Justin specialises in systems biology and the analysis of omics data
Justin's research interests lie within the intersection of metabolism, bioinformatics, and mathematical modelling. He has broad research interests in probabilistic approaches to analyse different types of high-throughput data and their applications for understanding cellular metabolism. Justin is currently working at IDEA Bio, a synthetic biology service, developing computational pipelines and methods to guide rational design for the enhanced production of biologically-based products. Justin completed his PhD in biochemistry with a specialisation in bioinformatics at the University of Ottawa (Canada) in 2025, where he developed Markov chain-based methods to analyse large-scale metabolic flux networks. His MSc, also completed at the University of Ottawa, focused on developing Bayesian methods to identify lipid subclasses from liquid chromatography coupled to tandem mass spectrometry (LC-MS) data. Justin's honours thesis quantified statistical biases of common peak callers on 100 chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) datasets from the ENCODE Consortium, and proposed a statistical recalibration procedure to reduce false positive protein-DNA binding sites.
Key Publications
Chitpin JG and Perkins TJ: A Markov constraint to uniquely identify elementary flux mode weights in unimolecular metabolic networks, Journal of Theoretical Biology, 2023.
Chitpin JG, Surendra A, Nguyen TT, Taylor GP, Xu H, Alecu I, Ortega R, Tomlinson JJ, Crawley AM, McGuinty M, Schlossmacher MG, Saunders-Pullman R, Cuperlovic-Culf M, Bennett AL, Perkins TJ: BATL: Bayesian annotations for targeted lipidomics, Bioinformatics, 2022.
Chitpin JG, Awdeh A, Perkins TJ: RECAP reveals the true statistical significance of ChIP-seq peak calls, Bioinformatics, 2019.