80 research outputs found
Modularity and anti-modularity in networks with arbitrary degree distribution
Networks describing the interaction of the elements that constitute a complex
system grow and develop via a number of different mechanisms, such as the
addition and deletion of nodes, the addition and deletion of edges, as well as
the duplication or fusion of nodes. While each of these mechanisms can have a
different cause depending on whether the network is biological, technological,
or social, their impact on the network's structure, as well as its local and
global properties, is similar. This allows us to study how each of these
mechanisms affects networks either alone or together with the other processes,
and how they shape the characteristics that have been observed. We study how a
network's growth parameters impact the distribution of edges in the network,
how they affect a network's modularity, and point out that some parameters will
give rise to networks that have the opposite tendency, namely to display
anti-modularity. Within the model we are describing, we can search the space of
possible networks for parameter sets that generate networks that are very
similar to well-known and well-studied examples, such as the brain of a worm,
and the network of interactions of the proteins in baker's yeast.Comment: 23 pages. 13 figures, 1 table. Includes Supplementary tex
Punishment in Public Goods games leads to meta-stable phase transitions and hysteresis
The evolution of cooperation has been a perennial problem in evolutionary
biology because cooperation can be undermined by selfish cheaters who gain an
advantage in the short run, while compromising the long-term viability of the
population. Evolutionary game theory has shown that under certain conditions,
cooperation nonetheless evolves stably, for example if players have the
opportunity to punish cheaters that benefit from a public good yet refuse to
pay into the common pool. However, punishment has remained enigmatic because it
is costly, and difficult to maintain. On the other hand, cooperation emerges
naturally in the Public Goods game if the synergy of the public good (the
factor multiplying the public good investment) is sufficiently high. In terms
of this synergy parameter, the transition from defection to cooperation can be
viewed as a phase transition with the synergy as the critical parameter. We
show here that punishment reduces the critical value at which cooperation
occurs, but also creates the possibility of meta-stable phase transitions,
where populations can "tunnel" into the cooperating phase below the critical
value. At the same time, cooperating populations are unstable even above the
critical value, because a group of defectors that are large enough can
"nucleate" such a transition. We study the mean-field theoretical predictions
via agent-based simulations of finite populations using an evolutionary
approach where the decisions to cooperate or to punish are encoded genetically
in terms of evolvable probabilities. We recover the theoretical predictions and
demonstrate that the population shows hysteresis, as expected in systems that
exhibit super-heating and super-cooling. We conclude that punishment can
stabilize populations of cooperators below the critical point, but it is a
two-edged sword: it can also stabilize defectors above the critical point.Comment: 22 pages, 9 figures. Slight title change, version that appears in
Physical Biolog
Critical properties of complex fitness landscapes
Evolutionary adaptation is the process that increases the fit of a population
to the fitness landscape it inhabits. As a consequence, evolutionary dynamics
is shaped, constrained, and channeled, by that fitness landscape. Much work has
been expended to understand the evolutionary dynamics of adapting populations,
but much less is known about the structure of the landscapes. Here, we study
the global and local structure of complex fitness landscapes of interacting
loci that describe protein folds or sets of interacting genes forming pathways
or modules. We find that in these landscapes, high peaks are more likely to be
found near other high peaks, corroborating Kauffman's "Massif Central"
hypothesis. We study the clusters of peaks as a function of the ruggedness of
the landscape and find that this clustering allows peaks to form interconnected
networks. These networks undergo a percolation phase transition as a function
of minimum peak height, which indicates that evolutionary trajectories that
take no more than two mutations to shift from peak to peak can span the entire
genetic space. These networks have implications for evolution in rugged
landscapes, allowing adaptation to proceed after a local fitness peak has been
ascended.Comment: 7 pages, 6 figures, requires alifex11.sty. To appear in Proceedings
of 12th International Conference on Artificial Lif
Does self-replication imply evolvability?
The most prominent property of life on Earth is its ability to evolve. It is
often taken for granted that self-replication--the characteristic that makes
life possible--implies evolvability, but many examples such as the lack of
evolvability in computer viruses seem to challenge this view. Is evolvability
itself a property that needs to evolve, or is it automatically present within
any chemistry that supports sequences that can evolve in principle? Here, we
study evolvability in the digital life system Avida, where self-replicating
sequences written by hand are used to seed evolutionary experiments. We use 170
self-replicators that we found in a search through 3 billion randomly generated
sequences (at three different sequence lengths) to study the evolvability of
generic rather than hand-designed self-replicators. We find that most can
evolve but some are evolutionarily sterile. From this limited data set we are
led to conclude that evolvability is a likely--but not a guaranteed-- property
of random replicators in a digital chemistry.Comment: 8 pages, 5 figures. To appear in "Advances in Artificial Life":
Proceedings of the 13th European Conference on Artificial Life (ECAL 2015
Evolution of complex modular biological networks
Biological networks have evolved to be highly functional within uncertain
environments while remaining extremely adaptable. One of the main contributors
to the robustness and evolvability of biological networks is believed to be
their modularity of function, with modules defined as sets of genes that are
strongly interconnected but whose function is separable from those of other
modules. Here, we investigate the in silico evolution of modularity and
robustness in complex artificial metabolic networks that encode an increasing
amount of information about their environment while acquiring ubiquitous
features of biological, social, and engineering networks, such as scale-free
edge distribution, small-world property, and fault-tolerance. These networks
evolve in environments that differ in their predictability, and allow us to
study modularity from topological, information-theoretic, and gene-epistatic
points of view using new tools that do not depend on any preconceived notion of
modularity. We find that for our evolved complex networks as well as for the
yeast protein-protein interaction network, synthetic lethal pairs consist
mostly of redundant genes that lie close to each other and therefore within
modules, while knockdown suppressor pairs are farther apart and often straddle
modules, suggesting that knockdown rescue is mediated by alternative pathways
or modules. The combination of network modularity tools together with genetic
interaction data constitutes a powerful approach to study and dissect the role
of modularity in the evolution and function of biological networks.Comment: 28 pages, 10 figures, 8 supplemental figures, and one supplementary
table. Final version to appear in PLoS Comp Bi
Origin of life in a digital microcosm
While all organisms on Earth descend from a common ancestor, there is no
consensus on whether the origin of this ancestral self-replicator was a one-off
event or whether it was only the final survivor of multiple origins. Here we
use the digital evolution system Avida to study the origin of self-replicating
computer programs. By using a computational system, we avoid many of the
uncertainties inherent in any biochemical system of self-replicators (while
running the risk of ignoring a fundamental aspect of biochemistry). We
generated the exhaustive set of minimal-genome self-replicators and analyzed
the network structure of this fitness landscape. We further examined the
evolvability of these self-replicators and found that the evolvability of a
self-replicator is dependent on its genomic architecture. We studied the
differential ability of replicators to take over the population when competed
against each other (akin to a primordial-soup model of biogenesis) and found
that the probability of a self-replicator out-competing the others is not
uniform. Instead, progenitor (most-recent common ancestor) genotypes are
clustered in a small region of the replicator space. Our results demonstrate
how computational systems can be used as test systems for hypotheses concerning
the origin of life.Comment: 20 pages, 7 figures. To appear in special issue of Philosophical
Transactions of the Royal Society A: Re-Conceptualizing the Origins of Life
from a Physical Sciences Perspectiv
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