Biology subjects

Guitton, Y.

Publications and source records attributed to Guitton, Y..

3 recordsLinked to original sources

bioRxiv2023

PhysioFit: a software to quantify cell growth parameters and extracellular fluxes

SummaryQuantification of growth parameters and extracellular uptake and production fluxes is central in systems and synthetic biology. Fluxes can be estimated using various mathematical models by fitting time-course measurements of the concentration of cells and extracellular substrates and products. A single tool is available to non-computational biologists to calculate extracellular fluxes, but it is hardly interoperable and is limited to a single hard-coded growth model. We present our open-source flux calculation software, PhysioFit, which can be used with any growth model and is interoperable by design. PhysioFit includes some of the most common growth models, and advanced users can implement additional models to calculate extracellular fluxes and other growth parameters for metabolic systems or experimental setups that follow alternative kinetics. PhysioFit can be used as a Python library and offers a graphical user interface for intuitive use by end-users and a command-line interface to streamline integration into existing pipelines. Availability and ImplementationPhysioFit is implemented in Python 3 and was tested on Windows, Unix and MacOS platforms. PhysioFit is also freely available online at https://workflow4metabolomics.org. The source code, the data and the documentation are freely distributed under GPL3 license at https://github.com/MetaSys-LISBP/PhysioFit/ and https://physiofit.readthedocs.io/.

bioinformatics↗

bioRxiv2020

Combining mass spectrometric platforms for lipidome investigation - Application to the characterisation of disruptions in the lipid profile of pig serum upon β agonist treatment

In the last decade, many mass spectrometric fingerprinting methods dedicated to lipidomics have been proposed: either non-targeted approaches, coupled with annotation methods, or targeted strategies, aiming at specifically monitoring a limited number of substances. In a general public health perspective and through a strategy combining non-targeted and targeted lipidomics MS-based approaches, this study aims at identifying disrupted patterns in serum lipidome upon growth promoter treatment in pig and evaluating the relative contributions of the three platforms involved. Pig serum samples collected during an animal experiment involving control and treated animals, whose food had been supplemented with ractopamine, were extracted and characterised using three MS strategies: Non-targeted RP LC-HRMS; the targeted Lipidyzer platform (differential ion mobility associated with shotgun lipidomics) and a homemade LC-HRMS triglyceride platform. The three different platforms showed complementarity insight into lipid characterisation, which, applied to a selected set of samples, enabled highlighting specific lipid profile patterns involving various lipid classes, mainly in relation with cholesterol esters, sphingomyelins, lactosylceramide, phosphatidylcholines and triglycerides. Thanks to the combination of both non-targeted and targeted MS approaches, the exploration of various compartments of the pig serum lipidome could be performed, including commonly characterised lipids (Lipidyzer), triglyceride isomers (Triglyceride platform) -whose accurate analysis was considered an analytical challenge, and unique lipid features (non-targeted LC-HRMS). Thanks to their respective characteristics, the complementarity of the three tools could be demonstrated for public health purposes, with enhanced lipidome coverage, level of characterisation and applicability.

biochemistry↗

bioRxiv2019

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Lack of reliable peak detection impedes automated analysis of large scale GC-MS metabolomics datasets. Performance and outcome of individual peak-picking algorithms can differ widely depending on both algorithmic approach and parameters as well as data acquisition method. Comparing and contrasting between algorithms is thus difficult. Here we present a workflow for improved peak picking (WiPP), a parameter optimising, multi-algorithm peak detection for GC-MS metabolomics. WiPP evaluates the quality of detected peaks using a machine learning-based classification scheme based on seven peak classes. The quality information returned by the classifier for each individual peak is merged with results from different peak detection algorithms to create one final high quality peak set for immediate down stream analysis. Medium and low quality peaks are kept for further inspection. By applying WiPP to standard compound mixes and a complex biological dataset we demonstrate that peak detection is improved through the novel way to assign peak quality, an automated parameter optimisation, and results integration across different embedded peak picking algorithms. Furthermore, our approach can provide an impartial performance comparison of different peak picking algorithms. WiPP is freely available on GitHub (https://github.com/bihealth/WiPP) under MIT licence.

bioinformatics↗