Scanning electron micrograph of Mycobacterium tuberculosis, the pathogen that causes TB (CDC).
Scanning electron micrograph of Mycobacterium tuberculosis, the pathogen that causes TB (CDC).


My research interests lie primarily in the modeling of infectious diseases, both on the molecular level (using approaches from computational and systems biology) as well as on the population level (using approaches from epidemiology and biostatistics). I am particularlly interested in the interactions between science, medicine and policy as they relate to improving patient outcomes, especially in low-income, low-resource settings.

I hold a PhD in Applied Mathematics from MIT and a BSc in Mathematics and Computer Science from McGill University. My doctoral work focused on metabolic models of tuberculosis (TB). I have been working in Ted Cohen's group since September 2012. I recently submitted a paper on a joint model of the TB and HIV epidemics in Southern Africa and I am currently working on identifying mixed TB infection in a population.

Between my PhD and my postdoctoral fellowship I worked as a computational biologist at Pfizer, developing methods for interpreting gene expression, genetic and metabolomic data using a large network of known biological relationships. I also spent some time working at the Massachusetts General Hospital and the Clinton Health Access Initiative.

News I have been offered - and accepted - an Assistant Professor position at Simon Fraser University, in the School of Computing Science! I am actively looking for excellent, highly motivated students, so if you are interested in working on any of the topics above please send me an email!


As an applied mathematician I use a variety of mathematical methods to address one of today's most pressing problems: infectious disease epidemics. I am pursuing a cross-disciplinary approach to the study of infectious disease that incorporates computational biology, phylogenetics, epidemiology, and economic game theory.

Computational biology: analyzing metabolic networks, with applications to M. tuberculosis.

My doctoral research involved the modeling of the metabolism of TB. I developed an algorithmic pipeline called MetaMerge which allowed me to reconcile differences in format, nomenclature, and annotation between two models of TB metabolism. MONGOOSE, another doctoral project of mine, is a tool for analyzing metabolic network models in exact arithmetic, resulting in consistent, reproducible predictions.

Epidemiology: compartmental modeling of the tuberculosis-HIV/AIDS co-epidemic.

As part of my postdoctoral research I developed a compartmental model that accounts for the joint natural history of TB and HIV. I calibrated this model to data available for South Africa and used it to investigate the effects of various interventions aimed at reducing the burden of these diseases in order to help policy-makers select the most impactful and cost-effective one among many alternatives.

Phylogenetics: analyzing prevalence and transmission patterns of mixed tuberculosis infection.

I am developing a method for elucidating the genetic forces at play in the TB epidemics in South Africa and South America by using the TB strains' MIRU-VNTR fingerprints. My method integrates uncertainties about the strain composition of a sample and the phylogenetic relationship between the strains into a robust algorithmic framework to identify patients with mixed infections or mutating strains.

Economic game theory: modeling the alignment of incentives between public and private sector.

I am currently pursuing a collaboration examining the economic incentive structures for the interaction between public and private medical sectors in India. Using a principal-agent paradigm and incorporating the stochasticity of the health outcomes allows us to combine a population-level stochastic model of the course of an infectious disease epidemic with a game-theoretic model of dynamic contract negotiation.



An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models (HTML)

Leonid Chindelevitch, Jason Trigg, Aviv Regev, Bonnie Berger.

Nature Communications, 5:4893 (2014)

Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models (HTML)

Jeffrey Eaton, Nicolas Menzies, John Stover, Valentina Cambiano, Leonid Chindelevitch et al.

The Lancet Global Health, Volume 2, Issue 1, January 2014, Pages e23-e34

The potential effects of changing HIV treatment policy on TB outcomes in South Africa: results from three TB-HIV transmission models (HTML)

Carel Pretorius, Nick Menzies, Leonid Chindelevitch, Ted Cohen, Anne Cori et al.

AIDS (2014) January Supplement (1) S25-34


Optimizing a global alignment of protein interaction networks (HTML)

Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie.

Bioinformatics, Volume 29, Issue 21, 2013, Pages 2765-2773


Causal reasoning on biological networks: interpreting transcriptional changes (HTML)

Chindelevitch, Leonid; Ziemek, Daniel; Enayetallah, Ahmed; Randhawa, Ranjit; Sidders, Ben; Brockel, Christoph; Huang, Enoch S.

Bioinformatics, Volume 28, Issue 8, 2012, Pages 1114-1121

MetaMerge: scaling up genome-scale metabolic reconstructions with application to Mycobacterium tuberculosis (PDF)

Chindelevitch, Leonid; Stanley, Sarah; Hung, Deborah; Regev, Aviv; Berger, Bonnie.

Genome Biology, Volume 3, Issue 1, 2012, Page R6

Assessing statistical significance in causal graphs (HTML)

Chindelevitch, Leonid; Loh, Po-Ru; Enayetallah, Ahmed; Berger, Bonnie; Ziemek, Daniel.

BMC Bioinformatics, Volume 3, Issue 1, 2012, Page 35

Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge (HTML)

Huang, Chia-Ling; Lamb, John; Chindelevitch, Leonid; Kostrowicki, Jarek; Guinney, Justin; DeLisi, Charles; Ziemek, Daniel.

BMC Bioinformatics, Volume 3, Issue 1, 2012, Page 46


Causal reasoning on biological networks: Interpreting transcriptional changes (PDF)

Chindelevitch, Leonid; Ziemek, Daniel; Enayetallah, Ahmed; Randhawa, Ranjit; Sidders, Ben; Brockel, Christoph; Huang, Enoch.

Research in Computational Molecular Biology, 2011, Pages 34-37

Metabolic network analysis demystified (PDF)

Chindelevitch, Leonid; Regev, Aviv; Berger, Bonnie.

Research in Computational Molecular Biology, 2011, Pages 31-33


Local optimization for global alignment of protein interaction networks (PDF)

Chindelevitch, Leonid; Liao, Chung-Shou; Berger, Bonnie.

Pacific Symposium on Biocomputing, Volume 15, 2010, Pages 123-132


Inverting the Viterbi algorithm: an abstract framework for structure design (PDF)

Schnall-Levin, Michael; Chindelevitch, Leonid; Berger, Bonnie.

Proceedings of the 25th International Conference on Machine Learning, 2008, Pages 904-911


Error analysis and preconditioning for an enhanced DtN-FE algorithm for exterior scattering problems (HTML)

Chindelevitch, Leonid; Nicholls, David P; Nigam, Nilima.

Journal of Computational and Applied Mathematics, Volume 2004, Issue 2, 2007, Pages 493-504


On the inference of parsimonious indel evolutionary scenarios (HTML)

Chindelevitch, Leonid; Li, Zhentao; Blais, Eric; Blanchette, Mathieu.

Journal of Bioinformatics And Computational Biology, Volume 4, Issue 3, 2006, Pages 721-744


MetaMerge: combining and reconciling metabolic network model reconstructions

MetaMerge is a package for combining different metabolic network models of the same organism by reconciling the differences in annotation, coverage and model specification. We successfully used it to combine two existing metabolic models of TB. MetaMerge is available for download at

MONGOOSE: exact arithmetic-based analysis of metabolic network models

MONGOOSE (MetabOlic Network GrOwth Optimization Solved Exactly) is a package for structural analysis and refinement of constraint-based metabolic networks. Unlike other existing software, MONGOOSE uses exact rational arithmetic, which makes its results certifiably accurate. MONGOOSE is available for download at

MixTB: classifying mixed TB infection (in preparation)

MixTB is a program that takes microsatellite (molecular fingerprinting) data of a collection of TB strains and for each strain containing loci with more than one variant, determines whether it's more likely to be the result of microevolution within the host or multiple infections. We expect to have MixTB freely available to academic researchers in January 2015.

Lab Members

The Lab Members page is a place where you can provide information about people in your lab/office or your professional affiliations. Use it to provide details about their education, research, and to link to their personal pages or LinkedIn profiles.

  • Ben Schmeckles

    Postdoctoral Fellow

    Cancer stem cells and kinetochore biochemistry

  • Emily Smith

    Postdoctoral Fellow


  • Ben Schmeckles

    Postdoctoral Fellow


  • Emily Smith

    Postdoctoral Fellow


  • Ben Schmeckles

    Postdoctoral Fellow


  • Emily Smith

    Postdoctoral Fellow


  • Bender

    Bending Unit

    Bending Stuff

  • Emily Smith

    Postdoctoral Fellow


  • Ben Schmeckles

    Postdoctoral Fellow


  • Emily Smith

    Postdoctoral Fellow


  • Ben Schmeckles

    Postdoctoral Fellow


  • Emily Smith

    Postdoctoral Fellow



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