9,461 research outputs found
Stochastic neural network models for gene regulatory networks
Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale gene expression data sets is still in the very early developmental stage. In this paper we present some stochastic models by introducing stochastic processes into neural network models that can describe intermediate regulation for large scale gene networks. Poisson random variables are used to represent chance events in the processes of synthesis and degradation. For expression data with normalized concentrations, exponential or normal random variables are used to realize fluctuations. Using a network with three genes, we show how to use stochastic simulations for studying robustness and stability properties of gene expression patterns under the influence of noise, and how to use stochastic models to predict statistical distributions of expression levels in population of cells. The discussion suggest that stochastic neural network models can give better description of gene regulatory networks and provide criteria for measuring the reasonableness o mathematical models
Modeling and Estimation for Real-Time Microarrays
Microarrays are used for collecting information about a large number of different genomic particles simultaneously. Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes (e.g., DNA fragments) bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In this paper, we study the signal processing aspects of the real-time microarray system design. In particular, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross hybridization. These are important steps toward developing optimal detection algorithms for real-time microarrays, and to understanding their fundamental limitations
Fast and Robust Rank Aggregation against Model Misspecification
In rank aggregation, preferences from different users are summarized into a
total order under the homogeneous data assumption. Thus, model misspecification
arises and rank aggregation methods take some noise models into account.
However, they all rely on certain noise model assumptions and cannot handle
agnostic noises in the real world. In this paper, we propose CoarsenRank, which
rectifies the underlying data distribution directly and aligns it to the
homogeneous data assumption without involving any noise model. To this end, we
define a neighborhood of the data distribution over which Bayesian inference of
CoarsenRank is performed, and therefore the resultant posterior enjoys
robustness against model misspecification. Further, we derive a tractable
closed-form solution for CoarsenRank making it computationally efficient.
Experiments on real-world datasets show that CoarsenRank is fast and robust,
achieving consistent improvement over baseline methods
Individuality and slow dynamics in bacterial growth homeostasis
Microbial growth and division are fundamental processes relevant to many
areas of life science. Of particular interest are homeostasis mechanisms, which
buffer growth and division from accumulating fluctuations over multiple cycles.
These mechanisms operate within single cells, possibly extending over several
division cycles. However, all experimental studies to date have relied on
measurements pooled from many distinct cells. Here, we disentangle long-term
measured traces of individual cells from one another, revealing subtle
differences between temporal and pooled statistics. By analyzing correlations
along up to hundreds of generations, we find that the parameter describing
effective cell-size homeostasis strength varies significantly among cells. At
the same time, we find an invariant cell size which acts as an attractor to all
individual traces, albeit with different effective attractive forces. Despite
the common attractor, each cell maintains a distinct average size over its
finite lifetime with suppressed temporal fluctuations around it, and
equilibration to the global average size is surprisingly slow (> 150 cell
cycles). To demonstrate a possible source of variable homeostasis strength, we
construct a mathematical model relying on intracellular interactions, which
integrates measured properties of cell size with those of highly expressed
proteins. Effective homeostasis strength is then influenced by interactions and
by noise levels, and generally varies among cells. A predictable and measurable
consequence of variable homeostasis strength appears as distinct oscillatory
patterns in cell size and protein content over many generations. We discuss the
implications of our results to understanding mechanisms controlling division in
single cells and their characteristic timescalesComment: In press with PNAS. 50 pages, including supplementary informatio
A Genome-Wide Analysis of Promoter-Mediated Phenotypic Noise in Escherichia coli
Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alon
Gene-network inference by message passing
The inference of gene-regulatory processes from gene-expression data belongs
to the major challenges of computational systems biology. Here we address the
problem from a statistical-physics perspective and develop a message-passing
algorithm which is able to infer sparse, directed and combinatorial regulatory
mechanisms. Using the replica technique, the algorithmic performance can be
characterized analytically for artificially generated data. The algorithm is
applied to genome-wide expression data of baker's yeast under various
environmental conditions. We find clear cases of combinatorial control, and
enrichment in common functional annotations of regulated genes and their
regulators.Comment: Proc. of International Workshop on Statistical-Mechanical Informatics
2007, Kyot
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