2,881 research outputs found
The effect of scale-free topology on the robustness and evolvability of genetic regulatory networks
We investigate how scale-free (SF) and Erdos-Renyi (ER) topologies affect the
interplay between evolvability and robustness of model gene regulatory networks
with Boolean threshold dynamics. In agreement with Oikonomou and Cluzel (2006)
we find that networks with SFin topologies, that is SF topology for incoming
nodes and ER topology for outgoing nodes, are significantly more evolvable
towards specific oscillatory targets than networks with ER topology for both
incoming and outgoing nodes. Similar results are found for networks with SFboth
and SFout topologies. The functionality of the SFout topology, which most
closely resembles the structure of biological gene networks (Babu et al.,
2004), is compared to the ER topology in further detail through an extension to
multiple target outputs, with either an oscillatory or a non-oscillatory
nature. For multiple oscillatory targets of the same length, the differences
between SFout and ER networks are enhanced, but for non-oscillatory targets
both types of networks show fairly similar evolvability. We find that SF
networks generate oscillations much more easily than ER networks do, and this
may explain why SF networks are more evolvable than ER networks are for
oscillatory phenotypes. In spite of their greater evolvability, we find that
networks with SFout topologies are also more robust to mutations than ER
networks. Furthermore, the SFout topologies are more robust to changes in
initial conditions (environmental robustness). For both topologies, we find
that once a population of networks has reached the target state, further
neutral evolution can lead to an increase in both the mutational robustness and
the environmental robustness to changes in initial conditions.Comment: 16 pages, 15 figure
Degeneracy: a design principle for achieving robustness and evolvability
Robustness, the insensitivity of some of a biological system's
functionalities to a set of distinct conditions, is intimately linked to
fitness. Recent studies suggest that it may also play a vital role in enabling
the evolution of species. Increasing robustness, so is proposed, can lead to
the emergence of evolvability if evolution proceeds over a neutral network that
extends far throughout the fitness landscape. Here, we show that the design
principles used to achieve robustness dramatically influence whether robustness
leads to evolvability. In simulation experiments, we find that purely redundant
systems have remarkably low evolvability while degenerate, i.e. partially
redundant, systems tend to be orders of magnitude more evolvable. Surprisingly,
the magnitude of observed variation in evolvability can neither be explained by
differences in the size nor the topology of the neutral networks. This suggests
that degeneracy, a ubiquitous characteristic in biological systems, may be an
important enabler of natural evolution. More generally, our study provides
valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009
A Publish-Subscribe Model of Genetic Networks
We present a simple model of genetic regulatory networks in which regulatory connections among genes are mediated by a limited number of signaling molecules. Each gene in our model produces (publishes) a single gene product, which regulates the expression of other genes by binding to regulatory regions that correspond (subscribe) to that product. We explore the consequences of this publish-subscribe model of regulation for the properties of single networks and for the evolution of populations of networks. Degree distributions of randomly constructed networks, particularly multimodal in-degree distributions, which depend on the length of the regulatory sequences and the number of possible gene products, differed from simpler Boolean NK models. In simulated evolution of populations of networks, single mutations in regulatory or coding regions resulted in multiple changes in regulatory connections among genes, or alternatively in neutral change that had no effect on phenotype. This resulted in remarkable evolvability in both number and length of attractors, leading to evolved networks far beyond the expectation of these measures based on random distributions. Surprisingly, this rapid evolution was not accompanied by changes in degree distribution; degree distribution in the evolved networks was not substantially different from that of randomly generated networks. The publish-subscribe model also allows exogenous gene products to create an environment, which may be noisy or stable, in which dynamic behavior occurs. In simulations, networks were able to evolve moderate levels of both mutational and environmental robustness
Scaling laws in bacterial genomes: A side-effect of selection of mutational robustness?
In the past few years, numerous research projects have focused on identifying and understanding scaling properties in the gene content of prokaryote genomes and the intricacy of their regulation networks. Yet, and despite the increasing amount of data available, the origins of these scalings remain an open question. The RAevol model, a digital genetics model, provides us with an insight into the mechanisms involved in an evolutionary process. The results we present here show that (i) our model reproduces qualitatively these scaling laws and that (ii) these laws are not due to differences in lifestyles but to differences in the spontaneous rates of mutations and rearrangements. We argue that this is due to an indirect selective pressure for robustness that constrains the genome size
Associative memory in gene regulation networks
The pattern of gene expression in the phenotype of an organism is determined in part by the dynamical attractors of the organismâs gene regulation network. Changes to the connections in this network over evolutionary time alter the adult gene expression pattern and hence the fitness of the organism. However, the evolution of structure in gene expression networks (potentially reflecting past selective environments) and its affordances and limitations with respect to enhancing evolvability is poorly understood in general. In this paper we model the evolution of a gene regulation network in a controlled scenario. We show that selected changes to connections in the regulation network make the currently selected gene expression pattern more robust to environmental variation. Moreover, such changes to connections are necessarily âHebbianâ â âgenes that fire together wire togetherâ â i.e. genes whose expression is selected for in the same selective environments become co-regulated. Accordingly, in a manner formally equivalent to well-understood learning behaviour in artificial neural networks, a gene expression network will therefore develop a generalised associative memory of past selected phenotypes. This theoretical framework helps us to better understand the relationship between homeostasis and evolvability (i.e. selection to reduce variability facilitates structured variability), and shows that, in principle, a gene regulation network has the potential to develop ârecallâ capabilities normally reserved for cognitive systems
Degeneracy: a link between evolvability, robustness and complexity in biological systems
A full accounting of biological robustness remains elusive; both in terms of the mechanisms by which robustness is achieved and the forces that have caused robustness to grow over evolutionary time. Although its importance to topics such as ecosystem services and resilience is well recognized, the broader relationship between robustness and evolution is only starting to be fully appreciated. A renewed interest in this relationship has been prompted by evidence that mutational robustness can play a positive role in the discovery of adaptive innovations (evolvability) and evidence of an intimate relationship between robustness and complexity in biology.
This paper offers a new perspective on the mechanics of evolution and the origins of complexity, robustness, and evolvability. Here we explore the hypothesis that degeneracy, a partial overlap in the functioning of multi-functional components, plays a central role in the evolution and robustness of complex forms. In support of this hypothesis, we present evidence that degeneracy is a fundamental source of robustness, it is intimately tied to multi-scaled complexity, and it establishes conditions that are necessary for system evolvability
The evolutionary origins of hierarchy
Hierarchical organization -- the recursive composition of sub-modules -- is
ubiquitous in biological networks, including neural, metabolic, ecological, and
genetic regulatory networks, and in human-made systems, such as large
organizations and the Internet. To date, most research on hierarchy in networks
has been limited to quantifying this property. However, an open, important
question in evolutionary biology is why hierarchical organization evolves in
the first place. It has recently been shown that modularity evolves because of
the presence of a cost for network connections. Here we investigate whether
such connection costs also tend to cause a hierarchical organization of such
modules. In computational simulations, we find that networks without a
connection cost do not evolve to be hierarchical, even when the task has a
hierarchical structure. However, with a connection cost, networks evolve to be
both modular and hierarchical, and these networks exhibit higher overall
performance and evolvability (i.e. faster adaptation to new environments).
Additional analyses confirm that hierarchy independently improves adaptability
after controlling for modularity. Overall, our results suggest that the same
force--the cost of connections--promotes the evolution of both hierarchy and
modularity, and that these properties are important drivers of network
performance and adaptability. In addition to shedding light on the emergence of
hierarchy across the many domains in which it appears, these findings will also
accelerate future research into evolving more complex, intelligent
computational brains in the fields of artificial intelligence and robotics.Comment: 32 page
Evolution of Robustness and Plasticity under Environmental Fluctuation: Formulation in terms of Phenotypic Variances
The characterization of plasticity, robustness, and evolvability, an
important issue in biology, is studied in terms of phenotypic fluctuations. By
numerically evolving gene regulatory networks, the proportionality between the
phenotypic variances of epigenetic and genetic origins is confirmed. The former
is given by the variance of the phenotypic fluctuation due to noise in the
developmental process; and the latter, by the variance of the phenotypic
fluctuation due to genetic mutation. The relationship suggests a link between
robustness to noise and to mutation, since robustness can be defined by the
sharpness of the distribution of the phenotype. Next, the proportionality
between the variances is demonstrated to also hold over expressions of
different genes (phenotypic traits) when the system acquires robustness through
the evolution. Then, evolution under environmental variation is numerically
investigated and it is found that both the adaptability to a novel environment
and the robustness are made compatible when a certain degree of phenotypic
fluctuations exists due to noise. The highest adaptability is achieved at a
certain noise level at which the gene expression dynamics are near the critical
state to lose the robustness. Based on our results, we revisit Waddington's
canalization and genetic assimilation with regard to the two types of
phenotypic fluctuations.Comment: 23 pages 11 figure
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