30 research outputs found
Are Genetically Robust Regulatory Networks Dynamically Different from Random Ones?
We study a genetic regulatory network model developed to demonstrate that
genetic robustness can evolve through stabilizing selection for optimal
phenotypes. We report preliminary results on whether such selection could
result in a reorganization of the state space of the system. For the chosen
parameters, the evolution moves the system slightly toward the more ordered
part of the phase diagram. We also find that strong memory effects cause the
Derrida annealed approximation to give erroneous predictions about the model's
phase diagram.Comment: To be published in Computer Simulation Studies in Condensed-Matter
Physics XX. Ed. by D.P. Landau, S. P. Lewis, H.-B. Schuttler
(Springer-Verlag, Berlin Heidelberg New York
Perturbation propagation in random and evolved Boolean networks
We investigate the propagation of perturbations in Boolean networks by
evaluating the Derrida plot and modifications of it. We show that even small
Random Boolean Networks agree well with the predictions of the annealed
approximation, but non-random networks show a very different behaviour. We
focus on networks that were evolved for high dynamical robustness. The most
important conclusion is that the simple distinction between frozen, critical
and chaotic networks is no longer useful, since such evolved networks can
display properties of all three types of networks. Furthermore, we evaluate a
simplified empirical network and show how its specific state space properties
are reflected in the modified Derrida plots.Comment: 10 pages, 8 figure
The phase diagram of random threshold networks
Threshold networks are used as models for neural or gene regulatory networks.
They show a rich dynamical behaviour with a transition between a frozen and a
chaotic phase. We investigate the phase diagram of randomly connected threshold
networks with real-valued thresholds h and a fixed number of inputs per node.
The nodes are updated according to the same rules as in a model of the
cell-cycle network of Saccharomyces cereviseae [PNAS 101, 4781 (2004)]. Using
the annealed approximation, we derive expressions for the time evolution of the
proportion of nodes in the "on" and "off" state, and for the sensitivity
. The results are compared with simulations of quenched networks. We
find that for integer values of h the simulations show marked deviations from
the annealed approximation even for large networks. This can be attributed to
the particular choice of the updating rule.Comment: 8 pages, 6 figure
Response of Boolean networks to perturbations
We evaluate the probability that a Boolean network returns to an attractor
after perturbing h nodes. We find that the return probability as function of h
can display a variety of different behaviours, which yields insights into the
state-space structure. In addition to performing computer simulations, we
derive analytical results for several types of Boolean networks, in particular
for Random Boolean Networks. We also apply our method to networks that have
been evolved for robustness to small perturbations, and to a biological
example
Evolution of Canalizing Boolean Networks
Boolean networks with canalizing functions are used to model gene regulatory
networks. In order to learn how such networks may behave under evolutionary
forces, we simulate the evolution of a single Boolean network by means of an
adaptive walk, which allows us to explore the fitness landscape. Mutations
change the connections and the functions of the nodes. Our fitness criterion is
the robustness of the dynamical attractors against small perturbations. We find
that with this fitness criterion the global maximum is always reached and that
there is a huge neutral space of 100% fitness. Furthermore, in spite of having
such a high degree of robustness, the evolved networks still share many
features with "chaotic" networks.Comment: 8 pages, 10 figures; revised and extended versio
Semantic interoperability for an integrated product development process: a systematic literature review
International audienceGlobal competitiveness challenges manufacturing industry to rationalise different ways of bringing to the market new products in a short lead-time with competitive prices while ensuring higher quality levels and customisation. Industries need to effectively share heterogeneous information during Product Development Process (PDP) within and across their institutional boundaries to be competitive. However, problems with misinterpretation and mistakes have been identified during information exchange due to the semantic interoperability obstacles. Thus, this research proposes a systematic literature review to identify the main researches and the milestones reference works on semantic interoperability field. A rigorous methodology was conducted in different databases, covering the articles published in scientific journals from 2005 to 2015 as a preliminary study had indicated that the incidence of articles related to the subject was more frequent from the second half of the 2000s. The research structure consisted of four steps: Survey-searching, analysis and selection of recent researches; Categorization-categorization of the selected papers; References citation frequency analysis-the selected papers were analysed and the main researches and milestones references were identified; and Main researches critical analysis – the main researches were analysed for their contributions and limitations, their contributions and limitations, resulting in 14 selected scientific articles and 8 identified milestones references. It is evident that this field has interesting perspectives on future research opportunities on semantic interoperability of information issues across PDP, contributing to the new concepts of future factories
Prediction of lethal and synthetically lethal knock-outs in regulatory networks
The complex interactions involved in regulation of a cell's function are
captured by its interaction graph. More often than not, detailed knowledge
about enhancing or suppressive regulatory influences and cooperative effects is
lacking and merely the presence or absence of directed interactions is known.
Here we investigate to which extent such reduced information allows to forecast
the effect of a knock-out or a combination of knock-outs. Specifically we ask
in how far the lethality of eliminating nodes may be predicted by their network
centrality, such as degree and betweenness, without knowing the function of the
system. The function is taken as the ability to reproduce a fixed point under a
discrete Boolean dynamics. We investigate two types of stochastically generated
networks: fully random networks and structures grown with a mechanism of node
duplication and subsequent divergence of interactions. On all networks we find
that the out-degree is a good predictor of the lethality of a single node
knock-out. For knock-outs of node pairs, the fraction of successors shared
between the two knocked-out nodes (out-overlap) is a good predictor of
synthetic lethality. Out-degree and out-overlap are locally defined and
computationally simple centrality measures that provide a predictive power
close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at
http://www.bioinf.uni-leipzig.de/publications/supplements/11-01
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
Evolving Sensitivity Balances Boolean Networks
We investigate the sensitivity of Boolean Networks (BNs) to mutations. We are interested in Boolean Networks as a model of Gene Regulatory Networks (GRNs). We adopt Ribeiro and Kauffman’s Ergodic Set and use it to study the long term dynamics of a BN. We define the sensitivity of a BN to be the mean change in its Ergodic Set structure under all possible loss of interaction mutations. Insilico experiments were used to selectively evolve BNs for sensitivity to losing interactions. We find that maximum sensitivity was often achievable and resulted in the BNs becoming topologically balanced, i.e. they evolve towards network structures in which they have a similar number of inhibitory and excitatory interactions. In terms of the dynamics, the dominant sensitivity strategy that evolved was to build BNs with Ergodic Sets dominated by a single long limit cycle which is easily destabilised by mutations. We discuss the relevance of our findings in the context of Stem Cell Differentiation and propose a relationship between pluripotent stem cells and our evolved sensitive networks
Chaotic Gene Regulatory Networks Can Be Robust Against Mutations and Noise
Robustness to mutations and noise has been shown to evolve through
stabilizing selection for optimal phenotypes in model gene regulatory networks.
The ability to evolve robust mutants is known to depend on the network
architecture. How do the dynamical properties and state-space structures of
networks with high and low robustness differ? Does selection operate on the
global dynamical behavior of the networks? What kind of state-space structures
are favored by selection? We provide damage propagation analysis and an
extensive statistical analysis of state spaces of these model networks to show
that the change in their dynamical properties due to stabilizing selection for
optimal phenotypes is minor. Most notably, the networks that are most robust to
both mutations and noise are highly chaotic. Certain properties of chaotic
networks, such as being able to produce large attractor basins, can be useful
for maintaining a stable gene-expression pattern. Our findings indicate that
conventional measures of stability, such as the damage-propagation rate, do not
provide much information about robustness to mutations or noise in model gene
regulatory networks.Comment: JTB accepte