Russell S. Schwartz

Professor

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

Wren NS, Schwartz R, Dahl KN. Modeling nuclear blebs in a nucleoskeleton of independent filament networks. Cellular and Molecular Bioengineering, accepted for publication, 2011.

Misra N, Blelloch G, Ravi R, Schwartz R. An optimization-based sampling scheme for phylogenetic trees. Journal of Computational Biology, in press, 2011. (extended version of RECOMB 2011 conference paper)

Lee B, LeDuc PR, Schwartz R. Unified regression model of binding equilibria in crowded environments. Scientific Reports, 1:97, 2011.

Subramanian A, Shackney S, Schwartz R. Inference of tumor phylogenies from genomic assays on heterogeneous samples. Proceedings of the ACM Conference on Bioinformatics and Computational Biology, 2011.

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.

Gong H, Guo Y, Linstedt AD and Schwartz R. Discrete, continuous, and stochastic models of protein sorting in the Golgi. Physical Review E, 81(1):011914, 2010.