Russell S. Schwartz
654B Mellon Institute
Department of Biological Sciences
Carnegie Mellon University
4400 Fifth Avenue
Pittsburgh, PA 15213
Ph.D., Computer Science, Massachusetts Institute of Technology
Postdoctoral Appointment, Massachusetts Institute of Technology
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.
Chowdhury SA, Shackney SE, Heselmeyer-Haddad K, Ried T, Schäffer AA, Schwartz R. Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics. PLoS Comput Biol. 2014 Jul 31;10(7):e1003740.
Welch L, Lewitter F, Schwartz R, Brooksbank C, Radivojac P, Gaeta B, Schneider MV. Bioinformatics curriculum guidelines: toward a definition of core competencies. PLoS Comput Biol. 2014 Mar 6;10(3):e1003496.
Smith GR, Xie L, Lee B, Schwartz R. Applying molecular crowding models to simulations of virus capsid assembly in vitro. Biophys J. 2014 Jan 7;106(1):310-20.
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 Trans Comput Biol Bioinform. 2013 Sep-Oct;10(5):1137-49.
A. Subramanian, S. Shackney, and R. Schwartz. Novel multi-sample scheme for inferring phylogenetic markers from whole genome tumor profiles. IEEE/ACM Trans Comput Biol Bioinform. 2013 Nov-Dec;10(6):1422-31.
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. 2013 Jul 1;29(13):i189-98
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 Mol Biol. 2013 Jan 23;8(1):3.
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. Am J Pathol. 2012 Nov;181(5):1807-22.
L. Xie, G. Smith, X. Feng, and R. Schwartz. Surveying capsid assembly pathways through simulation-based data fitting. Biophys J. 2012 Oct 3;103(7):1545-54
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. Appl Phys Lett. 2012 Aug 6;101(6):63701.
A. Subramanian, S. Shackney, and R. Schwartz. Novel multi-sample scheme for inferring phylogenetic markers from whole genome tumor profiles. IEEE/ACM Trans Comput Biol Bioinform. 2013 Nov-Dec;10(6):1422-31
A. Subramanian, S. Shackney, and R. Schwartz. Inference of tumor phylogenies from genomic assays on heterogeneous samples. J Biomed Biotechnol. 2012;2012:797812.
H. Kuwahara and R. Schwartz. Stochastic steady state gain in a gene expression process with mRNA degradation control. J R Soc Interface. 2012 Jul 7;9(72):1589-98
B. Lee, P. R. LeDuc, and R. Schwartz. Three-dimensional stochastic off-lattice model of binding chemistry in crowded environments. PLoS One. 2012;7(1):e30131.
N. S. Wren, R. Schwartz, and K. N. Dahl. Modeling nuclear blebs in a nucleoskeleton of independent filament networks. Cell Mol Bioeng. 2012 Mar 1;5(1):73-81.
C. E. Tsourakakis, R. Peng, M. A. Tsiarli, G. L. Miller, and R. Schwartz. Approximation algorithms for speeding up dynamic programming and denoising aCGH data. Journal of Experimental Algorithmics, 16:1.8, 2011.
Misra N, Blelloch G, Ravi R, Schwartz R. An optimization-based sampling scheme for phylogenetic trees. J Comput Biol. 2011 Nov;18(11):1599-609.
Lee B, LeDuc PR, Schwartz R. Unified regression model of binding equilibria in crowded environments. Sci Rep. 2011;1:97.
Subramanian A, Shackney S, Schwartz R. Inference of tumor phylogenies from genomic assays on heterogeneous samples. J Biomed Biotechnol. 2012;2012:797812.
Tsai MC, Blelloch G, Ravi R, Schwartz R. A consensus-tree approach for reconstructing human evolutionary history and identifying population substructure. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 8(4):918-928, 2011. (extended version of ISBRA 2010 conference paper)
Kang J, Steward R, Schwartz R, Leduc PR, Puskar K. Controlled response of actin filament networks under cyclic stress through a coarse grained Monte Carlo model. Journal of Theoretical Biology, 274:109-119, 2011.
Misra N, Blelloch G, Ravi R, and Schwartz R. An optimization-based sampling scheme for phylogenetic trees. Proc. International Conference on Research in Computational Molecular Biology (RECOMB 2011), 252-266, 2011.
Miller GL, Peng R, Schwartz R, and Tsourakakis C. Approximate dynamic programming using halfspace queries and multiscale Monge decomposition. ACM Symposium on Discrete Algorithms (SODA2011), 1675-1682, 2011.
Misra N, Ravi R and Schwartz R. Generalized Buneman pruning for inferring the most parsimonious multi-state phylogeny. Journal of Computational Biology, 18(3):445-457, 2011. (extended version of RECOMB 2010 conference paper).
Kumar MS and Schwartz R. A parameter estimation technique for stochastic self-assembly systems and its application to human papillomavirus self-assembly. Physical Biology, 7:045005, 2010.
Tolliver D, Tsourakakis C, Subramanian A, Shackney S, Schwartz R. Robust unmixing of tumor states in array comparative genomic hybridization data. Bioinformatics (Proceedings Issue for Intelligent Systems for Molecular Biology (ISMB 2010)), 26(12):i106-i114, 2010.
Lancia G, Rizzi R, Schwartz R. Tiling Binary Matrices in Haplotyping: Complexity, Models and Algorithms. Proceedings of the Prague Stringology Conference (PSC 2010), pp. 89-102, 2010.
Schwartz R and Shackney S. Applying unmixing to gene expression data for tumor phylogeny inference. BMC Bioinformatics, 11:42, 2010.