Russell Schwartz

Department of Biological Sciences and Computational Biology Department
Carnegie Mellon University

Email: russells@andrew.cmu.edu
Phone: (412) 268-3971
Fax: (412) 268-7129
Office: 654B Mellon Institute

First created 11/4/02.
Last updated 5/10/16.

My research interests are in the area of computational molecular biology and the modeling and simulation of biological systems. My group is currently working most actively on three topics: simulation methods for macromolecular assembly systems, with special focus on more realistic models of assembly in cellular environments; methods for analysis of human genetic variation data, most recently focused on phylogenetics and population substructure analysis; and application of phylogenetic methods to study cancer evolution, heterogeneity, and progression.

Teaching

Spring 2016: 02-250/03-250 Introduction to Computational Biology

This is a substantially redesigned class introduction to major computational ideas, tools, and data sets for biological research aimed at biology students with little or no prior computational experience. The class can be taken either as a full-semester version, 02-250/03-250, that covers genomics, modeling, and image informatics, or as either of two half-semester mini-classes, 02-251/03-251 for the genomics material or 02-252/03-252 for modeling and image informatics. Students with prior training in programming, algorithms, and data structures should instead consider 03-511.
Recordings of my lectures from 02-251/03-251 are available in a Youtube playlist based on the 2015 run of the class.

Fall 2015: 02-512/02-712/03-512/03-712 Computational Methods for Biological Modeling and Simulation

This is a class on models, algorithms, and numerical methods used in modeling and simulating biological systems. The class is aimed at graduate students and advanced undergraduates interested in developing new methods and tools for computational biology. It assumes introductory knowledge of biology, algorithms and data structures, and computer programming.

The course notes for this class were converted into a textbook Biological Modeling and Simulation available from the MIT Press.

Past Classes

I previously developed 03-101C, Computational Resources for Biology, a first-year mini class for biology students devoted to hands-on work with on-line tools and databases for genomics. The material of this class was subsumed and expanded to create 03-311.

I also co-developed 02-730, Cell and Systems Modeling, a core class of our n Ph.D. Program in Computational Biology jointly run by the University of Pittsburgh and Carnegie Mellon. I co-taught this class with Joel Stiles and Ivan Maly in Spring 2007 and Spring 2007. It has most recently been run by Jim Faeder and Chris Langmead in a greatly revised form.

I also previously taught 03-311, the non-programming version of Introduction to Computational Biology. This was a 6-unit mini class intended to provide an introduction to major computational ideas, tools, and data sets aimed primarily at Biological Sciences students with little or no prior computational experience. This class was intended as a small-scale pilot for the material comprising the new 02-250/03-250, described above.

Software

CLEAX package for inferring population substructure, history, and admixture events.

HapMotif package for analyzing conserved segments of haploid sequences.

DESSA discrete event self-assembly simulation software, a package for fast simulation of self-assembly systems.

SCIMP web server implementing methods for finding maximum parsimony imperfect phylogenies on haplotype or genotype data.

We are in the process of migrating lab software from our local servers to a lab Github repository. Stay tuned for migrations of older software and future lab software releases.

Selected Recent Publications (past three years): (see my Google Scholar page for a full citation list)

T. Roman, L. Xie, and R. Schwartz. "Medoidshift clustering applied to genomic bulk tumor data" BMC Genomics, 17(S1):6, 2016.
D. Catanzaro, S. Shackney, A. Schäffer, and R. Schwartz. "Classifying the progression of ductal carcinoma from single-cell sampled data via integer linear programming: a case study." IEEE/ACM Transactions on Computational Biology and Bioinformatics, published online before print, 2016.
D. Wangsa, S. A. Chowdhury, M. Ryott, E. M. Gertz, G. Elmberger, G. Auer, E. A. Lundqvist, S. Küffer, P. Ströbel, A. A. Schäffer, R. Schwartz, E. Munck-Wikland, T. Ried, K. Heselmeyer-Haddad. "Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes in associated with poor prognosis." International Journal of Cancer, 138(1), 2016.
A. Subramanian and R. Schwartz. "Reference-free inference of tumor phylogenies from single-cell sequencing data." BMC Genomics, 16S11:S7, 2015.
T. Roman, B. Fasy, A. Nayyeri, and R. Schwartz. "A simplicial complex-based approach to unmixing tumor progression data." BMC Bioinformatics, 16:254, 2015.
SA Chowdhury, E Gertz, D Wangsa, K Heselmeyer-Haddad, T Ried, A Schaffer and R Schwartz. "Inferring models of multiscale copy number evolution for single-tumor phylogenetics." Bioinformatics (special proceedings issue for Intelligent Systems for Molecular Biology), 31(12):i258-i267, 2015.
J Kang, KM Puskar, AJ Ehrlicher, PR LeDuc, and RS Schwartz. "Structurally governed cell mechanotransduction through multiscale modeling." Scientific Reports. 5, 2015.
S. E. Shackney, S. A. Chowdhury, R. Schwartz. "A novel subset of human tumors that simultaneously overexpress multiple E2F responsive genes found in breast, ovarian, and prostate cancer." Cancer Informatics, 13(S5):89, 2014.
K. Heselmeyer-Haddad, L.Y. Berroa Garcia, A. Bradley, L. Hernandez, Y. Hu, J.K. Habermann, C. Dumke, C. Thorns, S. Perner, E. Pestova, C. Burke, S.A. Chowdhury, R. Schwartz, A.A. Schaffer, P. Paris, T. Ried. "Single-cell genetic analysis reveals insights into clonal development of prostate cancers and indicates loss of PTEN as a marker of poor prognosis", American Journal of Pathology, 184(10):2671-2686, 2014.

C. Tan, R. Schwartz, L. You. "Phenotypic signatures arising from unbalanced bacterial growth," PLoS Computational Biology, 10(8):e1003751, 2014.

H. Ashktorab, M. Daremipouran, J. Devaney, S. Varma, H. Rahi, E., Lee, B. Shokrani, R. Schwartz, M. Nickerson, H. Brim. "Identification of novel mutations by exome sequencing in African American colorectal cancer patients", Cancer, 121(1):34-42, 2014.

S.A. Chowdhury, S.E. Shackney, K. Heselmeyer-Haddad, T. Ried, A. Schaffer, R. Schwartz. "Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics," PLoS Computational Biology, 10(7):e1003740, 2014.

G.R. Smith, L. Xie, B. Lee, and R. Schwartz. "Applying cellular crowding models to simulations of capsid assembly in vitro." Biophysical Journal, 106(1):310-320, 2014.

M.-C. Tsai, G. Blelloch, R. Ravi, and R. Schwartz. "Coalescent-based Method for Learning Parameters of Admixture Events from Large-Scale Genetic Variation Data," IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(5):1137-1149, 2013. (extended version of ACM-BCB conference paper)

A. Subramanian, S. Shackney, and R. Schwartz. "Novel multi-sample scheme for inferring phylogenetic markers from whole genome tumor profiles." IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(6):1422-1431, 2013. (extended version of ISBRA conference paper)

C. Tan, S. Saurabh, M. Bruchez, R. Schwartz, and P. LeDuc. "Molecular crowding shapes gene expression in synthetic cellular nanosystems." Nature Nanotechnology, 8(8):602-608, 2013.

S. A. Chowdhury, S. E. Shackney, K. Heselmeyer-Haddad, T. Ried, A. A. Schaeffer, R. Schwartz. "Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations." Bioinformatics (proceedings issue for ISMB 2013), 29(13):i189-i198, 2013.

D. Catanzaro, R. Ravi, and R. Schwartz. "A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion." Algorithms for Molecular Biology, 8:3, 2013.