350 research outputs found
Stochastic modeling of regulation of gene expression by multiple small RNAs
A wealth of new research has highlighted the critical roles of small RNAs
(sRNAs) in diverse processes such as quorum sensing and cellular responses to
stress. The pathways controlling these processes often have a central motif
comprising of a master regulator protein whose expression is controlled by
multiple sRNAs. However, the regulation of stochastic gene expression of a
single target gene by multiple sRNAs is currently not well understood. To
address this issue, we analyze a stochastic model of regulation of gene
expression by multiple sRNAs. For this model, we derive exact analytic results
for the regulated protein distribution including compact expressions for its
mean and variance. The derived results provide novel insights into the roles of
multiple sRNAs in fine-tuning the noise in gene expression. In particular, we
show that, in contrast to regulation by a single sRNA, multiple sRNAs provide a
mechanism for independently controlling the mean and variance of the regulated
protein distribution
Regulation by small RNAs via coupled degradation: mean-field and variational approaches
Regulatory genes called small RNAs (sRNAs) are known to play critical roles
in cellular responses to changing environments. For several sRNAs, regulation
is effected by coupled stoichiometric degradation with messenger RNAs (mRNAs).
The nonlinearity inherent in this regulatory scheme indicates that exact
analytical solutions for the corresponding stochastic models are intractable.
Here, we present a variational approach to analyze a well-studied stochastic
model for regulation by sRNAs via coupled degradation. The proposed approach is
efficient and provides accurate estimates of mean mRNA levels as well as higher
order terms. Results from the variational ansatz are in excellent agreement
with data from stochastic simulations for a wide range of parameters, including
regions of parameter space where mean-field approaches break down. The proposed
approach can be applied to quantitatively model stochastic gene expression in
complex regulatory networks.Comment: 4 pages, 3 figure
Analysis of Classifiers for Prediction of Type II Diabetes Mellitus
Diabetes mellitus is a chronic disease and a health challenge worldwide. According to the International Diabetes Federation, 451 million people across the globe have diabetes, with this number anticipated to rise up to 693 million people by 2045. It has been shown that 80% of the complications arising from type II diabetes can be prevented or delayed by early identification of the people who are at risk. Diabetes is difficult to diagnose in the early stages as its symptoms grow subtly and gradually. In a majority of the cases, the patients remain undiagnosed until they are admitted for a heart attack or begin to lose their sight. This paper analyzes the different classification algorithms based on a patient's health history to aid doctors identify the presence of as well as promote early diagnosis and treatment. The experiments were conducted on Pima Indian Diabetes data set. Various classifiers used include K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Support Vector Machine and Neural Network. Results demonstrate that Random Forests performed well on the data set giving an accuracy of 79.7%. © 2018 IEEE.E
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