Office : Mellon Institute MI 654F
Email : ayshwarya AT cmu DOT edu
I am a Ph.D Candidate in Computational Biology at the Department of Biological Sciences
at Carnegie Mellon University
advised by Dr.Russell Schwartz
I graduated with an undergraduate Honors degree in Life Sciences from BITS,Pilani
, India in 2007.
I am broadly interested in the design of research problems in health and medicine using computational, statistical and mathematical concepts. Specifically, I have worked on inferring evolution in biological systems like tumors and human populations drawing ideas in machine learning, phylogenetics, statistics and computational geometry.
Computational biology, Bioinformatics, Machine Learning, Big data, Personalized medicine, Statistics, Computational Geometry
My research tries to understand tumors as complex evolutionary systems using evolutionary trees or phylogenies for representation. Specifically, I developed methods to build tumor phylogenies from array-based whole genome copy number data using principles from biology.
Research in cancer biology has inferred that cancers are complex evolutionary systems with characteristics of hypermutability and selection for mutations favoring the continued survival of the tumor. The competing forces of hypermutability and selection lead to diverse heterogeneity both within a single tumor and between tumor samples. This heterogeneity poses a major challenge to devising general diagnostic and treatment paradigms. There is however an understanding that underlying the heterogeneity, there is still a smaller sequence of common driver mutations that is essential to tumor survival in a specific organ system and a larger set of mutations private to individual patient tumor samples for the same organ system of study. There is also an understanding that these driver mutations aim to disrupt specific driver pathways and can hence be found in any key member of these pathways. There is hence, much interest in understanding the sequence of driver mutations underlying tumor progression in a specific organ system and my dissertation attempts to study this problem of inferring tumor evolution by applying a combination of approaches drawn from phylogenetics, machine learning and statistical driven by sound biological principles.
Peer-reviewed Journal Articles
Subramanian A, Shackney S, Schwartz R. Novel multi-sample scheme for inferring phylogenetic markers from whole genome tumor profiles. IEEE/ACM Trans Comput Biol Bioinform (in press).
Subramanian A, Shackney S, Schwartz R. Inference of tumor phylogenies from genomic assays on heterogeneous samples. J Biomed Biotechnol. 2012; 2012:797812.
Tolliver D, Tsourakakis C, Subramanian A, Shackney S, Schwartz R. Robust unmixing of tumor states in array comparative genomic hybridization data. Bioinformatics. 2010 Jun 15;26(12):i106-14.
Adithi M, Kandalam M, Ramkumar HL, Subramanian A, Venkatesan N, Krishnakumar S. Retinoblastoma: expression of HLA-G. Ocul Immunol Inflamm. 2006 Aug; 14(4):207-13.
Peer-reviewed Conference Papers
Ayshwarya Subramanian, Russell Schwartz and Stanley Shackney. "Novel multi-sample scheme for inferring phylogenetic markers from whole genome tumor profiles". International Symposium on Bioinformatics Research and Applications (ISBRA) 2012.
Ayshwarya Subramanian, Stanley Shackney and Russell Schwartz. Inference of tumor phylogenies from genomic assays on heterogeneous samples. ACM-BCB 2011.
Subramanian A, Shackney S, Schwartz R.
Tumor phylogenetics in the Next Generation Sequencing era : Strategies and Challenges
. Applications of Next Generation Sequencing in Cancer Research 2012(in submission).
Ayshwarya Subramanian, Stanley Shackney and Russell Schwartz. Towards novel marker discovery from phylogenetic analysis of heterogeneous tumor samples. AACR(American Association for Cancer Research)Annual Meeting 2011.
Ayshwarya Subramanian, Stanley Shackney and Russell Schwartz. Phylogenetic Methods for inferring tumor progression pathways from aCGH profiles of mixed cell populations. Poster, ISMB 2010.
Inferring Tumor progression in breast cancer
(with Dr.Russell Schwartz
and Dr.Stanley Shackney
, June 2009 - present)
I develop computational methods to discern cancer subtypes from next generation sequencing data to determine tumor evolution and novel marker discovery.In the past,I performed geometric unmixing of array comparative genomic hybridization (aCGH) data and then examined phylogeny inference on the unmixed data.
Key Areas : Tumor Progression, Statistics, Denoising, Phylogenetics, Computational geometry, Tumor heterogeneity, Next generation Sequencing
Analysis of tree statistics in human evolutionary trees
(with Dr.Russell Schwartz, June 2008 - June 2009)
I determined tree statistics for human population phylogenies built from whole genome SNP data with a view to predicting cell fate and mutation hotspots.
Key Areas : Statistics, Machine Learning, Population Genetics, Phylogenetics
Information driven clustering of cell-images
(Rotation project with Dr.Robert Murphy
, September 2007 - December 2007)
I applied unsupervised learning algorithms to yeast cell-images with a view to determining intra-cellular protein location patterns and clustering.
Key Areas : Unsupervised learning,Model Selection
Validation of Bayesian Models for Population Structure Inference
(Rotation project with Dr.Eric Xing
, December 2007 - March 2008)
I simulated haplotypes and population data for inference of Population structure and recombination events using Bayesian Models.
Key Areas : Population Structure Inference
Mathematical Modeling of phytotactic responses in Halobacterium salinarium
(Undergraduate Honor Thesis with Dr.Wolfgang Marwan, January 2007 - May 2008)
I built a theoretical minimal model to explain the cytoplasmic intermixing of Physarum polycephalum plasmodia. To analyse the time resolved somatic complementation by recording cytoplasmic intermixing from image data, I customized an image processing repertoire.I built DAE models to study the effect of receptor clustering on the adaptation mechanism of phototaxis in Halobacterium salinarium and performed simulations.
Key Areas : Simulation, Modeling, Image Processing
10701 Machine Learning, 10702 Statistical Machine Learning, 10705 Intermediate Statistics,02712 Computational Methods for Biological Modeling and Simulation,15211 Data Structures and Algorithms
03710 Computational Biology,Spring 2009 with Dr.Robert Murphy
03711 Computational Molecular Biology and Genomics,Fall 2009 with Dr.Dannie Durand
03712 Computational Methods for Biological Modeling and Simulation,Fall 2010 with Dr.Russell Schwartz
Professional Assistant, BIO C461 Recombinant DNA Technology,Fall 2006 with Dr.Ashish K.Das
Reviewer for RECOMB 2011,WABI 2010, RECOMB 2010, RECOMB 2009, BIBM 2009
Undergraduate Research Mentor for Titas Banerjee, Fall 2010
Associate Member, American Association for Cancer Research (AACR)
Student Member, International Society for Computational Biology(ISCB)
VP,Planning and Organization.SEA(Scientists and Engineers for America)-CMU Chapter
Invited Panelist, ACM Women In Bioinformatics, ACM-BCB 2011
CMU Department Graduate Travel Fellowship, Fall 2011
ACM-BCB Travel Fellowship,ACM-BCB 2011
Indian Academy of Science Summer Science Fellowship, Summer 2006