Interests

• Analysis of high-throughput biological data
• Algorithmic and analytical methods in systems/network biology
• Algorithms for data mining and analysis
• Parallel computing, algorithms for distributed systems
• Optimization problems in scientific computing

Graduate Students

• Sinan Erten, Ph.D. student
• Matthew M. Ruffalo, Ph.D. student (with T. LaFramboise)
• Marzieh Ayati, Ph.D. student
• Pavel Manaenkov, M.S. student (with P. Scacheri)
• Corey S. Adler, M.S. student

Undergraduate Students

• Ye Fang (CS)
• Theodore Roman (CS/Math, with R. Ewing)
• Alex Galante (Biology, with R. Ewing)
• Mitchell Murphy (CS, with R. Ewing)

Former Students

• Gokhan Yavas, Ph.D. (now with Case Comprehensive Cancer Center)
• Salim Akhter Chowdhury, M.S. (now with Carnegie Mellon University)

Ongoing Projects

Our research mainly focuses on development of models, algorithms, and computational techniques to extract information from a variety of data sources that relate to Molecular Biology, Systems Biology, and Genetics. The main challenges associated with analyzing this type of data include (i) the complexity of biological systems at multiple levels (from populations to molecules), (ii) the dynamical nature of biological phenomena in spatio-temporal dimensions, (iii) large scale and high-dimensionality of data, along with the combinatorial nature of interactions between different entities, and (iv) incompleteness and noisy nature of data collected from high-throughput experiments. While addressing these challenges, we often encounter sophisticated abstractions and intractable computational problems, which in turn provide us with the opportunity to contribute to computational sciences through development of advanced algorithms and computational techniques. The following projects are among those that are currently undertaken by our group.

Discovery of Coordinately Dysregulated Subnetworks in Complex Phenotypes
	Cellular systems are orchestrated through combinatorial organization of thousands of biomolecules. This complexity is reflected in the diversity of phenotypic effects, which generally present themselves as weak signals in the expression profiles of single molecules. For this reason, researchers increasingly focus on identification of multiple markers that together exhibit differential expression with respect to various phenotypes. In collaboration with the research group of Mark Chance, we focus on human colorectal cancer and develop abstractions and algorithms to define coordinate dysregulation of multiple genes within network context and identify such network patterns with a view to establishing markers for prognosis of cancer and targets for theurapetic intervention. For this purpose, our algorithms integrate genomic, transcriptiomic, proteomic, and interactomic data. This project is supported in part by NSF CAREER Award CCF-0953195.
Characterization of Copy Number Variation in Human Genome
Not long ago, it was discovered that individuals may differ in copy numbers of their genes, meaning that a segment of DNA may have more or less copies than usual in an individual's chromosome. Recent research suggests that these variations are associated with many diseases including Autism and Schizophrenia. Copy number variation (CNV) in somatic cells also underly various cancers. Copy numbers are usually identified using SNP microarrays, however, short-read sequence data is emerging as an important resource for characterizing structural variation in human genome. In collaboration with the research group of Thomas LaFramboise, we develop optimization based algorithms for fast and accurate identification of rare and de novo CNVs from these two data sources, with a view to enabling personalized genomics applications. This project is supported by National Science Foundation Award IIS-0916102.
Comparative Analysis of Cellular Organization
Since all biological systems are connected to each other through the process of evolution, a useful approach to understanding these systems involves comparing them. The organization of cellular systems is often abstracted using graph models that describe the network of molecular interactions (protein-protein interactions, gene regulation, transcription, signaling, etc.). We develop algorithms to identify commonalities and differences in networks that belong to different species, as well as to reconstruct phylogenies based on network information.
Functional Annotation of Modularity in Molecular Networks
	An important task in understanding biological systems is determining the functions of different biological entities. Recent efforts have been quite successful in annotating an important fraction of biomolecules, and functional ontologies have been developed to unify our understanding of molecular function. In collaboration with the research group of Ananth Grama, we develop algorithms to extend these ontologies and annotations to systems level, by discovering modular and overrepresented patterns in molecular interaction networks.