25,526 research outputs found
Messenger RNA Fluctuations and Regulatory RNAs Shape the Dynamics of Negative Feedback Loop
Single cell experiments of simple regulatory networks can markedly differ
from cell population experiments. Such differences arise from stochastic events
in individual cells that are averaged out in cell populations. For instance,
while individual cells may show sustained oscillations in the concentrations of
some proteins, such oscillations may appear damped in the population average.
In this paper we investigate the role of RNA stochastic fluctuations as a
leading force to produce a sustained excitatory behavior at the single cell
level. Opposed to some previous models, we build a fully stochastic model of a
negative feedback loop that explicitly takes into account the RNA stochastic
dynamics. We find that messenger RNA random fluctuations can be amplified
during translation and produce sustained pulses of protein expression.
Motivated by the recent appreciation of the importance of non--coding
regulatory RNAs in post--transcription regulation, we also consider the
possibility that a regulatory RNA transcript could bind to the messenger RNA
and repress translation. Our findings show that the regulatory transcript helps
reduce gene expression variability both at the single cell level and at the
cell population level.Comment: 87.18.Vf --> Systems biology 87.10.Mn --> Stochastic models in
biological systems 87.18.Tt --> Noise in biological systems
http://www.ncbi.nlm.nih.gov/pubmed/20365787
http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevE2010.pd
Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
A number of representation schemes have been presented for use within
learning classifier systems, ranging from binary encodings to neural networks.
This paper presents results from an investigation into using discrete and fuzzy
dynamical system representations within the XCSF learning classifier system. In
particular, asynchronous random Boolean networks are used to represent the
traditional condition-action production system rules in the discrete case and
asynchronous fuzzy logic networks in the continuous-valued case. It is shown
possible to use self-adaptive, open-ended evolution to design an ensemble of
such dynamical systems within XCSF to solve a number of well-known test
problems
Mammalian Brain As a Network of Networks
Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD
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