Lezon Lab

Computational Systems Pharmacology


Cellular behavior emerges from a complex network of chemical interactions, the details of which remain largely unknown. Developing effective therapies requires a quantitative understanding of these fundamental processes and how they can be safely manipulated to thwart disease. Our lab focuses on constructing mathematical models to assist in diagnosis and treatment of neurodegenerative disease and cancer.

Combination Therapies for Neurodegeneration

Many complex diseases can only be successfully treated with multi-drug therapeutic strategies. Identifying the proper drugs, doses and scheduling for an effective combination therapy is hindered by the sheer number of permissible combinations. Working with other researchers at the University of Pittsburgh Drug Discovery Institute, our lab is developing a combined experimental and computational methodology for streamlining the discovery of combination therapeutics. We are developing novel chemogenomics approaches to uncover pathways that synergistically modulate cell behavior in Alzheimer's disease, Huntington's disease and traumatic brain injury.

Modeling Immunotherapy

An important part of drug discovery is determining safe dose regimens for a heterogeneous patient population. Increasingly, this means personalizing medications and doses to provide optimal effects in individual patients. Working with industry collaborators, the lab is developing mathematical models of the immune response to cancer treatment. Our goal is to quantitatively predict how individual patients will react to a variety of dosing regimens, in order to optimize the efficacy and minimize the side effects of cancer therapy.

Modeling Cellular Heterogeneity

Non-genetic cellular heterogeneity poses a major obstacle to developing effective personalized therapies. In cancer, for example, slight differences determine whether or not a cell will go on to produce a tumor. Our lab investigates the origins of phenotypic cellular heterogeneity and its potential for improving diagnosis of cancer. The lab is refining methods for automatically quantifying heterogeneity and extracting useful biological information from analysis of cellular phenotype distributions. Working with industry partners, we are developing new computational methods and software to exploit distributions of cellular phenotypes and their spatial organization to assist pathology. We also continue to develop methods for predicting the time-evolution of heterogeneous cell populations.

Team Members

Principal Investigator


Jian Cui

Jian Cui, PhD

Predicting druggable pathways in neurodegenerative disease.


Derek Alton

Derek Alton

Pitt Mathematics

Prediction of synergistic drug combinations for treating complex diseases

Feng Guo

Feng Guo

University of Pittsburgh/Tsinghua University student

Prediction of synergistic drug combinations for treating complex diseases

Malcolm Large

Malcolm Large

Pitt Bioinformatics

Inferring druggable pathways in traumatic brain injury

Glenn Mersky

Glenn Mersky

Pitt Mathematics

Prediction of synergistic drug combinations for treating complex diseases

Lab Alumni

Michelle Situ

Undergraduate Researcher (2017-2018)

Cemal Erdem, PhD

CPCB PhD Student (2012-2018)

Employing quantitative systems pharmacology to characterize differences in IGF1 and insulin signaling pathways in breast cancer.

Left for Marc Birtwistle's lab at Clemson.

Stephen Provencher

Undergraduate Researcher (2016-2017)

Left for GlaxoSmithKline

Shana Bergman

Undergraduate Researcher (2015)

Left for Weill Cornell Medical College

Morgan Essex

Undergraduate Researcher (2013)

Left for better things

Ariel Gewirtz

Undergraduate Researcher (2013)

Left for Princeton University

Nicholas Giangreco

Undergraduate Researcher (2012)

Left for Columbia University

Andrew King

Undergraduate Researcher (2012-2013)

Left for University of Pittsburgh Biomedical Informatics


Journal Articles

Book Chapters

  • Schurdak ME, Pei F, Lezon TR, Carlisle D, Friedlander R, Taylor DL, Stern AM. A Quantitative Systems Pharmacology Approach to Infer Pathways Involved in Complex Disease Phenotypes, in Phenotypic Screening. Methods in Molecular Biology, vol 1787. Edited by Wagner B. Humana Press, New York, NY, 2018. PMID: 29736721
  • Gough A, Lezon T, Faeder JR, Chennubhotla C, Murphy RF, Critchley-Thorne R and Taylor DL. High-content analysis with cellular and tissue systems biology: A bridge between cancer cell biology and tissue-based diagnostics, in The Molecular Basis of Cancer. Edited by Mendelsohn J, Gray JW, Howley PM, Israel MA and Thompson CB. Elsevier, 2014.
  • Zomot E, Bakan A, Shrivastava IH, DeChancie J, Lezon TR and Bahar I. Sodium-coupled secondary transporters: insights from structure-based computations, in Molecular Machines. Edited by Roux B. World Scientific, 2011.
  • Lezon TR, Shrivastava IH, Yang Z and Bahar I. Elastic network models for biomolecular dynamics: Theory and application to membrane proteins and viruses, in Handbook on Biological Networks. Edited by Boccaletti S, Latora V and Moreno Y. World Scientific, 2009.
  • Lezon TR, Banavar JR, Cieplak M, Fedoroff NV and Maritan A. The most probable genetic interaction networks inferred from gene expression patterns, in Analysis of Microarray Data: A Network-Based Approach. Edited by Dehmer M and Emmert-Streib F. Wiley, 2008.


Membrane ANM

Check us out on GitHub

Source C code from the paper Constraints imposed by the membrane selectively guide the alternating access dynamics of the glutamate transporter GltPh (Lezon & Bahar.  Biophys J 102:1331 (2012)) can be found on my membrane ANM page. The software is useful for calculating equilibrium dynamics of proteins that are embedded in coarse-grained membranes, and for incorporating rigid blocks into ANM calculations.

For those who are more comfortable working in python, or on a non-nix operating system, these methods are also incorporated into the ProDy software package as membrANM.

Parallel ANM

Source C code from the paper Modeling global changes induced by local perturbations to the HIV-1 capsid. (Bergman & Lezon. J Mol Graphics Modelling 71:218-226 (2017)) can be found on the ANMMPI page. This software is useful for constructing ENMs of very large protein complexes, such as the HIV capsid.


We are located in the University of Pittsburgh Drug Discovery Institute:

W956 Biomedical Science Tower
200 Lothrop Street
Pittsburgh, PA 15261
Phone: 412.383.8042
Email: lezon@pitt.edu