24,359 research outputs found
Evolution of associative learning in chemical networks
Organisms that can learn about their environment and modify their behaviour appropriately during their lifetime are more likely to survive and reproduce than organisms that do not. While associative learning â the ability to detect correlated features of the environment â has been studied extensively in nervous systems, where the underlying mechanisms are reasonably well understood, mechanisms within single cells that could allow associative learning have received little attention. Here, using in silico evolution of chemical networks, we show that there exists a diversity of remarkably simple and plausible chemical solutions to the associative learning problem, the simplest of which uses only one core chemical reaction. We then asked to what extent a linear combination of chemical concentrations in the network could approximate the ideal Bayesian posterior of an environment given the stimulus history so far? This Bayesian analysis revealed the âmemory tracesâ of the chemical network. The implication of this paper is that there is little reason to believe that a lack of suitable phenotypic variation would prevent associative learning from evolving in cell signalling, metabolic, gene regulatory, or a mixture of these networks in cells
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
Traffic flow count data in networks arise in many applications, such as
automobile or aviation transportation, certain directed social network
contexts, and Internet studies. Using an example of Internet browser traffic
flow through site-segments of an international news website, we present
Bayesian analyses of two linked classes of models which, in tandem, allow fast,
scalable and interpretable Bayesian inference. We first develop flexible
state-space models for streaming count data, able to adaptively characterize
and quantify network dynamics efficiently in real-time. We then use these
models as emulators of more structured, time-varying gravity models that allow
formal dissection of network dynamics. This yields interpretable inferences on
traffic flow characteristics, and on dynamics in interactions among network
nodes. Bayesian monitoring theory defines a strategy for sequential model
assessment and adaptation in cases when network flow data deviates from
model-based predictions. Exploratory and sequential monitoring analyses of
evolving traffic on a network of web site-segments in e-commerce demonstrate
the utility of this coupled Bayesian emulation approach to analysis of
streaming network count data.Comment: 29 pages, 16 figure
Probabilistic Meta-Representations Of Neural Networks
Existing Bayesian treatments of neural networks are typically characterized
by weak prior and approximate posterior distributions according to which all
the weights are drawn independently. Here, we consider a richer prior
distribution in which units in the network are represented by latent variables,
and the weights between units are drawn conditionally on the values of the
collection of those variables. This allows rich correlations between related
weights, and can be seen as realizing a function prior with a Bayesian
complexity regularizer ensuring simple solutions. We illustrate the resulting
meta-representations and representations, elucidating the power of this prior.Comment: presented at UAI 2018 Uncertainty In Deep Learning Workshop (UDL AUG.
2018
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
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