Systems-based gene expression data analysis to discover sets of synergistically interacting genes jointly contributing to a phenotype, with particular emphasis in cancer research and in deciphering of the biological mechanisms responsible for synaptic connectivity in C. elegans. Information theoretic analysis of multivariate synergy. Comparative genomics focusing on the introns of alternatively spliced genes to identify regulatory mechanisms for synaptic connectivity in advanced organisms.
Computational identification and characterization of cis-regulatory elements that control transcription and mRNA processing, their combinatorial logic and condition dependence, and the interplay between chromatin structure and gene expression control; predictive modeling of the regulatory network of the cell based on physical interactions, through integration of functional genomics data of different type.
Reverse engineering of metabolic and gene regulatory networks using dynamical and statistical predictive models that can be validated experimentally; analysis of sequence, structure, and microarray data using pattern and association discovery algorithms; use of association discovery techniques and their related statistical models to dissect complex genetic traits in a whole genome context
Development of integrated software environments for the analysis of multiple genomics data modalities (gene expression, sequence, structure, network, literature); grid technologies for remote access to computational and data resources; machine learning algorithms for detection of remote sequence homology; algorithms and informatics infrastructure for genome wide association studies; computational pipelines for the analysis of transcriptome data.
Use of computational and theoretical methods to study the structure and function of proteins, nucleic acids and membranes; the combined use of physical and chemical methods, amino acid sequence analysis, three dimensional structure analysis and data mining as tools in bioinformatics and genome analysis.
Development of computational methods to integrate high-throughput data to unravel the structure, function, and evolution of molecular networks. Machine learning approaches for reconstruction of regulatory, signaling, and metabolic networks. "Genetic Genomics" - complexity of biological traits: analysis of how sequence polymorphisms between individuals manifest in phenotypic diversity. Modularity and motifs in molecular networks. Signal processing in cells via analysis of single cell proteomic data. Connections between regulation and fitness and how this drives the evolution of regulatory networks.
Development of computational methods for human genetics, and their application to understand human disease and genetic makeup; analysis of whold genome association studies; demographic structure of isolated and admixed populations; relationship between germline and somatic variation; whole-genome, whole-population genetic data.
Dr. Rabadan's current interest focuses on patterns of evolution in fast evolving biological systems. For the past several years, there has been a worldwide effort to sequence viral genomes, in particular, HIV and influenza. Dr. Rabadan and his colleagues at the Institute have been developing tools to analyze this data, extracting the relevant information to understand the molecular biology, evolution, and epidemiology of these viruses.
David E. Shaw
Dr. Shaw's lab is involved in the development of algorithms and machine architectures for the ultra-high-speed simulation of protein dynamics, and in the application of such simulations to elucidate the atomic-level mechanisms underlying various biological processes.
Development and application of computational methods to study human genetics and diseases. Specific areas of interest include genome sequencing and de novo assembly, genetic mapping of human diseases, autoimmunity and major histocompatibility complex, pharmacogenomics and personalized treatment.
Gustavo A. Stolovitzky
DNA chip analysis and gene expression data mining; reverse engineering of metabolic and gene regulatory networks; massively parallel signature sequencing analysis.
Integration and analysis of heterogeneous data types — including next-generation genome and transcriptome sequencing, metabolomics, proteomics, and electronic medical records — to advance understanding of basic biology and human disease.
Development and use of novel technologies for making genome-wide observations, together with computational and analytic tools required to turn these observations into predictive models of biological systems with the goal of understand the organizing principles that underlie the evolution and function of molecular networks. Our research focuses on these problems in the context of transcriptional regulatory and genetic networks of organisms ranging from bacteria to human.
Development of novel computational methods for reconstruction and simulation of cellular networks. Understanding evolution of biological networks and evolution of proteins in the context of networks. Prediction effects of deleterious mutations using constraint based approaches. Understanding principles of network regulation. Integrating information from structural and systems biology.
Development of forward-engineering tools to model and predict the behavior of synthetic genetic networks using stochastic calculus, dynamical systems, and adiabatic elimination via separation of time scales; development of reverse-engineering tools to deduce the circuitry of naturally occurring genetic networks using bioinformatics, e.g., clustering and dynamical systems. Automating capture of image features of interest to biologists.
Development and application of novel experimental and computational approaches to studying protein-RNA interactions, and transcriptome and global RNA-regulatory networks in the mammalian brain. Application of the knowledge we learn from model systems to understand the roles of post-transcriptional RNA regulation in evo-devo processes and in neurodegenerative diseases at the systems level.