Aivett Bilbao   Aivett Bilbao Ph.D., conducts research on computational tools for mass spectrometry-based omics, working directly with experimental biologists and chemists in interdisciplinary teams. She has acquired extensive experience developing software for mass spectrometry using multiple programming languages and technologies. Projects include proteomics and metabolomics molecular characterization entailing both algorithm design and software implementation for data from different instruments (time-of-flight and Fourier transform-based mass analyzers) and analytical separation techniques (liquid chromatography, ion mobility, solid-phase extraction, and gas chromatography). She obtained her Ph.D. from the University of Geneva in Switzerland with a special interest in data independent acquisition (DIA). Her first degree is in computer engineering from Universidad de Oriente in Venezuela (Cum Laude) and her M.Sc. studies were focused on machine learning algorithms and statistical methods at Telecom SudParis in France.

Metabolite Profiling for Synthetic Biology using Ion Mobility-Mass Spectrometry and Data-Independent Acquisition with Improved Targeted Data Extraction Software

Combining liquid chromatography, drift-tube ion mobility spectrometry (DTIMS)-mass spectrometry (MS) and data-independent acquisition (DIA) with improved targeted data extraction software, we developed a workflow to enable more effective synthetic biology research of hundreds Aspergillus pseudoterreus strains engineered for production of organic acids of industrial relevance. Data were acquired in two platforms: triple-quadrupole (QQQ) MS in MRM mode and DTIMS-QTOF MS in DIA mode. Read More
A library was created from >50 standards (transitions, retention times, and analyte ion collision-cross sections). Datasets from both platforms were processed using the command-line tool SkylineRunner.exe and customized R scripts to automatically extract chromatograms, generate Skyline projects and optimize peak-integration boundaries across replicates, greatly facilitating the comparison across instruments and multiple conditions. Preliminary results show that 90% of standards were detected at a 50 µM concentration by both platforms. The DTIMS detection increased the number of transitions, enhancing confident identification and accurate quantification in complex matrixes.