Dissection of a complex transcriptional response using genome-wide transcriptional modelling

Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response. The model predicted three dominant transcriptional activity profiles—an early response controlled by NFκB and c-Jun, a delayed response controlled by p53, and a late response related to cell cycle re-entry. The method also identified, with defined confidence limits, the transcriptional targets associated with each activity. Experimental inhibition of NFκB, c-Jun and p53 confirmed that target predictions were accurate. Model predictions directly explained 70% of the 200 most significantly upregulated genes in the DNA-damage response. Genome-wide transcriptional modelling (GWTM) requires no prior knowledge of either transcription factors or their targets. GWTM is an economical and effective method for identifying the main transcriptional activators in a complex response and confidently predicting their targets

IFITM proteins drive type 2 T helper cell differentiation and exacerbate allergic airway inflammation

T cells differentiated more efficiently to Th1, whereas Th2 differentiation was inhibited. Ifitm-family-deficient mice, but not Ifitm3-deficient mice, were less susceptible than WT to induction of allergic airways disease, with a weaker Th2 response and less severe disease and lower Il4 but higher Ifng expression and IL-27 secretion. Thus, the Ifitm family is important in adaptive immunity, influencing Th1/Th2 polarization, and Th2 immunopathology

Fitting ordinary differential equations to short time course data

Ordinary differential equations (ODEs) are widely used to model many systems in physics, chemistry, engineering and biology. Often one wants to compare such equations with observed time course data, and use this to estimate parameters. Surprisingly, practical algorithms for doing this are relatively poorly developed, particularly in comparison with the sophistication of numerical methods for solving both initial and boundary value problems for differential equations, and for locating and analysing bifurcations. A lack of good numerical fitting methods is particularly problematic in the context of systems biology where only a handful of time points may be available. In this paper, we present a survey of existing algorithms and describe the main approaches. We also introduce and evaluate a new efficient technique for estimating ODEs linear in parameters particularly suited to situations where noise levels are high and the number of data points is low. It employs a spline-based collocation scheme and alternates linear least squares minimization steps with repeated estimates of the noise-free values of the variables. This is reminiscent of expectation-maximization methods widely used for problems with nuisance parameters or missing data

Correction of scaling mismatches in oligonucleotide microarray data

Background: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied to microarrays may result in poor scaling of the summarized data which can hamper analytical interpretations. This is especially relevant in a systems biology context, where systematic biases in the signals of particular genes can have severe effects on subsequent analyses. Conventionally it would be necessary to replace the mismatched arrays, but individual time points cannot be rerun and inserted because of experimental variability. It would therefore be necessary to repeat the whole time series experiment, which is both impractical and expensive. Results: We explain how scaling mismatches occur in data summarized by the popular MAS5 (GCOS; Affymetrix) algorithm, and propose a simple recursive algorithm to correct them. Its principle is to identify a set of constant genes and to use this set to rescale the microarray signals. We study the properties of the algorithm using artificially generated data and apply it to experimental data. We show that the set of constant genes it generates can be used to rescale data from other experiments, provided that the underlying system is similar to the original. We also demonstrate, using a simple example, that the method can successfully correct existing imbalancesin the data. Conclusion: The set of constant genes obtained for a given experiment can be applied to other experiments, provided the systems studied are sufficiently similar. This type of rescaling is especially relevant in systems biology applications using microarray data