Inferring DNA sequences from mechanical unzipping: an ideal-case study

We introduce and test a method to predict the sequence of DNA molecules from in silico unzipping experiments. The method is based on Bayesian inference and on the Viterbi decoding algorithm. The probability of misprediction decreases exponentially with the number of unzippings, with a decay rate depending on the applied force and the sequence content.Comment: Source as TeX file with ps figure

Reconstructing phylogeny from RNA secondary structure via simulated evolution

DNA sequences of genes encoding functional RNA molecules (e.g., ribosomal RNAs) are commonly used in phylogenetics (i.e. to infer evolutionary history). Trees derived from ribosomal RNA (rRNA) sequences, however, are inconsistent with other molecular data in investigations of deep branches in the tree of life. Since much of te functional constraints on the gene products (i.e. RNA molecules) relate to three-dimensional structure, rather than their actual sequences, accumulated mutations in the gene sequences may obscure phylogenetic signal over very large evolutionary time-scales. Variation in structure, however, may be suitable for phylogenetic inference even under extreme sequence divergence. To evaluate qualitatively the manner in which structural evolution relates to sequence change, we simulated the evolution of RNA sequences under various constraints on structural change

Dynamics of gene expression and the regulatory inference problem

From the response to external stimuli to cell division and death, the dynamics of living cells is based on the expression of specific genes at specific times. The decision when to express a gene is implemented by the binding and unbinding of transcription factor molecules to regulatory DNA. Here, we construct stochastic models of gene expression dynamics and test them on experimental time-series data of messenger-RNA concentrations. The models are used to infer biophysical parameters of gene transcription, including the statistics of transcription factor-DNA binding and the target genes controlled by a given transcription factor.Comment: revised version to appear in Europhys. Lett., new titl

Justifying Inference to the Best Explanation as a Practical Meta-Syllogism on Dialectical Structures

This article discusses how inference to the best explanation (IBE) can be justified as a practical meta-argument. It is, firstly, justified as a *practical* argument insofar as accepting the best explanation as true can be shown to further a specific aim. And because this aim is a discursive one which proponents can rationally pursue in--and relative to--a complex controversy, namely maximising the robustness of one's position, IBE can be conceived, secondly, as a *meta*-argument. My analysis thus bears a certain analogy to Sellars' well-known justification of inductive reasoning (Sellars 1969); it is based on recently developed theories of complex argumentation (Betz 2010, 2011)

Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals

We develop Bayesian inference methods for a recently-emerging type of epigenetic data to study the transmission fidelity of DNA methylation patterns over cell divisions. The data consist of parent-daughter double-stranded DNA methylation patterns with each pattern coming from a single cell and represented as an unordered pair of binary strings. The data are technically difficult and time-consuming to collect, putting a premium on an efficient inference method. Our aim is to estimate rates for the maintenance and de novo methylation events that gave rise to the observed patterns, while accounting for measurement error. We model data at multiple sites jointly, thus using whole-strand information, and considerably reduce confounding between parameters. We also adopt a hierarchical structure that allows for variation in rates across sites without an explosion in the effective number of parameters. Our context-specific priors capture the expected stationarity, or near-stationarity, of the stochastic process that generated the data analyzed here. This expected stationarity is shown to greatly increase the precision of the estimation. Applying our model to a data set collected at the human FMR1 locus, we find that measurement errors, generally ignored in similar studies, occur at a nontrivial rate (inappropriate bisulfite conversion error: 1.6$$ with 80$$ CI: 0.9--2.3$$). Accounting for these errors has a substantial impact on estimates of key biological parameters. The estimated average failure of maintenance rate and daughter de novo rate decline from 0.04 to 0.024 and from 0.14 to 0.07, respectively, when errors are accounted for. Our results also provide evidence that de novo events may occur on both parent and daughter strands: the median parent and daughter de novo rates are 0.08 (80$$ CI: 0.04--0.13) and 0.07 (80$$ CI: 0.04--0.11), respectively.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS297 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inferring rate coefficents of biochemical reactions from noisy data with KInfer

Dynamical models of inter- and intra-cellular processes contain the rate constants of the biochemical reactions. These kinetic parameters are often not accessible directly through experiments, but they can be inferred from time-resolved data. Time resolved data, that is, measurements of reactant concentration at series of time points, are usually affected by different types of error, whose source can be both experimental and biological. The noise in the input data makes the estimation of the model parameters a very difficult task, as if the inference method is not sufficiently robust to the noise, the resulting estimates are not reliable. Therefore "noise-robust" methods that estimate rate constants with the maximum precision and accuracy are needed. In this report we present the probabilistic generative model of parameter inference implemented by the software prototype KInfer and we show the ability of this tool of estimating the rate coefficients of models of biochemical network with a good accuracy even from very noisy input data