Warwick Systems Biology Center

Warwick, United Kingdom

Warwick Systems Biology Center

Warwick, United Kingdom
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Polanski K.,Warwick Systems Biology Center | Rhodes J.,Warwick Systems Biology Center | Hill C.,Warwick Systems Biology Center | Zhang P.,Warwick Systems Biology Center | And 9 more authors.
Bioinformatics | Year: 2014

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g. high correlation of expression profiles across multiple time series datasets. However, numbers of up-or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation.Results: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions. © 2013 The Author 2013. Published by Oxford University Press.

Chan Y.-W.,Warwick Systems Biology Center | Millard A.D.,University of Warwick | Wheatley P.J.,University of Warwick | Holmes A.B.,Columbia University | And 7 more authors.
Environmental Microbiology | Year: 2015

Acaryochloris marina is a symbiotic species of cyanobacteria that is capable of utilizing far-red light. We report the characterization of the phages A-HIS1 and A-HIS2, capable of infecting Acaryochloris. Morphological characterization of these phages places them in the family Siphoviridae. However, molecular characterization reveals that they do not show genetic similarity with any known siphoviruses. While the phages do show synteny between each other, the nucleotide identity between the phages is low at 45-67%, suggesting they diverged from each other some time ago. The greatest number of genes shared with another phage (a myovirus infecting marine Synechococcus) was four. Unlike most other cyanophages and in common with the Siphoviridae infecting Synechococcus, no photosynthesis-related genes were found in the genome. CRISPR (clustered regularly interspaced short palindromic repeats) spacers from the host Acaryochloris had partial matches to sequences found within the phages, which is the first time CRISPRs have been reported in a cyanobacterial/cyanophage system. The phages also encode a homologue of the proteobacterial RNase T. The potential function of RNase T in the mark-up or digestion of crRNA hints at a novel mechanism for evading the host CRISPR system. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

Morrissey E.R.,Warwick Systems Biology Center | Juarez M.A.,Warwick Systems Biology Center | Denby K.J.,Warwick Systems Biology Center | Denby K.J.,University of Warwick | Burroughs N.J.,Warwick Systems Biology Center
Bioinformatics | Year: 2010

Motivation: Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology. Results: We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data. © The Author 2010. Published by Oxford University Press. All rights reserved.

Dulong S.,French Institute of Health and Medical Research | Dulong S.,University Paris - Sud | Ballesta A.,Warwick Systems Biology Center | Ballesta A.,Cancer Chronotherapy Unit | And 7 more authors.
Molecular Cancer Therapeutics | Year: 2015

Cancer chronotherapy aims at enhancing tolerability and efficacy of anticancer drugs through their delivery according to circadian clocks. However, mouse and patient data show that lifestyle, sex, genetics, drugs, and cancer can modify both host circadian clocks and metabolism pathways dynamics, and thus the optimal timing of drug administration. The mathematical modeling of chronopharmacology could indeed help moderate optimal timing according to patient-specific determinants. Here, we combine in vitro and in silico methods, in order to characterize the critical molecular pathways that drive the chronopharmacology of irinotecan, a topoisomerase I inhibitor with complex metabolism and known activity against colorectal cancer. Large transcription rhythms moderated drug bioactivation, detoxification, transport, and target in synchronized colorectal cancer cell cultures. These molecular rhythms translated into statistically significant changes in pharmacokinetics and pharmacodynamics according to in vitro circadian drug timing. The top-up of the multiple coordinated chronopharmacology pathways resulted in a four-fold difference in irinotecan-induced apoptosis according to drug timing. Irinotecan cytotoxicity was directly linked to clock gene BMAL1 expression: The least apoptosis resulted from drug exposure near BMAL1 mRNA nadir (P < 0.001), whereas clock silencing through siBMAL1 exposure ablated all the chronopharmacology mechanisms. Mathematical modeling highlighted circadian bioactivation and detoxification as the most critical determinants of irinotecan chronopharmacology. In vitro-in silico systems chronopharmacology is a new powerful methodology for identifying the main mechanisms at work in order to optimize circadian drug delivery. ©2015 AACR.

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