12,911 research outputs found
Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays
This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2013 IEEE.This paper is concerned with the gain-constrained recursive filtering problem for a class of time-varying nonlinear stochastic systems with probabilistic sensor delays and correlated noises. The stochastic nonlinearities are described by statistical means that cover the multiplicative stochastic disturbances as a special case. The phenomenon of probabilistic sensor delays is modeled by introducing a diagonal matrix composed of Bernoulli distributed random variables taking values of 1 or 0, which means that the sensors may experience randomly occurring delays with individual delay characteristics. The process noise is finite-step autocorrelated. The purpose of the addressed gain-constrained filtering problem is to design a filter such that, for all probabilistic sensor delays, stochastic nonlinearities, gain constraint as well as correlated noises, the cost function concerning the filtering error is minimized at each sampling instant, where the filter gain satisfies a certain equality constraint. A new recursive filtering algorithm is developed that ensures both the local optimality and the unbiasedness of the designed filter at each sampling instant which achieving the pre-specified filter gain constraint. A simulation example is provided to illustrate the effectiveness of the proposed filter design approach.This work was supported in part by the National Natural Science Foundation of China by Grants 61273156, 61028008, 60825303, 61104125, and 11271103, National 973 Project by Grant 2009CB320600, the Fok Ying Tung Education Fund by Grant 111064, the Special Fund for the Author of National Excellent Doctoral Dissertation of China by Grant 2007B4, the State Key Laboratory of Integrated Automation for the Process Industry (Northeastern University) of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. by Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
Positional information, positional error, and read-out precision in morphogenesis: a mathematical framework
The concept of positional information is central to our understanding of how
cells in a multicellular structure determine their developmental fates.
Nevertheless, positional information has neither been defined mathematically
nor quantified in a principled way. Here we provide an information-theoretic
definition in the context of developmental gene expression patterns and examine
which features of expression patterns increase or decrease positional
information. We connect positional information with the concept of positional
error and develop tools to directly measure information and error from
experimental data. We illustrate our framework for the case of gap gene
expression patterns in the early Drosophila embryo and show how information
that is distributed among only four genes is sufficient to determine
developmental fates with single cell resolution. Our approach can be
generalized to a variety of different model systems; procedures and examples
are discussed in detail
Data based identification and prediction of nonlinear and complex dynamical systems
We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin
Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-depleted Murine Embryonic Stem Cells
Embryonic stem cells (ESC) have the potential to self-renew indefinitely and
to differentiate into any of the three germ layers. The molecular mechanisms
for self-renewal, maintenance of pluripotency and lineage specification are
poorly understood, but recent results point to a key role for epigenetic
mechanisms. In this study, we focus on quantifying the impact of histone 3
acetylation (H3K9,14ac) on gene expression in murine embryonic stem cells. We
analyze genome-wide histone acetylation patterns and gene expression profiles
measured over the first five days of cell differentiation triggered by
silencing Nanog, a key transcription factor in ESC regulation. We explore the
temporal and spatial dynamics of histone acetylation data and its correlation
with gene expression using supervised and unsupervised statistical models. On a
genome-wide scale, changes in acetylation are significantly correlated to
changes in mRNA expression and, surprisingly, this coherence increases over
time. We quantify the predictive power of histone acetylation for gene
expression changes in a balanced cross-validation procedure. In an in-depth
study we focus on genes central to the regulatory network of Mouse ESC,
including those identified in a recent genome-wide RNAi screen and in the
PluriNet, a computationally derived stem cell signature. We find that compared
to the rest of the genome, ESC-specific genes show significantly more
acetylation signal and a much stronger decrease in acetylation over time, which
is often not reflected in an concordant expression change. These results shed
light on the complexity of the relationship between histone acetylation and
gene expression and are a step forward to dissect the multilayer regulatory
mechanisms that determine stem cell fate.Comment: accepted at PLoS Computational Biolog
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