreasing ts testability, and giving increasedconfidence in the results because of their robustness Wimsatt 198 a). Rasmussen s alsoclearly aware of this fact.

These criteria differ in their strength and in the conditions under which they can beapplied. All can be applied, as Rasmussen does, in the analysis of experimentally preturbedsystems. Scope of influence and temporal assymmetry an both be relatively easily appliedobservationally, without experimental manipulation f the system. But they are weaker thanassymmetric unctional dependence, which would normally be the main tool of applicationin experimental ontexts. Of these three criteria, only asymmetric unctional dependenceentails relative generative entrenchment f one trait by another when it is appropriately sedin experimental ontexts. Obviously, this criterion an only be meaningfully applied whenboth traits are parts of the same system or same kind of system. It is implicit in thediscussion above (D12) of the use of covariances n traits n the analysis of cross-phylogenetic comparisons, which shows that assymmetric unctional dependence can alsobe

appliedn

comparativebservational ontexts. The other two criteria are

applicablewhether he traits are in the same or in different kinds of systems, and are thus potentiallymore broadly useful for cross-phylogenetic comparisons.

The criterion of scope of influence suggests that things with a broader cope of influenceare more deeply generatively ntrenched, ut raises an important ualification n the relationbetween generative entrenchment nd resistance o change (see also Rasmussen 1986). Onetrait may have a broader cope of influence than a second, but the things which it influencesmay have less of an effect on fitness or be more canalized or homeostatically egulated hanthose of the second. We might then expect that he second could be more conservative nevolution than the first. Thus we need a quantitative measure of generative entrenchment,and the models we present below all use such a measure. Secondly, even if the thingsinfluenced by the second are a subset of those influenced by the first, the first may not becausally involved in the production f the second, since their nfluences could proceed viadistinct pathways.

Finally, temporal order does not entail that the first of two traits has a causal effect on thesecond, even if they are in the same system, since they might be operating n causallydistinct subsystems, as is reflected for some of the mutations n Rasmussen's diagram.(This is resolved by fiat in our simulation models, where the causal dependencies n ourgene control networks are specified.) For real systems, causal analysis n experimentalcontexts of the development of the system should be capable of demonstrating which ofthese alternatives s true.

We will make primary use of the criterion of breadth of scope in our modelling and

analysis of generative entrenchment n gene control networks.6. Evolution, Development, and the Search or Generic Properties

We have argued n the preceeding sections that the developmental ock model ofgenerative entrenchment as interesting and mportant mplications or developmental andevolutionary biology. In the next few sections we will take a look at what we believe maybe an important rea of theoretical pplication or the concept of generative ntrenchment.This area s the large scale structure nd dynamics of cellular gene control systems.

The theoretical and empirical oundations or the models to be discussed are based on thework of Stuart Kauffmann, who has (1969, 1971, 1974) investigated properties of real andmodel cellular and

developmental gene control systems in a series of analytical, empirical,and simulation tudies. Distinguishing between arge scale dynamical and small scaleproperties of gene control at the cellular evel (i.e. transcription, ranslation, nd proteinmodification), Kauffman's basic research strategy n his modelling has been to use small



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scale properties f gene control systems (what Kauffman often calls local constraints) oconstrain and suggest hypotheses about he large scale dynamic behavior of these systems.

On this approach, Kauffman's basic strategy has been the search or generic statisticalproperties of ensembles of binary switching networks defined by certain ocal properties(e.g., the types of Boolean functions which characterize he model genes in a network, hegene/connection atio, the variance n input ines to model genes, etc.). By generic statisticalproperties Kauffman means: those properties which are exhibited by virtually any systemsconstructed ubject to certain ocal constraints. Since these ensembles of possible Booleannetworks can be very large, the generic properties f the ensemble must be discovered bysampling rom the ensemble, a procedure which requires omputationally ntensive MonteCarlo simulations. Since it is found in virtually all members of an ensemble of systems, ageneric property or state is thus in effect a high entropy property or state n the class ofpossible structures nd states of such systems. Although Kauffman reats generic vs.specific' as a dichotomous property, ne may reasonably generalize his to treat genericity asa

degree property apableof also

takingvalues in between the extremes which he discusses,

in effect paralleling quantitative measures of entropy.

Intermediate egrees of genericity should be very important n making comparativequalitative predictions about he rates of evolution and stability of different kinds ofstructures which may be less than totally generic. This fact gives Kauffman's pproach anappealing eature which is missing from most selectionist models as applied n real cases,since the genericity of these properties llows one to make statistical predictions of theiroccurrence even in cases where one may not know much about he details of the situation.

Like high entropy states or properties, generic properties r states are characteristic frandomly assembled systems, and are also approached pontaneously by any system whichstarts out in a state which is far from generic when it is subjected o random perturbations.

Our results below suggest that selectionist approaches an also be predictive n somecases where this aim has been given up as unattainable. The key here lies in the recognitionthat selection regimes and environments an also have generic properties. As a result, weare ed to recognize the existence of generic unctional properties of systems, acomplementary lass to Kauffman's eneric intrinsic properties.

Intuitively, he more generic a property or state is, the easier it should be for selection tomaintain t against the degradationalforces of mutation, ecombination, nd genetic drift.The more generic a selection regime s, the more requently t should be realized--either ncases of a given type, or, if not realized under those conditions, with random structural

changes in the organism or environment. ndeed, given that selection regimes can changejust as (and in part because) organisms can, it is reasonable o believe that organisms willevolve in such a way as to be especially responsive o generic properties of selectionregimes. This is in effect, a corollary o Levins' (1968) claim that organisms evolve so asto minimize the uncertainty n their environments. See Wimsatt 1980a for further elevantdiscussion of this point.)

7. Local Constraints, Extended Forcing Structures, nd Large Scale Dynamical Behavior nGene Control Networks

A fundamental implifying assumption n Kauffman's heoretical work is that the activityof a gene can be approximated s an 'on-off' (binary) witch governed by an n-placeBoolean function.

Empirically,his

assumption appearso be

appoximatelyrue for

manyphage and bacterial genes (Kauffman 1974), and theoretically approximates ontinuoussigmoidal response functions (Glass and Kauffman 1972). Kauffman's model gene controlnetworks are networks of such Boolean automata. The state of a gene at time t is determined



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by a Boolean function of m input lines transmitting he states at time t - 1 of other genes inthe network. Its outputs o any other genes at t are the same as its internal state at t. Largeensembles of Boolean networks are built with various structural onstraints determined by

the small scale properties of components of the network.The model genes of these networks are assumed to change in state synchronously

according o a discrete clock. A sequence of states which is repeated during a givensimulation s called a cycle, and a sequence of states which leads to a given cycle is called atransient. Such networks are deterministic, o that when a state recurs, the descendants ofthat state must recur also, producing a cycle with length given by the number of clock cyclesuntil the initial state recurs. Since a finite network has only a fiite number of states, tsbehavior will necessarily be either (a) steady states or (b) finite state cycles, reached after atransient of finite length.

Kauffman (1969, 1971, 1974) found that a specific category of Boolean functions hasparticularly nteresting onsequences for the dynamic behavior of

binary switchingnetworks. These functions, which he called orcing functions are defined as Booleanfunctions which have at least one value of the target gene's state determined by at least onebinary state value of at least one of its input ines. Kauffman analyzed he behavior ofensembles of these networks characterized y local constraints n (a) the percentage offorcing functions n networks which are members of the ensemble (b) the numbers ofcontrol variables orcing a model gene, and (c) the number of input ines per gene, randomlyconstructed according o constraints on (d) the numbers of output ines for each model geneand (e) the random assignment of a fixed number of output ines from each model gene toother model genes in the network.

He found that under certain certain ocal constraints, binary switching networks producebehavior analogous to that found in cellular gene control systems; .e, when (a) a fixednumber of input ines to each model gene of 2 or more, (b) 60% or more of the Booleaninput functions are forcing, and (c) 2 or more of the imput ines to a forcing function areforcing input lines. Kauffman (1986) summarized nd compared he generic behavior ofbinary switching networks satisfying these constraints with that of typical eukaryotic cells:

1. The predicted number of cell types in an organism rises less than inearly withrespect to the number of genes, as appears o be observed.... In fact, the expectednumber of cell types is about a square oot of the number of genes capable of playingregulatory oles; a genomic system with about 50,000 such genes would be expected tohave on the order of several hundred ell types. This is not a trivial prediction; in otherclasses of model systems the number of cell types rise much faster than the numbers ofgenes. A system with 50,000 genes could readily have billions of cell types.

2. The patterns of gene expression n different cell types are highly similar, typicallydiffering n 10% or less of the loci. This is reasonably close to the observeddifferences....

3. A large core of genes is ubiquitously active in all model cell types in theorganism. Similarly, a large core of genes is ubiquitously xpressed n heterogeneousnuclear RNA, and to a lesser extent in the cytoplastic messenger populations of the manycell types of one higher eukaryote....

4. Model cell types are stable to most fluctuations which transiently ctivate orinactivate genes. Presumably o are contemporary ells.

5. Any cell type can be induced to differentiate irectly oonly

a fewneighboring

elltypes, which may themselves differentiate o a few other neighbors. This implies thenecessary existence of branching pathways of differentiation n ontogeny, a ubiquitousfeature of all metazoan ontogeny.



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6. The limited range of neighbors o any cell type limits the range of abnormaldifferentiation athways open to each, hence simultaneously imiting the range ofabnormal nduction transitions. This property also offers an account of the 'poised'

propertiesof tissues in which

many non-physiologicalr abnormal nductive

agentsinduce the same differentiation tep--e.g. early ectoderm o neurectoderm.

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Stable Steady State

1 -AD> Asquith and R.N. Giere.Lansing, Michigan: The Philosophy of Science Association. pp. 122-183.

_ (1986). "Developmental Constraints, Generative Entrenchment, andthe Innate-Acquired istinction," In Integrating Scientific Disciplines. Edited by W.Bechtel. Dordrecht: Martinus-Nijhoff. Pages 185-208.

(1987). "Generative ntrenchment, cientific Change, and theAnalytic-Synthetic Distinction: A Developmental Model of Scientific Evolution."Under revision for submission to Philosophy of Science.

Wimsatt, W., and J. Schank. (1987). "Two Constraints n the Evolution of ComplexAdaptations." Paper n preparation or Ideas of Progress in Evolution, he 10thannual Spring Systematics Symposium of the Field Museum of Natural History inChicago, May, 1987.

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Generative Entrenchment and Evolution