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D. Lansing Taylor

D. Lansing Taylor, Ph.D.
Distinguished Professor and Allegheny Foundation Professor of Computational & Systems Biology
Director, University of Pittsburgh Drug Discovery Institute

Ph.D. in Cell Biology, State University of New York at Albany
Taylor
Contact

Assistant: Maura Sullivan
Phone: (412) 648-9200
E-mail: mas633@pitt.edu

10045 Biomedical Science Tower 3
3501 Fifth Avenue
Pittsburgh, PA 15260

Phone: 412-648-9200
E-mail: dltaylor@pitt.edu
Website:www.upddi.pitt.edu/index.php?page=director-associate-directors-2

Research Summary

My research interests have been rooted in understanding the temporal-spatial dynamics of signaling molecules and proteins in living cells, coupled to defining the mechanisms of fundamental cell functions such as cell division and cell migration. I have always integrated the development of new technologies in fluorescence-based reagents and light microscope imaging in order to improve the ability to define molecular events in cells and tissue models. My interests have evolved from single cell activities to understanding cellular population dynamics, including the biological basis for heterogeneity in response to perturbations such as drug treatments. We are also investigating populations of cancer cell models labeled with a panel of fluorescent probes of pathway nodes, organelle functions and cell health to measure, model and predict outcomes using computational and systems biology methods.

In my role as the Director of the University of Pittsburgh Drug Discovery Institute, I have the goal to assist both academic and commercial collaborators to discover and to develop efficacious and safe therapeutics based on the integration of outstanding science, technology and drug discovery/development methods. Protein-protein interactions are a personal interest, including the development of biosensors of the interactions.

Recent Publications
Uttam S, Stern AM, Furman S, Pullara F, Spagnolo D, Nguyen L, Gough AH, Sevinsky C, Ginty F, Taylor DL, Chennubhotla SC (2020) Spatial domain analysis predicts risk of colorectal cancer recurrence and infers associated tumor microenvironment networks Nature Communications. 11: 3515

Tosun AB, Nguyen L, Ong N, Navolotskaia O, Carter G, Fine JL, Taylor DL, Chennubhotla SC (2017) Histological detection of high-risk benign breast lesions from whole slide images, Medical Image Computing and Computer Assisted Intervention (MICCAI ‘17), Quebec, Canada, September 10-14, 2017, Proceedings, part 2, pp. 1444-152 .

Nguyen L, Tosun AB, Fine JL, Lee AV, Taylor DL, Chennubhotla SC (2017) Spatial statistics for segmenting histological structures in H&E stained tissue images. IEEE Trans Med Imaging. 36(7): 1522-1532
Grants
Project title Proj Start Date Proj End Date Funding Source
Cancer Center Support Grant 08/01/2010 07/31/2016 NIH P30
Chemical Biology Consortium – Task Order 8: Administrative Support 02/24/2011 03/31/2016 NIH
A 3D biomimetic liver sinusoid construct for predicting physiology and toxicity 07/01/2014 06/30/2017 NIH UH3
All Human Microphysical Model of Metastasis Therapy 07/01/14 06/30/17 NIH UH3
Vanderbilt-Pittsburgh Resource for Organotypic Models for Predictive Toxicology 12 /01/2014 11/30/2018 EPA
Measuring the Temporal=Spatial Responses of Dormancy and Drug Resistance in a Human Breast Cancer Metastatic Niche within a Liver-on-a-Chip Microphysiological Platform 09/01/2015 06/30/2016 NIH UH3
Determining Mechanisms of Disease Progression using Quantitative Systems Pharmacology (QSP) 01/01/2015 12/31/2018 PA Dept of Health
Computational Pathology to Aid the Efficiency and Accuracy of Cancer Diagnoses 11/15/2015 05/15/2016 UPMC