ORCID Profile
0000-0002-6663-8336
Current Organisations
The University of Edinburgh
,
SISSA
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Publisher: Oxford University Press (OUP)
Date: 12-02-2008
DOI: 10.1093/BFGP/ELN011
Abstract: Technological developments in the life sciences have resulted in an ever-accelerating pace of data production. Systems Biology tries to shed light upon these data by building complex models describing the interactions between biological components. However, extracting information from this morass of data requires the use of sophisticated computational techniques. Here, we propose a method suitable to integrate data drawn from quantitative proteomics into a metabolic scaffold and identify the metabolic pathways which are collectively up-regulated or down-regulated. The availability of such a tool is highly desirable as the extracted information could then be taken as a starting point for in-depth analyses, in particular in fields like Synthetic Biology, where datasets need be characterized routinely.
Publisher: Elsevier BV
Date: 2011
Publisher: Portland Press Ltd.
Date: 10-03-2016
DOI: 10.1042/BJ20150536
Abstract: The glutathione/cysteine exporter CydDC maintains redox balance in Escherichia coli. A cydD mutant strain was used to probe the influence of CydDC upon reduced thiol export, gene expression, metabolic perturbations, intracellular pH homoeostasis and tolerance to nitric oxide (NO). Loss of CydDC was found to decrease extracytoplasmic thiol levels, whereas overexpression diminished the cytoplasmic thiol content. Transcriptomic analysis revealed a dramatic up-regulation of protein chaperones, protein degradation (via phenylpropionate henylacetate catabolism), β-oxidation of fatty acids and genes involved in nitrate/nitrite reduction. 1H NMR metabolomics revealed elevated methionine and betaine and diminished acetate and NAD+ in cydD cells, which was consistent with the transcriptomics-based metabolic model. The growth rate and ΔpH, however, were unaffected, although the cydD strain did exhibit sensitivity to the NO-releasing compound NOC-12. These observations are consistent with the hypothesis that the loss of CydDC-mediated reductant export promotes protein misfolding, adaptations to energy metabolism and sensitivity to NO. The addition of both glutathione and cysteine to the medium was found to complement the loss of bd-type cytochrome synthesis in a cydD strain (a key component of the pleiotropic cydDC phenotype), providing the first direct evidence that CydDC substrates are able to restore the correct assembly of this respiratory oxidase. These data provide an insight into the metabolic flexibility of E. coli, highlight the importance of bacterial redox homoeostasis during nitrosative stress, and report for the first time the ability of periplasmic low molecular weight thiols to restore haem incorporation into a cytochrome complex.
Publisher: MIT Press - Journals
Date: 08-2011
DOI: 10.1162/NECO_A_00156
Abstract: We present a variational Bayesian (VB) approach for the state and parameter inference of a state-space model with point-process observations, a physiologically plausible model for signal processing of spike data. We also give the derivation of a variational smoother, as well as an efficient online filtering algorithm, which can also be used to track changes in physiological parameters. The methods are assessed on simulated data, and results are compared to expectation-maximization, as well as Monte Carlo estimation techniques, in order to evaluate the accuracy of the proposed approach. The VB filter is further assessed on a data set of taste-response neural cells, showing that the proposed approach can effectively capture dynamical changes in neural responses in real time.
Publisher: Proceedings of the National Academy of Sciences
Date: 16-07-2012
Abstract: Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of complex underlying processes in conflict, such as diffusion, relocation, heterogeneous escalation, and volatility. Using ideas from statistics, signal processing, and ecology, we provide a predictive framework able to assimilate data and give confidence estimates on the predictions. We demonstrate our methods on the WikiLeaks Afghan War Diary. Our results show that the approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from previous years.
Publisher: Oxford University Press (OUP)
Date: 21-02-2008
DOI: 10.1093/BIOINFORMATICS/BTN066
Abstract: Motivation: A fundamental task in systems biology is the identification of groups of genes that are involved in the cellular response to particular signals. At its simplest level, this often reduces to identifying biological quantities (mRNA abundance, enzyme concentrations, etc.) which are differentially expressed in two different conditions. Popular approaches involve using t-test statistics, based on modelling the data as arising from a mixture distribution. A common assumption of these approaches is that the data are independent and identically distributed however, biological quantities are usually related through a complex (weighted) network of interactions, and often the more pertinent question is which subnetworks are differentially expressed, rather than which genes. Furthermore, in many interesting cases (such as high-throughput proteomics and metabolomics), only very partial observations are available, resulting in the need for efficient imputation techniques. Results: We introduce Mixture Model on Graphs (MMG), a novel probabilistic model to identify differentially expressed submodules of biological networks and pathways. The method can easily incorporate information about weights in the network, is robust against missing data and can be easily generalized to directed networks. We propose an efficient s ling strategy to infer posterior probabilities of differential expression, as well as posterior probabilities over the model parameters. We assess our method on artificial data demonstrating significant improvements over standard mixture model clustering. Analysis of our model results on quantitative high-throughput proteomic data leads to the identification of biologically significant subnetworks, as well as the prediction of the expression level of a number of enzymes, some of which are then verified experimentally. Availability: MATLAB code is available from www.dcs.shef.ac.uk/~guido/software.html Contact: guido@dcs.shef.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Royal Society of Chemistry (RSC)
Date: 2009
DOI: 10.1039/B904729H
Abstract: We present a systems biology approach to study the global metabolic effects of the insertion of synthetic circuits in a cellular chassis. Our approach combines high-throughput proteomics with the MMG probabilistic tool, which integrates the data with the metabolic circuit's topology. We present a theoretical analysis of the foundations of our approach, as well as experimental results on a mutant strain of Escherichia coli where a light-receptor circuit was inserted and coupled with lactose metabolism. Our results show that the systems approach manages to extract meaningful information from the proteomic data that cannot be recovered by naive thresholding of the data. This tool can be used to characterise the relationship between new circuits and chassis in synthetic biology applications.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
Publisher: Elsevier BV
Date: 06-2010
Publisher: American Physiological Society
Date: 15-09-2013
DOI: 10.1152/AJPRENAL.00113.2013
Abstract: Oxygenation defects may contribute to renal disease progression, but the chronology of events is difficult to define in vivo without recourse to invasive methodologies. Blood oxygen level-dependent magnetic resonance imaging (BOLD MRI) provides an attractive alternative, but the R2* signal is physiologically complex. Postacquisition data analysis often relies on manual selection of region(s) of interest. This approach excludes from analysis significant quantities of biological information and is subject to selection bias. We present a semiautomated, anatomically unbiased approach to compartmentalize voxels into two quantitatively related clusters. In control F344 rats, low R2* clustering was located predominantly within the cortex and higher R2* clustering within the medulla (70.96 ± 1.48 vs. 79.00 ± 1.50 3 scans per rat n = 6 P 0.01) consistent anatomically with a cortico-medullary oxygen gradient. An intravenous bolus of acetylcholine caused a transient reduction of the R2* signal in both clustered segments ( P 0.01). This was nitric oxide dependent and temporally distinct from the hemodynamic effects of acetylcholine. Rats were then chronically infused with angiotensin II (60 ng/min) and rescanned 3 days later. Clustering demonstrated a disruption of the cortico-medullary gradient, producing less distinctly segmented mean R2* clusters (71.30 ± 2.00 vs. 72.48 ± 1.27 n = 6 NS). The acetylcholine-induced attenuation of the R2* signal was abolished by chronic angiotensin II infusion, consistent with reduced nitric oxide bioavailability. This global map of oxygenation, defined by clustering in idual voxels on the basis of quantitative nearness, might be more robust in defining deficits in renal oxygenation than the absolute magnitude of R2* in small, manually selected regions of interest defined exclusively by anatomical nearness.
Publisher: Springer International Publishing
Date: 2013
Publisher: Oxford University Press (OUP)
Date: 25-09-2008
DOI: 10.1093/BIOINFORMATICS/BTN499
Abstract: Background: Mixture model on graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins. It is remarkably robust to missing data, a feature particularly important for its use in quantitative proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend. Availability: The original implementation (Matlab) is still available from www.dcs.shef.ac.uk/~guido/ the new implementation is available from wrightlab.group.shef.ac.uk eople_noirel.htm, from CRAN, and has been submitted to BioConductor, Contact: j.noirel@sheffield.ac.uk
Publisher: Informa UK Limited
Date: 10-2016
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Guido Sanguinetti.