Dr. Chindelevitch’s research programme focuses on the mathematical and computational modeling of antimicrobial resistance in infectious disease, both on the molecular level (using computational and systems biology) as well as on the population level (using epidemiology and population genetics). He is also interested in the application of science to policy towards the goal of improving patient outcomes, especially in low-income or low-resource settings. His methodological research interests focus on computational biology, algorithm development, discrete optimisation, machine learning and artificial intelligence.
Antimicrobial resistance (AMR) prediction
Traditionally carried out through costly and time-consuming methods such as antimicrobial sensitivity testing, the prediction of AMR in bacterial pathogens can now be performed through the analysis of whole-genome sequence data. The challenge is to carry out this prediction in a way that is both accurate (producing few incorrect predictions) and interpretable (identifying specific mutations that drive drug resistance). Dr Chindelevitch’s recent work is focussed on addressing this challenge, with methods involving exact optimization and modular deep neural networks. He is currently involved in a project involving the collection and curation of all publicly available genotype and AMR phenotype data in order to improve the predictive power of such machine learning models.
Strain-level analysis of genomic and metagenomic data
Previous work in Dr Chindelevitch's group has enabled the extraction of single-strain information from whole-genome sequencing data in M. tuberculosis and B. burgdorferi, two important bacterial pathogens, using a deconvolution approach. In collaboration with colleagues in Canada and Mexico, Dr Chindelevitch's group is now extending this work to metagenomic data, where challenges include a low depth of coverage for individual pathogens and the variability of gene content.
Epidemiological relatedness from whole-genome sequences
Another important use of whole-genome sequences is to identify the patients in an outbreak that may be related by a transmission chain. Traditionally, such an analysis may have been carried through a genotyping method that examines a specific set of genes or counts the copy numbers of tandem repeats. Recent developments have enabled a more precise analysis by examining the entire genome. Dr. Chindelevitch’s work integrates these methods into a unified platform to rapidly identify epidemiologically related samples in an outbreak.
Dr. Chindelevitch holds a PhD in Applied Mathematics from MIT (supervised by Bonnie Berger) and a BSc in Mathematics and Computer Science from McGill University. His doctoral work focussed on metabolic models of tuberculosis (TB). Dr. Chindelevitch completed a postdoctoral fellowship in Ted Cohen's group from 2012 to 2015 and was a faculty member at the School of Computing Science at Simon Fraser University from 2015 to 2020, where he received an Alfred P. Sloan Research Fellowship in Computational and Evolutionary Molecular Biology. He joined the Department of Infectious Disease Epidemiology and the MRC Centre for Global Infectious Disease Analysis at Imperial College in 2020.
Dr. Chindelevitch also has experience of working in industry and non-profit organisations. Between his PhD and his postdoctoral fellowship he worked as a computational biologist at Pfizer, where he developed methods for interpreting gene expression, genetic, and metabolomic data using a large network of known biological relationships. He has also worked at the Massachusetts General Hospital and the Clinton Health Access Initiative, and is currently consulting the Foundation for Innovative New Diagnostics on M. tuberculosis, where he co-led the recent effort to catalogue the genomic determinants of drug resistance.
et al., 2023, fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing, Bioinformatics, Vol:39, ISSN:1367-4803
Chindelevitch L, Sedaghat N, Stephen T, 2023, Speeding up the structural analysis of metabolic network models using the Fredman-Khachiyan Algorithm B, Journal of Computational Biology, Vol:30, ISSN:1066-5277
et al., 2023, Ten simple rules for the sharing of bacterial genotype-phenotype data on antimicrobial resistance, Plos Computational Biology, Vol:19, ISSN:1553-734X
et al., 2023, Counting Sorting Scenarios and Intermediate Genomes for the Rank Distance., Ieee/acm Trans Comput Biol Bioinform, Vol:PP
et al., 2023, Generalizations of the genomic rank distance to indels, Bioinformatics, Vol:39, ISSN:1367-4803, Pages:1-10