Jeroen Gilis

Postdoctoral researcher

Chalmers University of Technology

Biography

Hi! I am Jeroen Gilis, a postdoctoral researcher at the systems biology division of Chalmers University in Gothenburg, Sweden. My current work revolves around genome-scale metabolic models (GEMs). My main goal is to establish a computational framework that keeps tracks of the different sources of uncertainty in the both the reconstruction of GEMs and in downstream analyses such as flux balance analysis. Furthermore, we aim to also use this framework to inform GEMs with omics data. In addition, I am collaborating on other projects that either require developing novel GEMs, or improving existing GEMs. Prior to this position, I obtained a PhD candidate in data science at Ghent University, Belgium. There, I developed software for differential expression analysis of single-cell transcriptomics data.

I aspire to continue contributing to science at the interface of data science and metabolic engineering, bridging the gap between my background in both data science and biotechnology. My interest lies in leveraging novel advances in machine learning for the rational design of biological entities such as organisms, pathways, proteins or biology-inspired synthetic constructs.

Interests

Machine learning
Metabolic engineering
Data science
R, Python and Julia

Education

PhD candidate in Data Science

Ghent University, Belgium
MSc in Bioinformatics

Ghent University, Belgium
MSc in Biochemistry and Biotechnology

Leuven University, Belgium
BSc in Biochemistry and Biotechnology

Leuven University, Belgium

Experience

Postdoctoral researcher

Chalmers University of Technology

September 2024 – Present Gothenburg, Sweden

Developing a computational framework for that allows for uncertainty quantification and the integration of omics data into genome-scale metabolic models.

Promotor: Docent Eduard Kerkhoven

PhD candidate

Ghent University

November 2018 – May 2024 Ghent, Belgium

Developing software for the analysis of single-cell transcriptomics data.

Promotor 1: Prof. Dr. Lieven Clement
Promotor 2: Prof. Dr. Yvan Saeys
Promotor 3: Dr. Koen Van den Berge

I obtained a personal research grant from Research Foundation Flanders

MSc in bioinformatics

Ghent University

September 2017 – September 2019 Ghent, Belgium

Master thesis topic: development of a novel tool for studying differential transcript usage in single-cell transcriptomics data.

Promotor: Prof. Dr. Lieven Clement

Graduated summa cum laude

MSc and BSc in Biochemistry and Biotechnology

Leuven University

September 2012 – September 2017 Ghent, Belgium

Master thesis topic: Modification of TPS1 for increased acetic acid tolerance in second generation bioethanol fermentations.

Promotor: Prof. Dr. Johan Thevelein

Graduated magna cum laude

Research internship

Beer laboratory Delvaux

June 2016 – February 2016 Ghent, Belgium

Internship research topic: Characterization of phenolic acids and enzyme activity in barley varieties used for beer production.

Promotor: Dr. Flip Delvaux

Publications

Quickly discover relevant content by filtering publications.

Milan Malfait, Jeroen Gilis, Koen Van den Berge, Alemu Takele Assefa, Bie Verbist, Lieven Clement (2024). Strategies for addressing pseudoreplication in multi-patient scRNA-seq data. bioRXiv.

Preprint Cite

Jeroen Gilis, Laura Perin, Milan Malfait, Koen Van den Berge, Alemu Takele Assefa, Bie Verbist, Davide Risso, Lieven Clement (2023). Differential detection workflows for multi-sample single-cell RNA-seq data. bioRXiv.

Preprint Cite Code

Alexandre Segers, Jeroen Gilis, Mattias Van Heetvelde, Elfride De Baere, Lieven Clement (2023). Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. bioRXiv.

Preprint Cite Code

Robin Browaeys, Jeroen Gilis, Chananchida Sang-Aram, Pieter De Bleser, Levi Hoste, Simon J. Tavernier, Diether Lambrechts, Ruth Seurinck, Yvan Saeys (2023). MultiNicheNet; a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics data. bioRXiv.

Preprint Cite Code

Anthony Sonrel, Almut Luetge, Charlotte Soneson, Izaskun Mallona Gonzalez, Pierre-Luc Germain, Sergey Knyazev, Jeroen Gilis, Reto Gerber, Ruth Seurinck, Dominique Paul, Emanuel Sonder, Helena L Crowell, Imran Fanaswala, Ahmad Al Ajami, Elyas Heidari, Stephan Schmeing, Stefan Milosavljevic, Yvan Saeys, Serghei Mangul, Mark D Robinson (2023). Meta-analysis of (single-cell method) benchmarks reveals the need for extensibility and interoperability. Genome Biology.

Preprint PDF Cite Code

See all publications

Software

satuRn (author)

satuRn May 2023

satuRn provides a highly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. satuRn is written in R, and is freely available from the Bioconductor software project.

My contribution: main author

isoformSwitchAnalyzeR (contributor)

isoformSwitchAnalyzeR Jan 2023

R Package for identifying, annotating and visualizing alternative splicing and isoform switches with functional consequences from both short- and long-read RNA-seq data.

My contribution: Added a new functionality to the package to support differential expression tests with satuRn.
Maintainer: Kristoffer Vitting-Seerup

fishpond (contributor)

fishpond Apr 2022

Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files.

My contribution: Added a new functionality to the package to support working with Salmon and Alevin quantification files.
Maintainer: Michael Love

TENxPBMCData (contributor)

TENxPBMCData Dec 2019

Data package that allows for easy access to single-cell RNA-seq data generated with the 10X Genomics technology on PBMC cells.

My contribution: Added new CITE-seq data to the package during the European Bioconductor 2019 Hackathon.
Maintainer: Stephanie Hicks

Teaching

Teaching assistant for the statistics course at Ghent University (2018-2023)

This course is taught to BSc students in chemistry, biochemistry and biomedical sciences. It teaches the fundamentals in statistics, including introductory problems in regression, non-parametric tests and categorical data analysis. I am responsible for tutoring the practical sessions, which involve programming in R for solving real-life case studies to analyze data from biological or chemical experiments.

Instructor for the specialist course on single-cell transcriptomics data analysis at Ghent University (2022)

This course was directed towards PhD researchers, post-docs, university scientists and researchers from the industry. The goal of the course was to provide the participants with an in-depth understanding of the different steps in the analysis of single-cell transcriptomics data.

Co-instructor for Practical Statistics for the Life Sciences course at the Gulbenkian Institute, Portugal (2020 and 2021)

This course was directed towards PhD researchers, typically wet-lab scientist conducting biological experiments. The goal of this course was to provide the participants with the necessary tools to properly design their experiments and to allow them to perform a basic data exploration and analysis for their own data.