1,779 research outputs found
Semi-parametric estimation of the Wilshire creep life prediction model: an application to 2.25Cr-1Mo steel
The Wilshire equation is a recent addition to the literature on safe life prediction. While the effect of temperature on creep life is reasonably understood, the effect of stress isnât. The Wilshire equation deals with this by partitioning over sub ranges of stress, but this approximation can lead to poor life time predictions. This paper introduces a semi-parametric procedure that allows the data itself to identify the stress relationship. When applied to 2.25Cr-1Mo steel it was found that the stress relationship is non-linear, and this semi-parametric version of the Wilshire model had better predictive performance compared to any partitioned Wilshire model. This approach contains a limit to valid extrapolation and the isothermal predictions for creep life have a more realistic pattern of behaviour
Spectral plots and the representation and interpretation of biological data
It is basic question in biology and other fields to identify the char-
acteristic properties that on one hand are shared by structures from a
particular realm, like gene regulation, protein-protein interaction or neu- ral
networks or foodwebs, and that on the other hand distinguish them from other
structures. We introduce and apply a general method, based on the spectrum of
the normalized graph Laplacian, that yields repre- sentations, the spectral
plots, that allow us to find and visualize such properties systematically. We
present such visualizations for a wide range of biological networks and compare
them with those for networks derived from theoretical schemes. The differences
that we find are quite striking and suggest that the search for universal
properties of biological networks should be complemented by an understanding of
more specific features of biological organization principles at different
scales.Comment: 15 pages, 7 figure
Patterns of subnet usage reveal distinct scales of regulation in the transcriptional regulatory network of Escherichia coli
The set of regulatory interactions between genes, mediated by transcription
factors, forms a species' transcriptional regulatory network (TRN). By
comparing this network with measured gene expression data one can identify
functional properties of the TRN and gain general insight into transcriptional
control. We define the subnet of a node as the subgraph consisting of all nodes
topologically downstream of the node, including itself. Using a large set of
microarray expression data of the bacterium Escherichia coli, we find that the
gene expression in different subnets exhibits a structured pattern in response
to environmental changes and genotypic mutation. Subnets with less changes in
their expression pattern have a higher fraction of feed-forward loop motifs and
a lower fraction of small RNA targets within them. Our study implies that the
TRN consists of several scales of regulatory organization: 1) subnets with more
varying gene expression controlled by both transcription factors and
post-transcriptional RNA regulation, and 2) subnets with less varying gene
expression having more feed-forward loops and less post-transcriptional RNA
regulation.Comment: 14 pages, 8 figures, to be published in PLoS Computational Biolog
Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain
Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional
organization of the network as a whole. However, these structural
motifs can only tell one part of the functional story because in this
analysis each node and edge is treated on an equal footing. In real
networks, two motifs that are topologically identical but whose nodes
perform very different functions will play very different roles in the
network.
Methodology/Principal Findings: Here, we combine structural information
derived from the topology of the neuronal network of the nematode C.
elegans with information about the biological function of these nodes,
thus coloring nodes by function. We discover that particular
colorations of motifs are significantly more abundant in the worm brain
than expected by chance, and have particular computational functions
that emphasize the feed-forward structure of information processing in
the network, while evading feedback loops. Interneurons are strongly
over-represented among the common motifs, supporting the notion that
these motifs process and transduce the information from the sensor
neurons towards the muscles. Some of the most common motifs identified
in the search for significant colored motifs play a crucial role in the
system of neurons controlling the worm's locomotion.
Conclusions/Significance: The analysis of complex networks in terms of
colored motifs combines two independent data sets to generate insight
about these networks that cannot be obtained with either data set
alone. The method is general and should allow a decomposition of any
complex networks into its functional (rather than topological) motifs
as long as both wiring and functional information is available
The identification of informative genes from multiple datasets with increasing complexity
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.
Results
In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.
Conclusions
We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events
Signatures of arithmetic simplicity in metabolic network architecture
Metabolic networks perform some of the most fundamental functions in living
cells, including energy transduction and building block biosynthesis. While
these are the best characterized networks in living systems, understanding
their evolutionary history and complex wiring constitutes one of the most
fascinating open questions in biology, intimately related to the enigma of
life's origin itself. Is the evolution of metabolism subject to general
principles, beyond the unpredictable accumulation of multiple historical
accidents? Here we search for such principles by applying to an artificial
chemical universe some of the methodologies developed for the study of genome
scale models of cellular metabolism. In particular, we use metabolic flux
constraint-based models to exhaustively search for artificial chemistry
pathways that can optimally perform an array of elementary metabolic functions.
Despite the simplicity of the model employed, we find that the ensuing pathways
display a surprisingly rich set of properties, including the existence of
autocatalytic cycles and hierarchical modules, the appearance of universally
preferable metabolites and reactions, and a logarithmic trend of pathway length
as a function of input/output molecule size. Some of these properties can be
derived analytically, borrowing methods previously used in cryptography. In
addition, by mapping biochemical networks onto a simplified carbon atom
reaction backbone, we find that several of the properties predicted by the
artificial chemistry model hold for real metabolic networks. These findings
suggest that optimality principles and arithmetic simplicity might lie beneath
some aspects of biochemical complexity
Distributions of epistasis in microbes fit predictions from a fitness landscape model.
How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions
Null Models of Economic Networks: The Case of the World Trade Web
In all empirical-network studies, the observed properties of economic
networks are informative only if compared with a well-defined null model that
can quantitatively predict the behavior of such properties in constrained
graphs. However, predictions of the available null-model methods can be derived
analytically only under assumptions (e.g., sparseness of the network) that are
unrealistic for most economic networks like the World Trade Web (WTW). In this
paper we study the evolution of the WTW using a recently-proposed family of
null network models. The method allows to analytically obtain the expected
value of any network statistic across the ensemble of networks that preserve on
average some local properties, and are otherwise fully random. We compare
expected and observed properties of the WTW in the period 1950-2000, when
either the expected number of trade partners or total country trade is kept
fixed and equal to observed quantities. We show that, in the binary WTW,
node-degree sequences are sufficient to explain higher-order network properties
such as disassortativity and clustering-degree correlation, especially in the
last part of the sample. Conversely, in the weighted WTW, the observed sequence
of total country imports and exports are not sufficient to predict higher-order
patterns of the WTW. We discuss some important implications of these findings
for international-trade models.Comment: 39 pages, 46 figures, 2 table
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