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Review
TRENDSin Ecology & Evolution Vol.17 No.11 November 2002
528
The biology of digital organisms
Claus O. Wilke and Christoph Adami
ray mutations), or random insertions and/or deletions
of single instructions. The rates of explicit mutations
are under the control of the researcher, whereas
implicit mutations cannot typically be controlled.
Digital organisms have been studied for the past
12 years (Box 1), but only recently have evolutionary
experiments with digital organisms reached a level
of sophistication that is comparable to that of
experiments with bacteria or viruses. What kind of
question can be addressed with digital organisms?
Clearly, because digital organisms live in a completely
artificial world, every conclusion from a digital life
experiment is potentially an artifact of the particular
choices of that digital world (Box 2). However, this
apparent weakness of digital biology is, at the same
time, its biggest strength. By comparing results
across wide ranges of parameter settings in the
digital world, as well as with results from biochemical
organisms and from mathematical theories, it is
possible to disentangle general principles from effects
that are peculiar to a particular model organism.
Here, we highlight three distinct topics that have
recently been addressed using digital organisms: the
dynamics of long-term adaptation, the distribution
of epistatic interactions among mutations, and
quasi-species dynamics. Moreover, we discuss two
areas of future research that appear to be promising,
digital life genetics and evolutionary ecology.
Digital organisms are self-replicating computer programs that mutate and evolve.
They can be thought of as a domesticated form of computer virus that lives in,
and adapts to, a controlled environment. Digital organisms provide a unique
opportunity with which to study evolutionary biology in a form of life that shares
no ancestry with carbon-based life forms, and hence to distinguish general
principles of evolution from historical accidents that are particular to biochemical
life. In terms of the complexity of their evolutionary dynamics, digital organisms
can be compared with biochemical viruses and bacteria. Recent studies of digital
organisms have addressed long-term evolutionary adaptation and the growth of
complexity in evolving systems, patterns of epistatic interactions in various
genetic backgrounds, and quasi-species dynamics.
Published online: 03 September 2002
Historically, evolutionary biology has generally
been an observational and theoretical science.
The experimental verification of evolutionary
mechanisms is a challenging undertaking for several
reasons: most organisms have comparatively long
generation times; there are difficulties in determining
important parameters, such as mutation rates or
fitness values; and the large variances inherent in
evolution lead to poor statistical significance in
averages. Here, we highlight recent work on digital
organisms, for which generation times are of the
order of seconds and measurements can be taken
with unprecedented accuracy. Digital organisms are
self-replicating computer programs that live in a
controlled environment. Unlike other computational
approaches to studying evolution (such as genetic
algorithms [1] or numerical simulations) digital
organisms must explicitly create a copy of their own
GENOME
(see Glossary) to reproduce, and no particular
genomic sequence is designated as the target or
optimal sequence. Selection occurs because the
environment in which the digital organisms live
is space limited, that is, with the birth of a new
organism an older one (typically chosen randomly)
is removed from the population. Therefore, those
organisms that produce more offspring replace less
efficient replicators over time.
Mutations occur in digital organisms via explicit
genomic errors (such as point mutations incurred
during the copy process), or as
IMPLICIT MUTATIONS
that
are the result of flawed copy algorithms. For example,
an organism might skip part of its genome during
replication, or replicate part of its genome twice.
Copy mutations occur because the copying of a single
INSTRUCTION
in the genome has a certain probability of
failure that results in a random instruction written
into the daughter genome. Other
EXPLICIT MUTATIONS
are random changes in the genome of the organism
that occur independently of the copy process (cosmic
Long-term adaptation
One of the cornerstones of evolutionary biology is the
influence of mutation and selection on organisms over
long periods (of the order of thousands of generations
or more), because darwinian theory predicts
macroevolution and the emergence of novelty on that
timescale. However, this is also one of the most difficult
aspects to study, because of the long generation time of
most model organisms. Macroevolutionary changes in
biochemical organisms can only be studied through the
history of domesticated animals and plants, or through
the study of fossil data and molecular sequence
similarities among species. However, these are
purely observational approaches and do not allow
manipulation of key parameters of the evolutionary
process. Alternatively, one can study the long-term
evolution of organisms with extremely short
generation times, such as bacteria and viruses,
an approach taken to new lengths by Lenski and
co-workers [2–4]. Yet, even for these microorganisms,
it takes years to propagate populations through many
thousands of generations.
For digital organisms, such a propagation can be
achieved in a matter of days, and it is therefore not
Claus O. Wilke*
Digital Life Laboratory,
MC 136-93, California
Institute of Technology,
Pasadena, CA 91125, USA.
*e-mail:
wilke@caltech.edu
Christoph Adami
Jet Propulsion
Laboratory, MC 126-347,
California Institute of
Technology, Pasadena,
CA 91125, USA.
Review
TRENDSin Ecology & Evolution Vol.17 No.11 November 2002
529
intermittent fashion [7,8]. Brief periods of rapid
increases in fitness are interspersed with long periods
during which the mean fitness of the population
remains constant. Similar observations were made in
long-term experiments with
Escherichia coli
[2], and
are generally to be expected when fitness increases
depend on the occurrence of beneficial mutations
rather than on initial variation in the population.
The growth dynamics of digital organisms are
comparable to those of bacteria in a chemostat.
Yedid and Bell [12] investigated in detail the patterns
of adaptation in digital organisms, and compared
them to classic [13] and more recent [14] theories
of evolving chemostat populations of bacteria.
The traditional picture is that of periodic selection;
that is mutants arise occasionally in an otherwise
homogeneous population and either go to fixation or
disappear quickly [15,16]. At large population sizes or
high mutation rates, however, the assumption of near
homogeneity of the population cannot hold, and the
concurrent existence of several beneficial mutants in
a population (clonal interference) must be considered
[14]. Yedid and Bell varied the mutation rate over
two orders of magnitude. At low mutation rates,
their observations were consistent with periodic
selection. Dominant genotypes were typically direct
descendants of the previously dominant types,
and reached abundances close to 100%. At
intermediate mutation rates, this pattern started to
weaken. Coexistence of advantageous mutants could
be observed occasionally, and dominant abundances
were generally
Box 1. A brief history of digital life
In the mid 1980s, an increasing number of researchers became fascinated with the
idea of self-replicating computer programs. Reports of computer viruses (programs
that could autonomously propagate from one computer to the next) were becoming
more frequent, and certainly inspired the investigation of the ecology of reproducing
computer programs in controlled environments. The earliest such investigations
were in the form of games. In ‘Core War’ [a], human competitors compete against
each other by writing programs in a space-limited environment (the memory of
the computer). One winning strategy was writing self-replicating programs that
ultimately fill up the available space with copies of itself, thus displacing the
competing programs. Mutations did not occur, and the programs therefore did not
evolve. Other early investigations were concerned with questions of the origin of
self replication, rather than with evolution and ecology. Rasmussen et al.[b] studied
the emergence of self-replicating programs in a noisy environment, but did not
observe the evolution of complex programs. Tom Ray (a tropical plant ecologist
by training) was the first to succeed in creating true darwinian evolution of
self-replicating computer programs. In his
TIERRA
world (see Box Glossary) [c],
self-replicating programs had to face random variations in their code, which led to a
rapid diversification of the population of programs, and eventually to a significant
increase in fitness. After many generations, the programs in the population were
replicating much faster than were the hand-written ancestors that had seeded the
initial population. However, this fitness gain was usually obtained by shrinking the
program size. The evolution of complex programs from simple self replicators was
first observed in the
AVIDA
world [d], through the evolution of computational
reactions and pathways (Box 2). Finally, the
AMOEBA
world [e] was developed to
study the emergence of self-replicating programs from nonreplicators.
References
a Dewdney, A. (1984) In the game called Core War hostile programs engage in a battle of
bits.
Sci. Am.
250, 14–21
b Rasmussen, S.
et al.
(1990). The Coreworld: emergence and evolution of cooperative
structures in a computational chemistry.
Physica D
42, 111–134
c Ray, T. (1991) An approach to the synthesis of life. In
Artificial Life II
(Langton, C.G.
et al.,
eds), pp. 371-408, Addison–Wesley
d Adami, C. (1998)
Introduction to Artificial Life
, Springer–Verlag
e Pargellis, A.N. (1996) The spontaneous generation of digital life.
Physica D
91, 86–96
Box Glossary
Amoeba:
a digital life system developed by Andrew Pargellis to study the emergence of life.
Avida:
a particular software platform for digital life research developed at the California Institute
Tierra:
the original digital life environment developed by Tom Ray. Freely available from
95%. At the highest mutation rates,
the populations were highly polymorphic. Dominant
genotypes typically had abundances of
40%, and
were descendants of rare, nondominant genotypes.
Other long-term evolutionary studies with digital
organisms have addressed the growth of complexity
in evolving systems [10], genome differentiation [9], or
the influence of chance and history on adaptation [11].
The latter study re-created an experiment on
bacteria [17] in a digital setting, with essentially
similar results but higher statistical accuracy.
surprising that experiments with digital organisms
have traditionally focused on long-term evolutionary
trends [5–12]. Early observations made in such
experiments showed that evolution unfolds in an
Box 2. Drawbacks of the digital life approach
Epistatic interactions
Understanding epistatic interactions among
mutations is key to many important questions in
evolutionary biology. For example, the mutational
deterministic hypothesis of the origin of sex requires
that the effects of several deleterious mutations are
reinforcing (synergistic) in their effects [18–20],
as opposed to mitigating (antagonistic). Likewise,
Muller’s ratchet can operate at a significantly
reduced speed in the presence of synergistic
interactions, but can be accelerated by antagonistic
interactions [21–23]. However, the effect of epistatic
interactions on the speed of Muller’s ratchet can be
offset, in part, by a continuous distribution of
mutational effects of single mutations [24]. These
examples underline the importance of measuring
epistatic interactions as well as understanding their
The short generation time of digital organisms and the ease and accuracy with which
measurements can be taken make digital life research appealing. However, it also has
its drawbacks, both in comparison to biochemical experiments and to traditional
theoretical studies based on analytic calculations or simple Monte-Carlo simulations.
In contrast to purely experimental studies with bacteria or viruses, research with
digital organisms is restricted to abstract questions about general principles. We
cannot learn anything about the biology of a particular biochemical organism using
digital life. For example, because both transcription and translation are absent in
digital organisms, the evolutionary dynamics specific to these forms of expression
cannot be addressed. Moreover, the design choices that enter the construction of
the digital world potentially influence the outcome of experiments, so care must be
taken to study only those questions for which this influence is expected to be small.
Traditional theoretical or computational studies have the advantage that the
particular effect under investigation can often be described more cleanly: Irrelevant
details can be neglected in analytical calculations and in custom-made computer
simulations, whereas the dynamics of digital organisms often become as messy as
those of their biochemical counterparts. Therefore, the digital organism approach is
also not the method of choice when it is relatively easy to identify which aspects of the
system under study are relevant, and which can be disregarded.
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TRENDSin Ecology & Evolution Vol.17 No.11 November 2002
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interactions between individual pairs of mutations
by comparing the joint effect of two mutations with
the separate effects of each of the two mutations. The
results can be summarized as follows. A statistically
significant prevalence of antagonistic interactions
occurred in the complex genomes, whereas, for the
simple genomes, the average mutational effects of
multiple mutations did not deviate significantly from
the assumption of multiplicative interactions among
mutations. The analysis of single pairs of mutations
revealed the origin of these observations. In complex
genomes, there was a substantial (19%) fraction of
mutations that displayed epistatic interactions,
of which antagonistic interactions were more than
twice as frequent as synergistic interactions. In simple
genomes, however, the antagonistic:synergistic
interaction ratio was roughly equivalent to that in
complex genomes, but the overall fraction of epistatic
interactions between mutations was much lower (<
Box 3. Computational metabolism
Biochemical organisms often obtain large fitness advantages from particular
metabolic pathways. For example, ATP production through respiration has a much
higher yield of ATP molecules than does ATP production through fermentation,
which leaves organisms possessing the respiration pathway at a selective
advantage in many cases [a]. An analogous situation exists in the case of digital
organisms, which execute their programs at variable speeds, thus determining
their reproductive rate. The higher the speed of program execution, the faster the
digital organisms reproduce. The speed of execution, in turn, is determined by
the computational metabolism of an organism.
The computational metabolism is the set of all computational reactions that an
organism performs. Computational reactions occur as follows: digital organisms
can obtain numbers from their environment (these numbers can be compared to
chemicals that are present in the environment of the organisms). With the right
genetic code (equivalent to the sequence coding for an enzyme that catalyses a
particular reaction), organisms can perform computations on these numbers.
Rewarded computations are logical operations, such as bitwise AND (i.e. the logical
operator between numbers) performed on the inputs. The results are then deposited
back into the environment. If such a computation is deemed to be beneficial in the
given environment, the organism experiences an increase in program execution
speed as a result of that computation. Because different computational reactions
can be strung together to create even more beneficial computations, the set of
computational genes can be thought of as forming a computational pathway.
This computational metabolism is key to the evolution of complex organisms.
In the absence of rewarded computational pathways, the only way in which
organisms can increase their fitness is by shrinking their genomes as much as possible,
thus minimizing the time spent copying the genome. This dynamic is then very
similar to the serial transfer experiments of Spiegelman with Q
5%).
Consequently, the absolute amount of excess
antagonistic interactions was not large enough to
bias the overall multiplicative effect of mutations.
There are two conclusions to be drawn from these
findings. First, an apparent lack of epistasis in the
average effect of multiple mutations can in reality
be caused by the cancellation of synergistic and
antagonistic epistasis. Second, if these results are
also representative for biochemical organisms,
then the deterministic hypothesis cannot explain
the origin of sex.
The study of Lenski
et al.
was modeled after an
earlier one that used
E. coli
[25]. The
E. coli
study
focused on a single strain, and included only
225 mutants of that strain. There was no overall trend
towards synergism or antagonism between mutations,
and there were roughly equal amounts of synergistic
and antagonistic interactions in the analysis of single
pairs. The similarity in the results between the
E. coli
and digital organisms studies is striking, and
supports the hypothesis that many aspects of evolving
systems are governed by general principles.
A type of universality in evolving systems also
emerges from an investigation of the correlation
between the average effect of single mutations and the
epistatic interactions among multiple mutations [35]
that used both the digital genomes obtained by Lenski
et al.
[34] and newly derived data from RNA secondary
structure folding. For both data sets, the average effect
of single mutations was positively correlated to the
amount and strength of antagonistic interactions;
that is, genomes that were strongly affected by single
mutations showed an elevated level of antagonistic
interactions and vice versa. This observation is the
result of a geometrical constraint on the distribution
of viable sequences in sequence space [35–37],
and is therefore expected to hold universally.
phage [b]. When
computational pathways are allowed, however, the organisms experience a tradeoff
between the additional instructions spent performing calculations, which decrease
the rate of replication, and the increase in program execution speed obtained from
successful completion of calculations. In general, when computational pathways are
rewarded with a sufficiently high acceleration, the effort of doing the computations
is worth it. In that case, one can observe, over the course of several hundred
generations, a tremendous increase in the complexity of these organisms, resulting
eventually in organisms that can perform up to 50 or more distinct computations.
References
a Pfeiffer, T.
et al.
(2001) Cooperation and competition in the evolution of ATP-producing
pathways.
Science
292, 504–507
b Mills, D.R.
et al.
(1967) An extracellular Darwininan experiment with a self-duplicating
nucleic acid molecule.
Proc. Natl. Acad. Sci. U. S. A.
58, 217–224
relationship to the effects of single mutations in
experimental systems. However, although there is
no shortage of papers that address these issues in
biochemical organisms [25–32], the corresponding
experiments are very hard to perform and the results
are typically of weak statistical significance [33].
Lenski
et al.
[34] studied the influence of genome
complexity on strength and direction of epistatic
interactions in digital organisms. They set up
87 strains that were adapted to an environment
that was conducive to the evolution of complex
COMPUTATIONAL METABOLISMS
(i.e. an environment
that promoted the evolution of organisms with the
capability to perform complex mathematical
functions; Box 3). They also derived a set of 87 strains
with simple genomes by propagating each of the
87 complex strains in a simple environment in which
a computational metabolism was not beneficial,
and in which fitness improvements could only be
achieved via a lowering of generation time. For all
174 resulting strains, they analysed the average
effect of single and multiple (up to ten) mutations,
with a total of between 10
7
and 10
8
separate mutants
per genome. Lenski
et al.
also studied the epistatic
Quasi-species dynamics
The relevance of quasi-species evolution [38] for the
understanding of bacterial and viral dynamics has
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531
been debated for the past 20 years [39,40].
In a nutshell, the quasi-species concept states that
asexual organisms evolve as cohesive groups of
closely related mutants, and that selection acts on
these mutant clouds (quasi-species), rather than on
the individual organisms. To observe quasi-species
effects, the mutation rates must be relatively high,
of the order of one mutation per genome and
generation, which disqualifies most bacterial systems
as models in which evidence of quasi-species
evolution could be observed. The situation is different
in RNA viruses, which evolve at such high mutation
rates [41]. Likewise, digital organisms are expected to
behave as predicted by the quasi-species model at
correspondingly high mutation rates [42].
One of the most spectacular predictions of
quasi-species theory is that a fast replicator will be
less ‘fit’ than a slower replicator with better mutational
support. In other words, a mutational cloud that
contains some very fast replicating individuals and
some slowly replicating ones can be out-competed
by another mutational cloud in which most of the
individuals replicate at a medium speed, and fast
replicators are absent. The theory behind this effect
has been known since 1988 [43], but had negligible
impact on virology research. Recently, it has been
possible to demonstrate this effect in digital organisms
[44]. Wilke
et al.
propagated 40 evolved strains of
organisms for 1000 generations in both low and high
mutation rate environments. This resulted in 40 pairs
of organisms, each pair comprising a strain evolved at
a low mutation rate (the ‘A’ strain) and a strain evolved
at high mutation rate (the ‘B’ strain). Both strains
shared a common ancestor 1000 generations in the
past. Out of the 40 pairs, 36 had an A strain that had
evolved a higher replication rate than had the
B strain. In 12 of these cases, the replication rate of
A exceeded that of B by
inspection reveals that these genomes are surprisingly
well organized, and that they can often be subdivided
into functionally distinct blocks, which deserve to be
called genes. These genes can be discovered as follows:
one systematically replaces each instruction of the
genome, one at a time, with a special null instruction
that has no function. Then, one tests each of the
organisms obtained in that manner for their ability to
replicate, for their speed of replication, and for the
COMPUTATIONAL REACTIONS
they can complete. In that
way, one obtains a map of the parts of the genome that
are essential for replication, the ones that only play a
role in certain computational reactions but are not
vital, and the ones that have no discernible function
(junk genes). This method of mapping out genomes
can then be applied to closely related mutants, for
example to a sequence of successive descendants
taken from an evolving population. The mechanisms
by which evolution proceeds will be revealed in detail
in such a study, and it will be possible to identify, for
instance, whether new computational reactions form
de novo out of junk genes, or rather arise from genes
that code for other, preexisting reactions. At the time
of writing, no such study has been published, although
the necessary tools are available.
Evolutionary ecology
Work on digital organisms to date has focused on
single-niche systems, in which the organisms interact
only indirectly through their difference in speed of
replication. The computational metabolisms are not
affected by the presence of competitors that perform
either similar or different computations, implying an
absence of frequency-dependent selection. An obvious
direction for future work is to create ecological
interactions by coupling the efficiency of the
computational metabolism of the organisms to the
presence or absence of external resources [45],
in the following manner: to each computation, assign
a necessary resource. An organism performing a
computation (a mathematical reaction) increases its
execution speed by depleting the associated resource.
If the resource is abundant, the organism can reap
large benefits, whereas if the resource is scarce,
that particular reaction is not advantageous to the
organism. Moreover, it is possible to add resource
conversion, so that when a certain resource is
depleted on completion of a particular reaction,
another one is incremented. These resource
dynamics, perhaps combined with restrictions of
reactions (e.g. organisms performing reaction A
cannot perform reaction B, or organisms can perform
reaction B only after they have already completed
reaction A), should lead to multi-niche systems with
rich ecological interactions among digital organisms,
including frequency-dependent selection.
50%. The A and B strains of
these 12 pairs subsequently competed with each other
in environments with different mutation rates.
Without exception, the competition experiments
were won by A strains at low mutation rates and by
B strains at high mutation rates, notwithstanding
their replicative disadvantage. The crucial mutation
rate at which the experimental outcome would switch
in B’s favor could be predicted accurately from
quasi-species theory. Such experiments imply that
mutational robustness plays an increasing role in the
expectation of long-term evolutionary success as the
mutation rate increases. Because of the general
nature of the results, it is expected that the same effect
can be observed with biochemical organisms as long as
the relevant experimental conditions can be created.
Acknowledgments
We thank R.E. Lenski and
C. Ofria for extensive
discussions and
encouragement. This
work was supported by
the National Science
Foundation under
contract No DEB-9981397.
Part of this work was
carried out at the Jet
Propulsion Laboratory,
under a contract with the
National Aeronautics and
Space Administration.
Future research directions
Digital life genetics
To a human eye, the genome of an evolved digital
organism appears to be a random collection of
computer instructions, assembled without any
planning or organization. However, a detailed
Conclusions
Research on digital life forms has reached a level of
sophistication at which questions of biological
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TRENDSin Ecology & Evolution Vol.17 No.11 November 2002
532
that are not [46]. Additionally, digital life can help to
refine mathematical theories and aid in developing
and quickly testing new hypotheses about ecological
and evolutionary processes.
Current state-of-the-art digital life research
platforms create essentially single-niche ecosystems,
and the dynamics that unfold are similar to bacterial
or viral evolution in chemostats. However, more
complex ecosystem structures can be realized within
the paradigm of self-replicating computer programs,
and we can expect research to increase in this area.
Apart from their usefulness as a tool of
understanding evolution in general, it is important
to study the biology of artificial life forms in their
own right. Recently, Lipson and Pollack showed that
the principles of simple self-replicating robots are
within reach of current technology [47]. Eventually,
such robots, and the software that directs them,
might evolve without human interaction, at which
point they would become part of the ecosystem in
which we live.
Glossary
Computational metabolism:
the total set of computational reactions that an organism can do.
Computational reaction:
a computation carried out by an organism on numbers provided by the
environment. To achieve such a reaction, the organisms must possess a computational gene
(code) or pathway for this reaction.
Explicit mutation:
change in the genome between parent and offspring that is caused by noise in
the environment (copy mutation, insertion mutation, deletion mutation, etc.)
Genome:
the program (comprised of instructions arranged in a circular fashion) that an
organism executes.
Instruction:
the basic unit of information in the genome of a digital organism. Each instruction
corresponds typically to one unique action executed by the central processing unit.
For example, the copy instruction copies a single instruction in the genome of the organism,
and the divide instruction initiates the separation of the daughter genome after copying the
genome line by line.
Implicit mutation:
change in the genome between parent and offspring that is caused by a
malfunctioning copy process of the parent organism.
relevance can be both addressed and answered.
The main focus of digital life research is a sort of
comparative biology, which attempts to extricate
those aspects of simple living systems that are
germane to the type of chemistry used, from those
References
1 Foster, J.A. (2001) Evolutionary computation.
Nat. Rev. Genet.
2, 428–436
2 Lenski, R.E. and Travisano, M. (1994) Dynamics of
adaptation and diversification: a 10,000-generation
experiment with bacterial populations.
Proc. Natl.
Acad. Sci. U. S. A.
91, 6808–6814
3 Sniegowski, P.D.
et al.
(1997) Evolution of high
mutation rates in experimental populations of
E. coli
.
Nature
387, 703–705
4 Cooper, V.S. and Lenski, R.E. (2000) The
population genetics of ecological specialization in
evolving
E. coli
populations.
Nature
407, 736–739
5 Ray, T. (1991) An approach to the synthesis of life.
In
Artificial Life II
(Langton, C.G.
et al.
, eds),
pp. 371-408, Addison–Wesley
6 Ray, T.S. (1994) Evolution, complexity, entropy,
and artificial reality.
Physica D
75, 239–263
7 Adami, C. (1995) Self-organized criticality in
living systems.
Phys. Lett.
A 203, 29–32
8 Adami, C. (1998)
Introduction to Artificial Life
,
Springer–Verlag
9 Ofria, C.
et al.
(1999) Evolution of differentiated
expression patterns in digital organisms.
Lect. Notes Artif. Intell.
1674, 129–138
10 Adami, C.
et al.
(2000) Evolution of biological
complexity.
Proc. Natl. Acad. Sci. U.S.A.
97, 4463–4468
11 Wagenaar, D. and Adami, C. (2000) Influence of
chance, history and adaptation on evolution in
digitalia. In
Artificial Life VII (
Bedau, M.A.
et al.
,
eds), pp. 216–220, MIT Press
12 Yedid, G. and Bell, G. (2001) Microevolution in an
electronic microcosm.
Am. Nat.
157, 465–487
13 Atwood, K.C.
et al.
(1951) Selective mechanisms
in bacteria.
Cold Spring Harbor Symp. Quant.
Biol.
16, 146–155
14 Gerrish, P.J. and Lenski, R.E. (1998) The fate of
competing beneficial mutations in an asexual
population.
Genetica
103, 127–144
15 Notley-McRobb, L. and Ferenci, T. (2000)
Experimental analysis of molecular events during
mutational periodic selections in bacterial
evolution.
Genetics
156, 1493–1501
16 Imhof, M. and Schlotterer, C. (2001) Fitness
effects of advantageous mutations in evolving
Escherichia coli
populations.
Proc. Natl. Acad.
Sci. U.S.A.
98, 1113–1117
17 Travisano, M.
et al.
(1995) Experimental test of
the roles of adaptation, chance, and history in
evolution.
Science
267, 87–90
18 Kondrashov, A.S. (1982) Selection against
harmful mutations in large sexual and asexual
populations.
Genet. Res.
40, 325–332
19 Kondrashov, A.S. (1988) Deleterious mutations
and the evolution of sexual reproduction.
Nature
336, 435–440
20 West, S.A.
et al.
(1999) A pluralist approach to sex
and recombination.
J. Evol. Biol.
12, 1003–1012
21 Crow, J.F. and Kimura, M. (1978) Efficiency of
truncation selection.
Proc. Natl. Acad. Sci.
U. S. A.
76, 396–399
22 Kondrashov, A.S. (1994) Muller’s ratchet under
epistatic selection.
Genetics
136, 1469–1473
23 Colato, A. and Fontanari, J.F. (2001) Soluble
model for the accumulation of mutations in
asexual populations.
Phys. Rev. Lett.
87, 238102
24 Butcher, D. (1995) A general model for the
evolution of recombination.
Genet. Res.
65, 123–144
25 Elena, S.F. and Lenski, R.E. (1997) Test of
synergistic interactions among deleterious
mutations in bacteria.
Nature
390, 395–398
26 De Visser, J.A.G.M.
et al.
(1997) An experimental
test for synergistic epistasis and its application to
Chlamydomonas
.
Genetics
145, 815–819
27 De Visser, J.A.G.M. and Hoekstra, R. F. (1998)
Synergistic epistasis between loci affecting fitness:
evidence in plants and fungi.
Genet. Res.
71, 39–49
28 Elena, S.F. (1999) Little evidence for synergism
among deleterious mutations in a nonsegmented
RNA virus.
J. Mol. Evol.
49, 703–707
29 de la Peña, M.
et al.
(2000) Effect of deleterious
mutation accumulation on the fitness of RNA
bacteriophage MS2.
Evolution
54, 686–691
30 Whitlock, M.C. and Bourguet, D. (2000) Factors
affecting the genetic load in
Drosophila
:
synergistic epistasis and correlations among
fitness components.
Evolution
54, 1654–1660
31 Peters, A.D. and Keightley, P.D. (2000) A test for
epistasis among induced mutations in
Caenorhabditis elegans
.
Genetics
156, 1635–1647
32 Wloch, D.M.
et al.
(2001) Epistatic interactions of
spontaneous mutations in haploid strains of the
yeast
Saccharomyces cerevisiae. J. Evol. Biol.
14, 310–316
33 West, S.A.
et al.
(1998) Testing for epistasis between
deleterious mutations.
Genetics
149, 435–444
34 Lenski, R.E.
et al.
(1999) Genome complexity,
robustness, and genetic interactions in digital
organisms.
Nature
400, 661–664
35 Wilke, C.O. and Adami, C. (2001) Interaction
between directional epistasis and average
mutational effects.
Proc. R. Soc. Lond. Ser. B
268, 1469–1474
36 Wagner, G.P.
et al.
(1998) Genetic measurement
theory of epistatic effects.
Genetica
102/103, 569–580
37 Rice, S.H. (1998) The evolution of canalization
and the breaking of von Baer’s law: modeling the
evolution of development with epistasis.
Evolution
52, 647–656
38 Eigen, M. and Schuster, P. (1979)
The Hypercycle –
A Principle of Natural Self-Organization
,
Springer–Verlag
39 Holmes, E.C. and, Moya A. (2002) Is the
quasi-species concept relevant to RNA viruses?
J. Virol.
76, 460–462
40 Domingo, E. (2002) Quasi-species theory in
virology.
J. Virol.
76, 463–465
41 Burch, C.L. and Chao, L. (2000) Evolvability of an
RNA virus is determined by its mutational
neighbourhood.
Nature
406, 625–628
42 Wilke, C.O. (2002) Maternal effects in molecular
evolution.
Phys. Rev. Lett.
88, 078101
43 Schuster, P. and Swetina, J. (1988) Stationary
mutant distributions and evolutionary
optimization.
Bull. Math. Biol.
50, 635–660
44 Wilke, C.O.
et al.
(2001) Evolution of digital
organisms at high mutation rate leads to survival
of the flattest.
Nature
412, 331–333
45 Adami, C. (2002)
Ab initio
modeling of ecosystems
with artificial life.
Nat. Res. Mod.
15, 133–145
46 Maynard Smith, J. (1992) Byte-sized evolution.
Nature
335, 772–773
47 Lipson, H. and Pollack, J.B. (2000) Automatic
design and manufacture of robotic lifeforms.
Nature
406, 974–978
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