48,195 research outputs found
Flexible and robust networks
We consider networks with two types of nodes. The v-nodes, called centers,
are hyper- connected and interact one to another via many u-nodes, called
satellites. This central- ized architecture, widespread in gene networks,
possesses two fundamental properties. Namely, this organization creates
feedback loops that are capable to generate practically any prescribed
patterning dynamics, chaotic or periodic, or having a number of equilib- rium
states. Moreover, this organization is robust with respect to random
perturbations of the system.Comment: Journal of Bioinformatics and Computational Biology, in pres
Synthetic in vitro transcriptional oscillators
The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells
Robust circadian clocks from coupled protein modification and transcription-translation cycles
The cyanobacterium Synechococcus elongatus uses both a protein
phosphorylation cycle and a transcription-translation cycle to generate
circadian rhythms that are highly robust against biochemical noise. We use
stochastic simulations to analyze how these cycles interact to generate stable
rhythms in growing, dividing cells. We find that a protein phosphorylation
cycle by itself is robust when protein turnover is low. For high decay or
dilution rates (and co mpensating synthesis rate), however, the
phosphorylation-based oscillator loses its integrity. Circadian rhythms thus
cannot be generated with a phosphorylation cycle alone when the growth rate,
and consequently the rate of protein dilution, is high enough; in practice, a
purely post-translational clock ceases to function well when the cell doubling
time drops below the 24 hour clock period. At higher growth rates, a
transcription-translation cycle becomes essential for generating robust
circadian rhythms. Interestingly, while a transcription-translation cycle is
necessary to sustain a phosphorylation cycle at high growth rates, a
phosphorylation cycle can dramatically enhance the robustness of a
transcription-translation cycle at lower protein decay or dilution rates. Our
analysis thus predicts that both cycles are required to generate robust
circadian rhythms over the full range of growth conditions.Comment: main text: 7 pages including 5 figures, supplementary information: 13
pages including 9 figure
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Some recent works revealed that deep neural networks (DNNs) are vulnerable to
so-called adversarial attacks where input examples are intentionally perturbed
to fool DNNs. In this work, we revisit the DNN training process that includes
adversarial examples into the training dataset so as to improve DNN's
resilience to adversarial attacks, namely, adversarial training. Our
experiments show that different adversarial strengths, i.e., perturbation
levels of adversarial examples, have different working zones to resist the
attack. Based on the observation, we propose a multi-strength adversarial
training method (MAT) that combines the adversarial training examples with
different adversarial strengths to defend adversarial attacks. Two training
structures - mixed MAT and parallel MAT - are developed to facilitate the
tradeoffs between training time and memory occupation. Our results show that
MAT can substantially minimize the accuracy degradation of deep learning
systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table
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