[Paleopsych] PNAS: Evolvability is a selectable trait -- Earl and Deem 101 (32): 11531
Premise Checker
checker at panix.com
Sun Aug 15 19:54:21 UTC 2004
PNAS: Evolvability is a selectable trait -- Earl and Deem 101 (32): 11531
http://www.pnas.org/cgi/content/full/101/32/11531
[I don't know what the connection with group selection is.]
Published online before print August 2, 2004, 10.1073/pnas.0404656101
PNAS | August 10, 2004 | vol. 101 | no. 32 | 11531-11536
PHYSICS / EVOLUTION
Evolvability is a selectable trait
David J. Earl and Michael W. Deem^ [28]*
Department of Bioengineering and Department of Physics and Astronomy,
Rice University, Houston, TX 77005-1892
Communicated by David Chandler, University of California, Berkeley,
CA, June 30, 2004 (received for review April 19, 2004)
Concomitant with the evolution of biological diversity must^ have been
the evolution of mechanisms that facilitate evolution,^ because of the
essentially infinite complexity of protein sequence^ space. We
describe how evolvability can be an object of Darwinian^ selection,
emphasizing the collective nature of the process.^ We quantify our
theory with computer simulations of protein^ evolution. These
simulations demonstrate that rapid or dramatic^ environmental change
leads to selection for greater evolvability.^ The selective pressure
for large-scale genetic moves such as^ DNA exchange becomes
increasingly strong as the environmental^ conditions become more
uncertain. Our results demonstrate that^ evolvability is a selectable
trait and allow for the explanation^ of a large body of experimental
results.^
___________________________________
Darwin was obsessed with variation. His books, considered as^ an
ensemble, devote much more attention to variation than to^ natural
selection, because he knew that no satisfactory theory^ of
evolutionary change could be constructed until the causes^ of
variation and the empirical rule of its form and amount had^ been
elucidated ([37]1).
^
Whether the propensity to evolve, or evolvability ([38]2-[39]4),^ can
be an object of Darwinian natural selection is a topic of^ interest.
Causality would suggest not because of the apparently^ anticipatory
nature of evolvability ([40]5, [41]6). Many within the^ field of
evolutionary biology are uncomfortable with the concept^ that
evolvability is a selectable trait. A growing body of experimental^
data, however, would be explained if evolvability were a selectable^
trait ([42]7-[43]15).^
Higher organisms cannot evolve, or adapt, by germ-line mutation^ to an
environmental change within their own lifetime. Does this^ mean that
lineages and individuals cannot be under selection^ for evolvability?
Although viability is the selection criterion,^ the genotype that
determines the viability arises in a mutated,^ evolved way from that
of the previous generation as a result^ of base substitution,
recombination, transposition, and horizontal^ gene transfer. These
mutational processes are the driving forces^ of evolution, and their
rates fundamentally determine evolvability.^ The perspective we offer
here is that the evolvability of an^ organism is defined by the rates
of genetic change, that genetic^ change is not always deleterious, and
that these rates of genetic^ change are not fixed and are under
selective pressure. That^ is, the mechanisms that define the rates of
change are encoded^ in the genotype, and so they are selectable. An
analogy with^ thermodynamics illuminates the issue: How is free energy
minimized^ in a physical system of particles despite the difficulty
in^defining the entropy of a given configuration of the particles?^ An
ensemble of particle configurations allows the definition^ of free
energy and the approach to thermodynamic equilibrium^ just as a
population of evolving organisms allows the definition^ of and
selection for evolvability.^
Within the framework of point mutation, base substitution, and^
recombination, correlations of adaptation with function have^ been
observed. It is known that immunoglobins have evolved such^ that the
mutation rates in complementary determining regions,^ in which
mutation is most likely to generate useful variants,^ are much higher
than those in framework regions ([44]14, [45]16). Recent^ data point
to a role for DNA polymerases in regulating the somatic^ hypermutation
rate of immunoglobin genes ([46]13, [47]17). Similarly,^ codon usage
within the influenza hemagglutinin protein seems^ to be biased to
favor more rapid antigenic drift ([48]14). Furthermore,^ in HIV-1
protease, the probability of mutation is not randomly^ distributed
within the structure but rather concentrated at^ sites that alter the
geometry of the protein-binding domain,^ conferring significant
propensity for antigenic drift ([49]18).^ Such behavior is not mere
curiosity and has widespread implications^ for drug design and the
evolution of drug resistance ([50]19). Stressful^ conditions may
generally provoke activation of error-prone polymerases,^ triggering a
large increase in adaptive rates ([51]20). Not only^ point mutation
but also recombination are widely appreciated^ to confer increased
evolvability ([52]9, [53]21, [54]22). Recombination^ among the
hemagglutinin and neuraminidase proteins, for example,^ is believed to
be a significant mechanism leading to the emergence^ of new virulent
strains of influenza ([55]23). Computational and^ theoretical studies
have also shown the persistence under selection^ of
evolvability-enhancing moves in the context of point mutation^ and
recombination evolutionary dynamics ([56]24-[57]29).^
The selective forces that lead to the evolution and maintenance^ of
mechanisms for rearrangement, deletion, transfer, and transposition^
of genetic material, inasmuch as they lead to even greater evolution^
than point mutation and recombination alone, are of great interest.^
For example, the development of antibiotic resistance in bacteria^ has
evolved mainly through the swapping of DNA pieces between^ the
evolving bacteria ([58]8, [59]30). Similarly, the evolution of
Escherichia^ coli from Salmonella is thought to have occurred
exclusively^ from DNA swapping ([60]31). It has been proposed that the
success^ of bacteria as a group stems from a capacity to acquire
genes^from a large and diverse range of species ([61]32). It would
seem,^ then, that large genetic moves are pervasive and crucial to^
evolutionary dynamics ([62]6, [63]8, [64]10-[65]12, [66]15, [67]30,
[68]31, [69]33-[70]39).^ Concomitantly, evolvability is enhanced by
these larger moves,^ as shown experimentally for the case of DNA
shuffling ([71]32, [72]40-[73]44).^ A key question is whether
selection for evolvability fosters^ the husbandry of these moves.^
We address here, from a theoretical point of view, selection^ of
evolvability in the presence of large-scale genetic moves.^ Although
the use of the term evolvability has only recently^ come into vogue in
the scientific community, investigations^ into the evolution of
adaptation go back several decades ([74]45-[75]47).^ Prominent from a
theoretical perspective are works in population^ genetics ([76]48,
[77]49) and game theory ([78]50-[79]52). Despite the^ insights that
these studies give as to the origin and maintenance^ of evolvability,
evolution of and selection for evolvability^ remains a contested issue
primarily because of the causality^ principle ([80]5, [81]6). We show
here that evolvability is selected^ for, notwithstanding the
constraints imposed by causality, when^ a system is subject to a
constant, random environmental change.^ This selection for
evolvability occurs even when viability as^ a function of genotype is
an extremely complex function, with^ exponentially many optima, and
when the evolving system is unable^ to reach the global optimum of
viability in any one instance^ of the environment. We demonstrate our
results by using computer^ simulations of protein molecular evolution
that incorporate^ selection in a varying environment. The genotype of
a protein^ molecule is mapped to a complex phenotype by using a
generalized^ NK model in which all assumptions and relevant parameters
are^ known. The selective pressure for evolvability is shown to be^
greater for larger rates of environmental change. Interestingly,^ a
generalized susceptibility of the system correlates with the^
fluctuations in the environment, albeit not as a result of
generalized^linear response theory ([82]53). The addition of selection
for evolvability^ as a phenomenological law to the toolbox of
evolutionary theory^ allows for the explanation of a large body of
experimental results.^
The Generalized Block NK Model
[83][uarrow.gif] Top
[84][uarrow.gif] Abstract
[dot.gif] The Generalized Block NK...
[85][darrow.gif] System Evolution and...
[86][darrow.gif] Selection for Evolvability
[87][darrow.gif] Susceptibility
[88][darrow.gif] Implications for Evolution
[89][darrow.gif] Summary
[90][darrow.gif] References
Whether evolvability is selectable has been a difficult question^ to
answer, primarily because observations in evolutionary biology^ tend
to be correlative in nature and difficult on which to make^
mechanistic conclusions. Therefore, we consider here the dynamics^ of
evolvability in a well defined theoretical model of protein^ evolution
([91]54). Within this model of protein structure and function,^ we
have a fixed population of proteins, which we take to be^ 1,000. We
have a microscopic selection criterion, which we take^ to be the
folding and binding of a protein to a substrate. And^ we have a means
of inducing constant, random environmental change.^
We model the molecular evolution of protein systems by using^ a
generalization of the NK ([92]55-[93]57) and block NK ([94]58) models^
that has been used previously to study protein molecular evolution^
strategies ([95]54) and the immune-system response to vaccination^ and
disease ([96]59). The model includes a population of sequences,^ upon
which selection acts and in which occur genetic mutations.^ The
mutational hierarchy includes both point mutations and large-scale^
swapping moves, akin to transposition or translocation events.^
Although the model does not include recombination, such inclusion^ is
not expected to change the results because swapping can be^ viewed as
a powerful form of recombination ([97]54). For example,^ linkage
effects are mitigated even more rapidly by swapping^ in our model than
they would be by recombination. The selection^ for greater swapping
rates in more rapidly changing environments^ observed in our model
parallels results found in studies of^ the evolution of sex, for which
adaptation and variation in^ a heterogeneous environment is well
researched ([98]60).^
In the generalized block NK model, each individual evolving^ protein
sequence has an energy that is determined by secondary^ structural
subdomain energies, U^sd, subdomain-subdomain^ interaction energies,
U^sd-sd, and chemical binding energies,^ U^c. This energy is used as
the selection criteria in our studies^ and is given by ^
[fd1.gif] [1]
Within our generalized^ block NK model, each protein molecule is
composed of M = 10^ secondary structural subdomains of N = 10 aa in
length. We consider^ five chemically distinct amino acid classes
(negative, positive,^ polar, hydrophobic, and other) and L = 5
different types of^ subdomains (helices, strands, loops, turns, and
others). We^ therefore have L different subdomain energy functions of
the^ NK form ^
[fd2.gif] [2]
where a[j] is the amino acid^ type of the jth amino acid in the
subdomain, and {alpha} [i] is the type^ of the ith subdomain. As in
previous studies, we consider the^ case in which the range of the
interactions within a subdomain^ is specified by K = 4 ([99]54,
[100]59). Here {sigma} [{alpha} i] is a quenched Gaussian^ random
number with zero mean and a variance of unity, and it^ is different
for each value of its argument for each of the^ L subdomain types,
{alpha} [i]. The interaction energy between secondary^ subdomain
structures is given by ^
[fd3.gif] [3]
where^ we consider D = 6 interactions between secondary structures^
([101]54, [102]59). The zero-mean, unit-variance Gaussian [f4.gif] and
the interacting amino acids, j[1],..., j[K], are selected at^ random
for each interaction (i, j, k). In our model, P = 5 aa^ contribute
directly to a binding event, as in a typical pharmacophore,^ where the
chemical binding energy of each amino acid is given^ by ^
[fd5.gif] [4]
where the zero-mean, unit-variance^ Gaussian {sigma} [i] and the
contributing amino acid, i, are chosen at^ random.^
System Evolution and Environmental Change
[103][uarrow.gif] Top
[104][uarrow.gif] Abstract
[105][uarrow.gif] The Generalized Block NK...
[dot.gif] System Evolution and...
[106][darrow.gif] Selection for Evolvability
[107][darrow.gif] Susceptibility
[108][darrow.gif] Implications for Evolution
[109][darrow.gif] Summary
[110][darrow.gif] References
Our model system maintains a constant population of 1,000 proteins,^
each protein of 100 aa in length and initially distinct in sequence.^
The system evolves through the base substitution of single amino^
acids and through DNA swapping of amino acid subdomains from^
structural pools representing the five different subdomain types,^
each containing 250 low-energy subdomain sequences. These moves^
represent the small-scale adaptation and the large-scale, dramatic^
evolution that occur in nature. For protein i, n[mut](i) point^
mutations occur per sequence per round of selection. In addition,^ for
protein i, subdomain sequences are replaced randomly with^ sequences
from the same-type low-energy pools with probability^ p[swap](i).^
After pool swapping and point mutations, selection occurs, and^ the
20% lowest-energy protein sequences are kept and amplified^ to form
the population of 1,000 proteins for the next round^ of selection. The
parameters p[swap](i) and n[mut](i) are allowed^ to take a
log-Gaussian random walk for each protein sequence.^ This process is
repeated for N[gen] rounds of selection, after^ which an environmental
change is imposed on the system with^ a severity that is characterized
by the parameter p ([111]59). The^ parameter p is the probability of
(i) changing the type of each^ of the 10 subdomains in the protein
sequences, {alpha} [i] in Eq. 2, (ii)^ changing the amino acids and
energies that are involved in subdomain-subdomain^ interactions, j[k]
and {sigma} ^(k)[ij] in Eq. 3, and (iii) changing the^ amino acids and
energies that are involved in the chemical binding,^ i and {sigma} [i]
in Eq. 4. We repeat the process for a total of 100 environmental^
changes and average our results over 1,000 instances of the^ ensemble.
For each system studied, a steady state in n[mut], p[swap],^ and the
average energies at the beginning, < U > [start], and end,^ < U >
[end], of the dynamics in a single instance of the environment^ is
reached after <80 environmental changes in all cases.^ We average the
data over the last 20 environmental changes.^ We study how the
frequency of environmental change, 1/N[gen],^ and the severity of
environmental change, p, affect the evolvability^ of the protein
sequences. A schematic diagram showing the molecular^ evolution of our
protein system can be seen in [112]Fig. 1.^
^
View larger version (27K):
[113][in this window]
[114][in a new window]
Fig. 1. Schematic diagram showing the evolution of the protein
system.
Selection for Evolvability
[115][uarrow.gif] Top
[116][uarrow.gif] Abstract
[117][uarrow.gif] The Generalized Block NK...
[118][uarrow.gif] System Evolution and...
[dot.gif] Selection for Evolvability
[119][darrow.gif] Susceptibility
[120][darrow.gif] Implications for Evolution
[121][darrow.gif] Summary
[122][darrow.gif] References
Shown in [123]Fig. 2 are the steady-state values of p[swap] and
n[mut]^that our protein system selects as a function of imposed
frequency^ of environmental change, 1/N[gen], and severity of
environmental^ change, p. The DNA swapping moves that we propose have
a high^ capacity for evolutionary change, because a significant
number^of amino acids may be altered in a protein sequence in one
swap^move. It is clear that our systems select for higher
probabilities^ of DNA swapping moves, and thus evolvability, as the
frequency^ and severity of environmental change increases. We stress
the^ importance of this result. Mainstream evolutionary theory does^
not recognize a need for the selection of evolvability. More^
generally, we see that only in the limit of little or no
environmental^change, p[swap] -> 0, do large-scale changes tend to be
disfavored.^ The role of base substitution in our evolving system is
more^ complex. For more severe environmental changes and for higher^
frequencies of environmental change, the system depends more^ on DNA
swapping than on point mutation to produce low-energy^ proteins. In
these cases, because the protein must make large^ changes to its
sequence to adapt to the environmental change,^ selection results in
high values of p[swap], with base substitution^ having only a small
effect on the energy of the protein. For^ less severe environmental
changes and lower frequencies of environmental^ change, base
substitution is sufficient to achieve the small^ modifications in
protein sequence that are required for adaptation^ to the
environmental change. Thus, we observe the higher dependence^ on
n[mut]and lower dependence on p[swap] for small p. In addition,^ as
1/N[gen] -> 0, n[mut] -> 0, because mutations tend to be deleterious^
in stable systems with no environmental fluctuations.^
^
View larger version (22K):
[124][in this window]
[125][in a new window]
Fig. 2. n[mut] (dashed lines) and p[swap] (solid lines) as a
function of the frequency of environmental change, 1/N[gen], for
different values of the severity of environmental change, p. The
statistical errors in the results are smaller than the symbols on the
figure.
Evolvability is intimately related to the diversity of a population.^
At short times, evolvability can be quantified by the diffusion^
coefficient in protein sequence space, D[0], which is given by^ the
combined diffusion due to swapping of the subdomains and^ the point
mutation of individual amino acids ([126]61): ^
[fd6.gif] [5]
The overwhelming contribution to D[0] comes from^ the swapping step,
because the swapping move far more dramatically^ changes the sequence.
The short-time diffusion rate selected^ for reflects, as a function of
environmental change, a balance^ between staying within a favorable
basin of attraction, or niche,^ and adaptation to a newly created,
superior niche. As [127]Fig. 2^ shows, greater environmental change
favors greater local diffusion,^ as indicated by the monotonic
increase of p[swap] with p.^
It is useful to regard base substitution as a means of fine^ tuning
the protein sequences, whereas DNA swapping can be considered^ a
source of more substantial evolutionary change. This hierarchy^ within
the space of evolutionary moves becomes more apparent^ when studying
the difference between starting and ending protein^ sequences within
one environment as a function of p, p[swap],^ and n[mut]. The distance
between protein sequences is characterized^ by the Hamming distance
between the respective amino acid sequences.^ For a given p, the
Hamming distance decreases only slightly^ as the frequency of
environmental change, 1/N[gen], increases,^ but it has a very strong
dependence on the severity of the environmental^ change, p, as shown
in [128]Fig. 3a. The sensitivity of the Hamming^ distance also shows
markedly different behavior to p[swap] and^ n[mut], as shown in
[129]Fig. 3b. For state points with fixed n[mut],^ 1/N[gen], and p,
the Hamming distance strongly depends on the^ value of p[swap].
However, for state points with fixed p[swap],^ 1/N[gen], and p, the
Hamming distance displays little or no variation^ with n[mut]. The
Hamming distance is a long-time measure of the^ evolvability of the
system. The long-time diffusion coefficient^ can be defined as the
square of the Hamming distance multiplied^ by the frequency of
environmental change. As [130]Fig. 3a implies,^ the long-time
evolvability, as measured by the long-time diffusion^ coefficient,
increases with both the severity and frequency^ of environmental
change.^
^
View larger version (17K):
[131][in this window]
[132][in a new window]
Fig. 3. Hamming distance and average variance. (a) Hamming distance
as a function of the severity of environmental change, p, for the
state points shown in [133]Fig. 2. (b) Hamming distance as a function
of n[mut] (dashed lines) and p[swap] (solid lines) for fixed N[gen] =
15 and for different severities of environmental change, p. In
displaying the Hamming distance dependence on n[mut] (p[swap]), we fix
p[swap] (n[mut]) to the selected values from [134]Fig. 2. The selected
values of n[mut] and p[swap] at each state point are shown by light
and dark circles, respectively. (c) Average variance, {sigma}
^2[U][end], of the energy of a population at the end of an evolution,
U[end], as a function of the severity of environmental change, p, for
different frequencies of environmental change, 1/N[gen].
Due to the roughness of viability as a function of sequence,^ the
exploration performed by any particular individual is limited^ to a
local basin of attraction defined by the short-time mutation^ rates,
and thus more independent traces through sequence space^ allow for
more thorough evolution. In other words, the more^ diverse the
starting population of individuals, the greater^ potential there is
for evolution. [135]Fig. 3c shows the average^ variance of the energy
values at the end of the dynamics within^ a single instance of the
environment as a function of the severity^ and frequency of
environmental change. It is clear that the^ diversity increases
monotonically with p and 1/N[gen].^
As we have seen, evolvability is quantifiable at any point in^ time
through measurement of diversity and the local mutation^ rates. For
this reason, causality does not prevent selection^ for evolvability.
Because evolvability is an observable property,^ it can be selected
for.^
Susceptibility
[136][uarrow.gif] Top
[137][uarrow.gif] Abstract
[138][uarrow.gif] The Generalized Block NK...
[139][uarrow.gif] System Evolution and...
[140][uarrow.gif] Selection for Evolvability
[dot.gif] Susceptibility
[141][darrow.gif] Implications for Evolution
[142][darrow.gif] Summary
[143][darrow.gif] References
A further measure of long-time evolvability is the response,^ or
susceptibility, of the system to environmental change. In^ [144]Fig.
4a we plot the average energy at the start, < U > [start], and^ end, <
U > [end], of the dynamics within a single instance of the
environment.^ This quantity is shown as a function of the severity, p,
and^ frequency, 1/N[gen], of environmental change. It is apparent
that^at low frequencies of environmental change, populations with^
greater diversity and variation, which are more evolvable, have^
slightly lower values of < U > [end]. There is also a clear
increasing^trend in < U > [start] as a function of p, which is a
feature of the^ generalized NK model. Considering the ending energy of
a protein^ molecule within one instance of the environment to be
roughly^ the sum of n Gaussian terms from the generalized NK model, ^
[fd7.gif] [6]
The starting energy of this protein molecule^ after an environmental
change is given by ^
[fd8.gif] [7]
^where ^
[fd9.gif] [8]
and where [f10.gif] are random Gaussian variables with zero mean (
[f11.gif] ), whereas x[i] are evolved variables that are better than
random^ and typically negative. Thus, the average starting energy of^
this protein molecule is ^
[fd12.gif] [9]
Thus, averaging^ over the values in the new environment ^
[fd13.gif] [10]
^or, averaging over many environmental changes ^
[fd14.gif] [11]
^
^
View larger version (15K):
[145][in this window]
[146][in a new window]
Fig. 4. Average energy, average change in energy, and probability
distribution. (a) Average energy immediately after, < U > [start], and
immediately before, < U > [end], an environmental change as a function
of the severity of environmental change, p, for different frequencies
of environmental change. (b) Average change in energy, < {Delta} U > ,
multiplied by the frequency of environmental change, 1/N[gen], as a
function of the severity of environmental change, p. (c) Probability
distribution of the susceptibility for different values of the
severity of environmental change, p, for a fixed frequency of
environmental change, 1/N[gen] = 0.1.
This average reduction in the energy is a measure of the
susceptibility^ of a system, < {Delta} U > /N[gen] = ( < U > [end] - <
U > [start])/N[gen]. In [147]Fig. 4b^ we plot the susceptibility of
our system as a function of the^ severity of environmental change, p.
For a fixed frequency of^ environmental change, the susceptibility is
a linear function^ of the severity of environmental change, as in Eq.
11. This^ simple analysis captures the essence of the dynamics that
occurs^ in the correlated, generalized NK model. [148]Fig. 4c shows
that^ the probability distribution of the susceptibility is Gaussian^
in shape. Note also that the variance of the susceptibility^ increases
with p in [149]Fig. 4c, and thus the linearity of the susceptibility^
in [150]Fig. 4b is not simply the result of a generalized
fluctuation-dissipation^ theorem.^
Implications for Evolution
[151][uarrow.gif] Top
[152][uarrow.gif] Abstract
[153][uarrow.gif] The Generalized Block NK...
[154][uarrow.gif] System Evolution and...
[155][uarrow.gif] Selection for Evolvability
[156][uarrow.gif] Susceptibility
[dot.gif] Implications for Evolution
[157][darrow.gif] Summary
[158][darrow.gif] References
Our results have implications for evolutionary theory. In our^ model
system, populations of protein molecules that are subject^ to greater
environmental change select for higher rates of evolvability.^ The
selection criterion that we use is not a measure of evolvability^ in
any way, yet the system selects for evolvability based on^ the
implicit energetic benefits of adaptation to environmental^ change. In
addition, there is no reason to assume that selection^ is optimal. In
fact, systems optimal for one environment tend^ to have too little
evolvability and tend to be selected against^ when faced with the
inevitability of change.^
Given our results, we propose that it is not mere chance that^ highly
evolvable species tend to be found in rapidly changing^ environments
or that an environmental crisis can trigger an^ increase in the rate
of the evolution of a species. Indeed,^ selection for evolvability
allows for the explanation of many^ data: the existence of somatic
hypermutation in the immune system^ ([159]13, [160]14, [161]16,
[162]17), the evolution of drug resistance in species^ of bacteria
([163]8, [164]30), and the occurrence and success of transpositional^
events in bacterial evolution ([165]10, [166]31, [167]36). A recently
studied^ example from mammals is the San Nicolas Island fox, which is^
a highly endangered species and the most monomorphic sexually^
reproducing animal known. This species, however, is found to^ have
high levels of genetic variation within the major histocompatibility^
complex loci ([168]62) that allows for increased pathogen resistance.^
We believe that our results are of relevance to the field of^ vaccine
and drug design. Currently, the design of new vaccines^ and drugs is
largely based on the assumption that pathogens^ evolve by local space
searching in response to therapeutic and^ immune selection. However,
it is clear that we must anticipate^ the evolutionary potential of
large DNA swapping events in the^ development of viruses, parasites,
bacteria, and cancers if^ we are to engineer effective methods of
treating them. How evolvability^ correlates with treatment strategy,
and how to drive pathogens^ into regions of low evolvability where
they are eradicated most^ easily, is of importance to efforts for
vaccine and drug engineering.^
Specific pathogenic examples of evolvability include the emergence^ of
new influenza strains by a novel hemagglutinin neuraminidase^
recombination, followed by antigenic drift to a highly infectious^
strain ([169]23); emergence of many new HIV strains with the spread^
of the disease from its site of origin in Africa ([170]63, [171]64);
and^ the increased emergence of new infectious diseases associated^
with modern, post-World War II travel ([172]65). Additionally, a^
recent study of the dynamics of HIV-1 recombination suggests^ that
HIV-1 may have evolved high recombination rates to foster^ rapid
diversification and further its survival ([173]66).^
Note that evolvability is not simply the observation that new^ strains
occur; rather, it is the underlying probability with^ which new
strains are created by genetic modification. These^ new strains may
proliferate and be observed, or they may fail^ and not be observed to
an appreciable extent. Fundamental study^ of evolvability, then,
requires an appreciation of these underlying^ rates of genetic change.
These underlying rates, such as polymerase^ error rates, recombination
rates, and transposition rates, are^ what selection for increased
evolvability may modulate ([174]67).^ These underlying rates of change
are inheritable and can be^ altered by mutation. Study of these rates
of genetic change,^ deconvoluted from observed rates of evolution,
which are these^ rates multiplied by a probability of survival, is of
fundamental^ interest.^
It is intriguing that we find that at low frequencies of
environmental^change, populations that are subject to more severe
environmental^ changes can produce lower-energy individuals than
populations^ that are not subject to environmental changes ([175]Fig.
4a). Thus,^ under some conditions, adaptability can provide global
benefits.^ This finding can be contrasted to the more customary
expectation^ that specialists are better than generalists ([176]68).
In experimental^ studies of Chlamydomonas, generalists that were
evolved in alternating^ light and dark conditions were found to be
better than their^ ancestors in both light and dark conditions but
less good than^ specialists that had evolved exclusively in one of the
environmental^ conditions ([177]69). Studies of the evolution of E.
coli at constant^ and alternating temperatures produced similar
results ([178]70, [179]71).^ The nature of the environmental change in
these studies is not^ completely random as in our model. In addition,
the number of^ rounds of selected evolution under each environmental
condition^ is perhaps better defined within our model. These
experiments^ do point to possible tests of our theory. For a species
that^ is capable of DNA swapping evolutionary moves, a systematic^
study of competency as a function of the frequency of a random^
environmental change would be of interest. We predict that under^ some
conditions, certain frequencies of environmental change^ will produce
better individuals, after a given number of rounds^ of evolution and
selection, than would be produced by evolution^ in a constant
environment. Different severities of environmental^ change could also
be imposed by altering the change in environmental^ variables between
samples, such as temperature, food concentrations,^ light conditions,
and exposure to disease. With regard to susceptibility,^ we would
expect the rate of change of viability within an environment^ to be
higher in systems with more frequent and harsher environmental^
changes because of greater evolvability.^
Summary
[180][uarrow.gif] Top
[181][uarrow.gif] Abstract
[182][uarrow.gif] The Generalized Block NK...
[183][uarrow.gif] System Evolution and...
[184][uarrow.gif] Selection for Evolvability
[185][uarrow.gif] Susceptibility
[186][uarrow.gif] Implications for Evolution
[dot.gif] Summary
[187][darrow.gif] References
Not only has life evolved, but life has evolved to evolve. That^ is,
correlations within protein structure have evolved, and^ mechanisms to
manipulate these correlations have evolved in^ tandem. The rates at
which the various events within the hierarchy^ of evolutionary moves
occur are not random or arbitrary but^ are selected by Darwinian
evolution. Sensibly, rapid or extreme^ environmental change leads to
selection for greater evolvability.^ This selection is not forbidden
by causality and is strongest^ on the largest-scale moves within the
mutational hierarchy.^
Many observations within evolutionary biology, heretofore considered^
evolutionary happenstance or accidents, are explained by selection^
for evolvability. For example, the vertebrate immune system^ shows
that the variable environment of antigens has provided^ selective
pressure for the use of adaptable codons and low-fidelity^ polymerases
during somatic hypermutation. A similar driving^ force for biased
codon usage as a result of productively high^ mutation rates is
observed in the hemagglutinin protein of influenza^ A. Selection for
evolvability explains the prevalence of transposons^ among bacteria
and recombination among higher organisms. We^ suggest that
therapeutics also confer selective pressure on^ the evolvability of
pathogens, and that this driving force for^ antigenic drift should be
considered in drug- and vaccine-design^ efforts.^
Acknowledgements
We thank Kevin R. Foster for a careful reading of the manuscript.^
This research is supported by the National Institutes of Health.^
^* To whom correspondence should be addressed at: Department of
Bioengineering and Department of Physics and Astronomy, MS 142, Rice
University, 6100 Main Street, Houston, TX 77005-1892. E-mail:
[188]mwdeem at chinook.rice.edu.
© 2004 by [189]The National Academy of Sciences of the USA
References
[190][uarrow.gif] Top
[191][uarrow.gif] Abstract
[192][uarrow.gif] The Generalized Block NK...
[193][uarrow.gif] System Evolution and...
[194][uarrow.gif] Selection for Evolvability
[195][uarrow.gif] Susceptibility
[196][uarrow.gif] Implications for Evolution
[197][uarrow.gif] Summary
[dot.gif] References
1. Gould, S. J. (1983) Hen's Teeth and Horse's Toes (Norton, New
York).
2. Kirschner, M. & Gerhart, J. (1998) Proc. Natl. Acad. Sci. USA 95,
8420-8427.[198][Abstract/Free Full Text]
3. Dawkins, R. (1989) in Artificial Life, ed. Langton, C. G.
(Addison-Wesley, New York), pp. 201-220.
4. Radman, M., Matic, I. & Taddei, F. (1999) Ann. N.Y. Acad. Sci.
870, 146-155.[199][Abstract/Free Full Text]
5. Chicurel, M. (2001) Science 292, 1824-1827.[200][Free Full Text]
6. Partridge, L. & Barton, N. H. (2000) Nature 407,
457-458.[201][CrossRef][202][ISI][203][Medline]
7. Kidwell, M. G. (1997) Proc. Natl. Acad. Sci. USA 94,
7704-7711.[204][Abstract/Free Full Text]
8. Shapiro, J. A. (1997) Trends Genet. 13,
98-104.[205][CrossRef][206][ISI][207][Medline]
9. Barton, N. H. & Charlesworth, B. (1998) Science 281,
1986-1990.[208][Abstract/Free Full Text]
10. Fedoroff, N. (2000) Proc. Natl. Acad. Sci. USA 97,
7002-7007.[209][Abstract/Free Full Text]
11. Shapiro, J. A. (2002) J. Biol. Phys. 28,
745-764.[210][CrossRef][211][ISI]
12. Shapiro, J. A. (2002) Ann. N.Y. Acad. Sci. 981,
111-134.[212][Abstract/Free Full Text]
13. Storb, U. (2001) Nat. Immunol. 2,
484-485.[213][CrossRef][214][ISI][215][Medline]
14. Plotkin, J. B. & Dushoff, J. (2003) Proc. Natl. Acad. Sci. USA
100, 7152-7157.[216][Abstract/Free Full Text]
15. Caporale, L. H. (2003) Am. Sci. 91,
234-241.[217][CrossRef][218][ISI]
16. Kepler, T. B. (1997) Mol. Biol. Evol. 14, 637-643.[219][Abstract]
17. Friedberg, E. C., Feaver, W. F. & Gerlach, V. L. (2000) Proc.
Natl. Acad. Sci. USA 97, 5681-5683.[220][Free Full Text]
18. Freire, E. (2002) Nat. Biotechnol. 20,
15-16.[221][CrossRef][222][ISI][223][Medline]
19. Kepler, T. B. & Perelson, A. S. (1998) Proc. Natl. Acad. Sci. USA
95, 11514-11519.[224][Abstract/Free Full Text]
20. Rosenberg, S. M. (2001) Nat. Rev. Genet. 2,
504-515.[225][CrossRef][226][ISI][227][Medline]
21. Pepper, J. W. (2003) BioSystems 69,
115-126.[228][CrossRef][229][ISI][230][Medline]
22. Colegrave, N. (2002) Nature 420,
664-666.[231][CrossRef][232][ISI][233][Medline]
23. Frank, S. A. (2002) Immunology and Evolution of Infectious Disease
(Princeton Univ. Press, Princeton).
24. Wagner, G. P. & Altenberg, L. (1996) Evolution (Lawrence, Kans.)
50, 967-976.
25. Lenski, R. E., Ofria, C., Pennock, R. T. & Adami, C. (2003) Nature
423, 139-144.[234][CrossRef][235][ISI][236][Medline]
26. Blasio, F. V. D. (1999) Phys. Rev. E 60,
5912-5917.[237][CrossRef][238][ISI]
27. Travis, J. M. J. & Travis, E. R. (2002) Proc. R. Soc. London Ser.
B 269, 591-597.[239][CrossRef][240][ISI][241][Medline]
28. Siegal, M. L. & Bergman, A. (2002) Proc. Natl. Acad. Sci. USA 99,
10528-10532.[242][Abstract/Free Full Text]
29. Bergman, A. & Siegal, M. L. (2003) Nature 424,
549-552.[243][CrossRef][244][ISI][245][Medline]
30. Shapiro, J. A. (1992) Genetica 86, 99-111.[246][ISI][247][Medline]
31. Lawrence, J. G. (1997) Trends Microbiol. 5,
355-359.[248][CrossRef][249][ISI][250][Medline]
32. Zhang, Y.-X., Perry, K., Vinci, V. A., Powell, K., Stemmer, W. P.
C. & del Cardayre, S. B. (2002) Nature 415,
644-646.[251][CrossRef][252][ISI][253][Medline]
33. Pennisi, E. (1998) Science 281, 1131-1134.[254][Free Full Text]
34. Gilbert, W. (1978) Nature 271, 501.[255][ISI][256][Medline]
35. Gilbert, W., DeSouza, S. J. & Long, M. (1997) Proc. Natl. Acad.
Sci. USA 94, 7698-7703.[257][Abstract/Free Full Text]
36. Duret, L., Marais, G. & Biemont, C. (2000) Genetics 156,
1661-1669.[258][Abstract/Free Full Text]
37. Lonnig, W.-E. & Saedler, H. (2002) Annu. Rev. Genet. 36,
389-410.[259][CrossRef][260][ISI][261][Medline]
38. Levin, B. R. & Bergstrom, C. T. (2000) Proc. Natl. Acad. Sci. USA
97, 6981-6985.[262][Abstract/Free Full Text]
39. Doolittle, W. F. (2000) Sci. Am. 282 (2),
90-95.[263][ISI][264][Medline]
40. Stemmer, W. P. C. (1994) Nature 370,
389-391.[265][CrossRef][266][ISI][267][Medline]
41. Crameri, A., Raillard, S. A., Bermudez, E. & Stemmer, W. P. C.
(1998) Nature 391, 288-291.[268][CrossRef][269][ISI][270][Medline]
42. Zhang, J.-H., Dawes, G. & Stemmer, W. P. C. (1997) Proc. Natl.
Acad. Sci. USA 94, 4504-4509.[271][Abstract/Free Full Text]
43. Moore, J. C., Jin, H.-M., Kuchner, O. & Arnold, F. H. (1997) J.
Mol. Evol. 272, 336-347.
44. Lutz, S. & Benkovic, S. J. (2000) Curr. Opin. Biotechnol. 11,
319-324.[272][CrossRef][273][ISI][274][Medline]
45. Clarke, B. C. (1979) Proc. R. Soc. London Ser. B 205,
453-474.[275][ISI][276][Medline]
46. Dawkins, R. & Krebs, J. R. (1979) Proc. R. Soc. London Ser. B 205,
489-512.[277][ISI][278][Medline]
47. Gould, S. J. & Lewontin, R. C. (1979) Proc. R. Soc. London Ser. B
205, 581-598.[279][ISI][280][Medline]
48. Gillespie, J. H. (1991) The Causes of Molecular Evolution (Oxford
Univ. Press, Oxford).
49. Frank, S. A. & Slatkin, M. (1990) Am. Nat. 136,
244-260.[281][CrossRef][282][ISI]
50. Smith, J. M. (1979) Proc. R. Soc. London Ser. B 205,
475-488.[283][ISI][284][Medline]
51. Smith, J. M. (1982) Evolution and the Theory of Games (Cambridge
Univ. Press, Cambridge, U.K.).
52. Sasaki, A. & Ellner, S. (1995) Evolution (Lawrence, Kans.) 49,
337-350.
53. Sato, K., Ito, Y., Yomo, T. & Kaneko, K. (2003) Proc. Natl. Acad.
Sci. USA 100, 14086-14090.[285][Abstract/Free Full Text]
54. Bogarad, L. D. & Deem, M. W. (1999) Proc. Natl. Acad. Sci. USA 96,
2591-2595.[286][Abstract/Free Full Text]
55. Kauffman, S. & Levin, S. (1987) J. Theor. Biol. 128,
11-45.[287][ISI][288][Medline]
56. Kauffman, S. A. (1993) The Origins of Order (Oxford Univ. Press,
New York).
57. Kauffman, S. A. & MacReady, W. G. (1995) J. Theor. Biol. 173,
427-440.[289][CrossRef][290][ISI][291][Medline]
58. Perelson, A. S. & Macken, C. A. (1995) Proc. Natl. Acad. Sci. USA
92, 9657-9661.[292][Abstract]
59. Deem, M. W. & Lee, H. Y. (2003) Phys. Rev. Lett. 91,
068101.[293][CrossRef][294][Medline]
60. Michod, R. E. & Levin, B. R., eds. (1988) The Evolution of Sex
(Sinauer, Sunderland, MA).
61. Chandrasekhar, S. (1943) Rev. Mod. Phys. 15, 1-89.[295][CrossRef]
62. Aguilar, A., Roemer, G., Debenham, S., Binns, M., Garcelon, D. &
Wayne, R. K. (2004) Proc. Natl. Acad. Sci. USA 101,
3490-3494.[296][Abstract/Free Full Text]
63. Zhu, T. F., Korber, B. T., Nahmias, A. J., Hooper, E., Sharp, P.
M. & Ho, D. D. (1998) Nature 391,
594-597.[297][CrossRef][298][ISI][299][Medline]
64. Gao, F., Bailes, E., Robertson, D. L., Chen, Y. L., Rodenburg, C.
M., Michael, S. F., Cummins, L. B., Arthur, L. O., Peeters, M.,
Shaw, G. M., et al. (1999) Nature 397,
436-441.[300][CrossRef][301][ISI][302][Medline]
65. Lederberg, J., Shope, R. E. & S. C. Oaks, J., eds. (1992) Emerging
Infections: Microbial Threats to Health in the United States
(Natl. Acad. Press, Washington, DC).
66. Levy, D. N., Aldrovandi, G. M., Kutsch, O. & Shaw, G. M. (2004)
Proc. Natl. Acad. Sci. USA 101, 4204-4209.[303][Abstract/Free
Full Text]
67. Tan, T., Bogarad, L. D. & Deem, M. W. (2004) J. Mol. Evol., in
press.
68. Elena, S. F. & Lenski, R. E. (2003) Nat. Rev. Genet. 4,
457-469.[304][CrossRef][305][ISI][306][Medline]
69. Reboud, X. & Bell, G. (1997) Heredity 78,
507-514.[307][CrossRef][308][ISI]
70. Bennett, A. F. & Lenski, R. E. (1993) Evolution (Lawrence, Kans.)
47, 1-12.
71. Leroi, A. M., Lenski, R. E. & Bennett, A. F. (1994) Evolution
(Lawrence, Kans.) 48, 1222-1229.
References
188. mailto:mwdeem at chinook.rice.edu
198.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=95/15/8420
199.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=anny&resid=870/1/146
200.
http://www.pnas.org/cgi/ijlink?linkType=FULL&journalCode=sci&resid=292/5523/1824
201.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/35035173&link_type=DOI
202.
http://www.pnas.org/cgi/external_ref?access_num=000089727400025&link_type=ISI
203.
http://www.pnas.org/cgi/external_ref?access_num=11028981&link_type=MED
204.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=94/15/7704
205.
http://www.pnas.org/cgi/external_ref?access_num=10.1016/S0168-9525(97)01058-5&link_type=DOI
206.
http://www.pnas.org/cgi/external_ref?access_num=A1997WM03900006&link_type=ISI
207.
http://www.pnas.org/cgi/external_ref?access_num=9066268&link_type=MED
208.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=sci&resid=281/5385/1986
209.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=97/13/7002
210.
http://www.pnas.org/cgi/external_ref?access_num=10.1023/A:1021207310080&link_type=DOI
211.
http://www.pnas.org/cgi/external_ref?access_num=000179517900014&link_type=ISI
212.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=anny&resid=981/1/111
213.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/88673&link_type=DOI
214.
http://www.pnas.org/cgi/external_ref?access_num=000169078000009&link_type=ISI
215.
http://www.pnas.org/cgi/external_ref?access_num=11376332&link_type=MED
216.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=100/12/7152
217.
http://www.pnas.org/cgi/external_ref?access_num=10.1511/2003.3.234&link_type=DOI
218.
http://www.pnas.org/cgi/external_ref?access_num=000182420800020&link_type=ISI
219.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=molbiolevol&resid=14/6/637
220.
http://www.pnas.org/cgi/ijlink?linkType=FULL&journalCode=pnas&resid=97/11/5681
221.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/nbt0102-15&link_type=DOI
222.
http://www.pnas.org/cgi/external_ref?access_num=000173031600020&link_type=ISI
223.
http://www.pnas.org/cgi/external_ref?access_num=11753347&link_type=MED
224.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=95/20/11514
225.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/35080556&link_type=DOI
226.
http://www.pnas.org/cgi/external_ref?access_num=000169681600011&link_type=ISI
227.
http://www.pnas.org/cgi/external_ref?access_num=11433357&link_type=MED
228.
http://www.pnas.org/cgi/external_ref?access_num=10.1016/S0303-2647(02)00134-X&link_type=DOI
229.
http://www.pnas.org/cgi/external_ref?access_num=000182602700004&link_type=ISI
230.
http://www.pnas.org/cgi/external_ref?access_num=12689725&link_type=MED
231.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/nature01191&link_type=DOI
232.
http://www.pnas.org/cgi/external_ref?access_num=000179751800044&link_type=ISI
233.
http://www.pnas.org/cgi/external_ref?access_num=12478292&link_type=MED
234.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/nature01568&link_type=DOI
235.
http://www.pnas.org/cgi/external_ref?access_num=000182699600038&link_type=ISI
236.
http://www.pnas.org/cgi/external_ref?access_num=12736677&link_type=MED
237.
http://www.pnas.org/cgi/external_ref?access_num=10.1103/PhysRevE.60.5912&link_type=DOI
238.
http://www.pnas.org/cgi/external_ref?access_num=000083870900035&link_type=ISI
239.
http://www.pnas.org/cgi/external_ref?access_num=10.1098/rspb.2001.1902&link_type=DOI
240.
http://www.pnas.org/cgi/external_ref?access_num=000174837200007&link_type=ISI
241.
http://www.pnas.org/cgi/external_ref?access_num=11916475&link_type=MED
242.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=99/16/10528
243.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/nature01765&link_type=DOI
244.
http://www.pnas.org/cgi/external_ref?access_num=000184454700042&link_type=ISI
245.
http://www.pnas.org/cgi/external_ref?access_num=12891357&link_type=MED
246.
http://www.pnas.org/cgi/external_ref?access_num=A1992JX41200010&link_type=ISI
247.
http://www.pnas.org/cgi/external_ref?access_num=1334920&link_type=MED
248.
http://www.pnas.org/cgi/external_ref?access_num=10.1016/S0966-842X(97)01110-4&link_type=DOI
249.
http://www.pnas.org/cgi/external_ref?access_num=A1997XU80100007&link_type=ISI
250.
http://www.pnas.org/cgi/external_ref?access_num=9294891&link_type=MED
251.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/415644a&link_type=DOI
252.
http://www.pnas.org/cgi/external_ref?access_num=000173709100047&link_type=ISI
253.
http://www.pnas.org/cgi/external_ref?access_num=11832946&link_type=MED
254.
http://www.pnas.org/cgi/ijlink?linkType=FULL&journalCode=sci&resid=281/5380/1131
255.
http://www.pnas.org/cgi/external_ref?access_num=A1978EK65900015&link_type=ISI
256. http://www.pnas.org/cgi/external_ref?access_num=622185&link_type=MED
257.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=94/15/7698
258.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=genetics&resid=156/4/1661
259.
http://www.pnas.org/cgi/external_ref?access_num=10.1146/annurev.genet.36.040202.092802&link_type=DOI
260.
http://www.pnas.org/cgi/external_ref?access_num=000180365100015&link_type=ISI
261.
http://www.pnas.org/cgi/external_ref?access_num=12429698&link_type=MED
262.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=97/13/6981
263.
http://www.pnas.org/cgi/external_ref?access_num=000084951700030&link_type=ISI
264.
http://www.pnas.org/cgi/external_ref?access_num=10710791&link_type=MED
265.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/370389a0&link_type=DOI
266.
http://www.pnas.org/cgi/external_ref?access_num=A1994PA30400061&link_type=ISI
267.
http://www.pnas.org/cgi/external_ref?access_num=8047147&link_type=MED
268.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/34663&link_type=DOI
269.
http://www.pnas.org/cgi/external_ref?access_num=000071484400052&link_type=ISI
270.
http://www.pnas.org/cgi/external_ref?access_num=9440693&link_type=MED
271.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=94/9/4504
272.
http://www.pnas.org/cgi/external_ref?access_num=10.1016/S0958-1669(00)00106-3&link_type=DOI
273.
http://www.pnas.org/cgi/external_ref?access_num=000088685400002&link_type=ISI
274.
http://www.pnas.org/cgi/external_ref?access_num=10975450&link_type=MED
275.
http://www.pnas.org/cgi/external_ref?access_num=A1979HN99900003&link_type=ISI
276. http://www.pnas.org/cgi/external_ref?access_num=42055&link_type=MED
277.
http://www.pnas.org/cgi/external_ref?access_num=A1979HN99900005&link_type=ISI
278. http://www.pnas.org/cgi/external_ref?access_num=42057&link_type=MED
279.
http://www.pnas.org/cgi/external_ref?access_num=A1979HN99900010&link_type=ISI
280. http://www.pnas.org/cgi/external_ref?access_num=42062&link_type=MED
281.
http://www.pnas.org/cgi/external_ref?access_num=10.1086/285094&link_type=DOI
282.
http://www.pnas.org/cgi/external_ref?access_num=A1990EF56200007&link_type=ISI
283.
http://www.pnas.org/cgi/external_ref?access_num=A1979HN99900004&link_type=ISI
284. http://www.pnas.org/cgi/external_ref?access_num=42056&link_type=MED
285.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=100/24/14086
286.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=96/6/2591
287.
http://www.pnas.org/cgi/external_ref?access_num=A1987K078600002&link_type=ISI
288.
http://www.pnas.org/cgi/external_ref?access_num=3431131&link_type=MED
289.
http://www.pnas.org/cgi/external_ref?access_num=10.1006/jtbi.1995.0074&link_type=DOI
290.
http://www.pnas.org/cgi/external_ref?access_num=A1995QU64300009&link_type=ISI
291.
http://www.pnas.org/cgi/external_ref?access_num=7783452&link_type=MED
292.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=92/21/9657
293.
http://www.pnas.org/cgi/external_ref?access_num=10.1103/PhysRevLett.91.068101&link_type=DOI
294.
http://www.pnas.org/cgi/external_ref?access_num=12935112&link_type=MED
295.
http://www.pnas.org/cgi/external_ref?access_num=10.1103/RevModPhys.15.1&link_type=DOI
296.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=101/10/3490
297.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/35400&link_type=DOI
298.
http://www.pnas.org/cgi/external_ref?access_num=000071842300054&link_type=ISI
299.
http://www.pnas.org/cgi/external_ref?access_num=9468138&link_type=MED
300.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/17130&link_type=DOI
301.
http://www.pnas.org/cgi/external_ref?access_num=000078461700049&link_type=ISI
302.
http://www.pnas.org/cgi/external_ref?access_num=9989410&link_type=MED
303.
http://www.pnas.org/cgi/ijlink?linkType=ABST&journalCode=pnas&resid=101/12/4204
304.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/nrg1088&link_type=DOI
305.
http://www.pnas.org/cgi/external_ref?access_num=000183202600015&link_type=ISI
306.
http://www.pnas.org/cgi/external_ref?access_num=12776215&link_type=MED
307.
http://www.pnas.org/cgi/external_ref?access_num=10.1038/sj.hdy.6881210&link_type=DOI
308.
http://www.pnas.org/cgi/external_ref?access_num=A1997XA87600007&link_type=ISI
E-mail me if you have problems getting the referenced articles.
More information about the paleopsych
mailing list