4,566 research outputs found
Sensitivity Amplification in the Phosphorylation-Dephosphorylation Cycle: Nonequilibrium steady states, chemical master equation and temporal cooperativity
A new type of cooperativity termed temporal cooperativity [Biophys. Chem. 105
585-593 (2003), Annu. Rev. Phys. Chem. 58 113-142 (2007)], emerges in the
signal transduction module of phosphorylation-dephosphorylation cycle (PdPC).
It utilizes multiple kinetic cycles in time, in contrast to allosteric
cooperativity that utilizes multiple subunits in a protein. In the present
paper, we thoroughly investigate both the deterministic (microscopic) and
stochastic (mesoscopic) models, and focus on the identification of the source
of temporal cooperativity via comparing with allosteric cooperativity.
A thermodynamic analysis confirms again the claim that the chemical
equilibrium state exists if and only if the phosphorylation potential
, in which case the amplification of sensitivity is completely
abolished. Then we provide comprehensive theoretical and numerical analysis
with the first-order and zero-order assumptions in
phosphorylation-dephosphorylation cycle respectively. Furthermore, it is
interestingly found that the underlying mathematics of temporal cooperativity
and allosteric cooperativity are equivalent, and both of them can be expressed
by "dissociation constants", which also characterizes the essential differences
between the simple and ultrasensitive PdPC switches. Nevertheless, the degree
of allosteric cooperativity is restricted by the total number of sites in a
single enzyme molecule which can not be freely regulated, while temporal
cooperativity is only restricted by the total number of molecules of the target
protein which can be regulated in a wide range and gives rise to the
ultrasensitivity phenomenon.Comment: 42 pages, 13 figure
An effective likelihood-free approximate computing method with statistical inferential guarantees
Approximate Bayesian computing is a powerful likelihood-free method that has
grown increasingly popular since early applications in population genetics.
However, complications arise in the theoretical justification for Bayesian
inference conducted from this method with a non-sufficient summary statistic.
In this paper, we seek to re-frame approximate Bayesian computing within a
frequentist context and justify its performance by standards set on the
frequency coverage rate. In doing so, we develop a new computational technique
called approximate confidence distribution computing, yielding theoretical
support for the use of non-sufficient summary statistics in likelihood-free
methods. Furthermore, we demonstrate that approximate confidence distribution
computing extends the scope of approximate Bayesian computing to include
data-dependent priors without damaging the inferential integrity. This
data-dependent prior can be viewed as an initial `distribution estimate' of the
target parameter which is updated with the results of the approximate
confidence distribution computing method. A general strategy for constructing
an appropriate data-dependent prior is also discussed and is shown to often
increase the computing speed while maintaining statistical inferential
guarantees. We supplement the theory with simulation studies illustrating the
benefits of the proposed method, namely the potential for broader applications
and the increased computing speed compared to the standard approximate Bayesian
computing methods
A Context-aware Attention Network for Interactive Question Answering
Neural network based sequence-to-sequence models in an encoder-decoder
framework have been successfully applied to solve Question Answering (QA)
problems, predicting answers from statements and questions. However, almost all
previous models have failed to consider detailed context information and
unknown states under which systems do not have enough information to answer
given questions. These scenarios with incomplete or ambiguous information are
very common in the setting of Interactive Question Answering (IQA). To address
this challenge, we develop a novel model, employing context-dependent
word-level attention for more accurate statement representations and
question-guided sentence-level attention for better context modeling. We also
generate unique IQA datasets to test our model, which will be made publicly
available. Employing these attention mechanisms, our model accurately
understands when it can output an answer or when it requires generating a
supplementary question for additional input depending on different contexts.
When available, user's feedback is encoded and directly applied to update
sentence-level attention to infer an answer. Extensive experiments on QA and
IQA datasets quantitatively demonstrate the effectiveness of our model with
significant improvement over state-of-the-art conventional QA models.Comment: 9 page
Transition magnetic moment of Majorana neutrinos in the SSM
The nonzero vacuum expectative values of sneutrinos induce spontaneously
R-parity and lepton number violation, and generate three tiny Majorana neutrino
masses through the seesaw mechanism in the SSM, which is one of
Supersymmetric extensions beyond Standard Model. Applying effective Lagrangian
method, we study the transition magnetic moment of Majorana neutrinos in the
model here. Under the constraints from neutrino oscillations, we consider the
two possibilities on the neutrino mass spectrum with normal or inverted
ordering.Comment: 20 pages, 2 figures, accepted for publication in JHEP. arXiv admin
note: text overlap with arXiv:1305.4352, arXiv:1304.624
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