Russell S. Schwartz

Professor and Head, Computational Biology Department

Address:
654B Mellon Institute
Department of Biological Sciences
һ��
4400 Fifth Avenue
Pittsburgh, PA 15213

Phone: 412-268-3871
Fax: 412-268-7129

Education

Ph.D., Computer Science, Massachusetts Institute of Technology
Postdoctoral Appointment, Massachusetts Institute of Technology

Research

One major interest of my group is the analysis of genetic variations, with specific application to inference of population subgroups and phylogenetics. We have focused for a number of years on the analysis of single nucleotide polymorphism (SNP) data and how they can help us understand the formation of human population subgroups and our history as a species as well as assist us in identifying correlations between genotype and phenotype. Our work includes basic theory on models and algorithms for these inference problems as well as application to studies of large-scale variation patterns in the human genome. We have recently extended this work into examination of the phylogenetics of tumor development. Our hope is to use novel phylogeny inference methods to understand patterns of progression of cancers so as to better identify clinically significant cancer sub-types, markers of cancer progression, and possible novel therapeutic targets.

Our other major direction is modeling and simulation of biological systems, particularly self-assembly systems. Self-assemblies systems are ubiquitous in biology and essential to nearly every biological function, yet they are difficult to analyze either experimentally or theoretically. We seek to address these problems by developing and applying stochastic simulation methods for complex self-assemblies. Our lab works in part on theoretical issues in the development of accurate and efficient simulation of self-assemblies and in part on the application of these methods to specific systems of interest, most prominently virus capsid assembly. A recent focus of ours has been better understanding how models must be adapted to realistically model assembly in living cells versus the test tube environment in which most available data is gathered.

Publications

The following are selected recent publications:

A. Subramanian and R. Schwartz. "Reference-free inference of tumor phylogenies from single-cell sequencing data." BMC Genomics, accepted for publication, 2015.

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, accepted for publication, 2015.

J. Kang, R. Schwartz, J. Flickinger, and S. Beriwal. "Machine learning approaches to predicting radiotherapy outcomes: A clinician's perspective." International Journal of Radiation Oncology, Biology, Physics, accepted for publication, 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, 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. Schäffer, 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.

L. Welch, F. Lewitter, R. Schwartz, C. Brooksbank. P. Radivojac, B. Gaeta, M.V. Schneider. “Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies,” PLoS Computational Biology, 10(3): e1003496, 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.

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.

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. Schäffer, R. Schwartz. “Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations.” Bioinformatics, 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.

K. Heselmeyer-Haddad, L. Y. Berroa Garcia, A. Bradley, C. Ortiz-Melendez, W.-J. Lee, R. Christensen, S. A. Prindiville, K. A. Calzone, P. W. Soballe, Y. Hu, S. A. Chowdhury, R. Schwartz, A. A. Schäffer, and T. Ried. “Single-cell genetic analysis of ductal carcinoma in situ and invasive breast cancer reveals enormous tumor heterogeneity, yet conserved genomic imbalances and gain of MYC during progression.” American Journal of Pathology, 181(11):1807-1822, 2012.

L. Xie, G. Smith, X. Feng, and R. Schwartz. “Surveying capsid assembly pathways through simulation-based data fitting.” Biophysical Journal, 103:1545-1554, 2012.

W. C. Ruder, C.-P. D. Hsu, B. D. Edelman, R. Schwartz, and P. R. LeDuc “Biological colloid engineering: self-assembly of dipolar ferromagnetic chains in a functionalized biogenic ferrofluid.” Applied Physics Letters, 101:063701, 2012.

A. Subramanian, S. Shackney, and R. Schwartz. “Inference of tumor phylogenies from genomic assays on heterogeneous samples.” Journal of Biomedicine and Biotechnology, 2012:798812, 2012.

H. Kuwahara and R. Schwartz. “Stochastic steady state gain in a gene expression process with mRNA degradation control.” Journal of the Royal Society Interface, 9:1589-1598, 2012.

B. Lee, P. R. LeDuc, and R. Schwartz. “Three-dimensional stochastic off-lattice model of binding chemistry in crowded environments.” PLoS One, 7(1): e30131, 2012.

N. S. Wren, R. Schwartz, and K. N. Dahl. “Modeling nuclear blebs in a nucleoskeleton of independent filament networks.” Cellular and Molecular Bioengineering, 5(1):73-81, 2012.

News
Alumni

Forms

һ��������