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

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 intergrate 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.  Understanding signal processing in cells via analysis of single cell proteomic data.  Understanding 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.

Prediction of distinct aspects of protein structure and function from the evolutionary information contained in families of protein sequences. Prediction of functional classes for orphans, the sub-cellular localization from sequence, and protein-protein, and protein-substrate interactions from sequence and predicted structure. Improved protein structure prediction. Describing and clustering the space of protein sequences and structures in context of structural genomics.

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.

Learning and data mining research and its applications

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.