Home Page of Hin Hark Gan

 

Research Assistant Professor
Department of Chemistry
New York University
100 Washington Place, 1021 Silver
New York, NY 10003, USA
Email:  hgan@biomath.nyu.edu
Tel: (212) 998 3594; Fax: (212) 995 4152

My other NYU links: Department of Chemistry, Computational Biology, Graduate School of Arts and Science

B.Sc. 1984 (Theoretical Physics and Applied Mathematics), McMaster; M.Sc. 1986 (Physics), McGill; Ph.D. 1989 (Physics), McGill; postdoctoral, McGill and NYU/Howard Hughes Medical Institute

POSITIONS HELD
2005          Associate Director, Doctoral Program in Computational Biology , NYU
2003-         Research Assistant Professor, NYU
1999-03     HHMI Bioinformatics Specialist at NYU

RESEARCH AREAS

My research areas include theoretical modeling of RNA in vitro selection, prediction of RNA tertiary structures, and design of novel RNAs for biological applications.

Theoretical modeling of in vitro selection of RNAs

In vitro selection is a versatile experimental technology for discovering novel functional RNAs from random-sequence pools. In the last decade, many target-binding and catalytic RNAs have been identified for wide-ranging applications, including RNA-based biosensors, molecular therapeutics, synthetic biology, and nanotechnology.  Thus, theoretical modeling of in vitro selection of RNAs will help advance vital applications.

 

Current use of random pools for in vitro selection experiments has inherent limitations, including non-exhaustive coverage of sequence space and prevalence of simple topological motifs (e.g., stem-loop, stem-bulge-stem-loop). To improve RNA in vitro selection technology, synthesized RNA pools must possess sufficient sequence and structural complexity to enable discovery of complex RNA molecules (e.g., allosteric ribozymes).

 

Our aim is to develop computational methods for designing RNA pools with diverse sequences and structures. Recently, we have made progress in analysis and design of sequence pools. We showed quantitatively that random pools are not structurally diverse, confirming results from various in vitro selection experiments. We have also developed an automated algorithm for designing pools with user-specified target structural distribution (e.g., specific topological structures or graphs representing RNA trees). The algorithm optimizes the nucleotide mixing matrix or nucleotide mixing ratios in synthesis ports.  This algorithm has been implemented in a web server RAGPOOLS to allow experimentalists and other researchers to analyze and design RNA pools.

 

We are continuing development of efficient computational techniques for designing RNA pools and collaborating with experimentalists to test pool design strategies. In particular, we are exploringMonte Carlo strategies for efficient sampling of the sequence and structure space. We are also developing theoretical approaches to directed evolution, an experimental method for refining RNA function via cycles of mutations and function selection. Theoretical modeling of RNA in vitro selection and directed evolution requires close collaboration with experimentalists, a deep understanding of the relation between RNA structure and function, and innovative computational/mathematical techniques to model aspects of the experimental technology (i.e., pool synthesis, RNA selection and evolution).

 

Computational RNA tertiary folding and design

Current tertiary RNA folding simulations are limited to small systems (e.g., RNA hairpins), and RNA design algorithms are confined to analysis of 2D structures. In contrast, in protein folding and design, emerging computational concepts and technologies such as fragment assembly and 3D-based design have proven successful. To overcome current limitations in RNA structure prediction, we are combining computational technologies for proteins with knowledge of various RNA tertiary interaction motifs to develop effective approaches for predicting RNA tertiary structures. W e are also developing a 3D-based framework for computational RNA engineering to extend the capability and precision of current 2D design algorithms. This framework for molecular engineering, which includes advanced dynamics analysis tools, allows analysis of the functional properties of designed RNAs through computation of tertiary structures, RNA-ligand binding and dynamics. Specifically, we are interested in engineering RNA systems such as allosteric ribozymes, riboswitches and fluorescent aptamers for emerging applications in bioengineering.

 

Other approaches to RNA structure and function

We are also pursuing other approaches to RNA structure and function. For example, we have used in vitro selected RNAs to find RNA motifs in genomes and employed statistical techniques to estimate the fraction of non-coding RNAs in mammalian transcriptomes (i.e., total expressed transcripts in cells). In addition, we are developing computational approaches to identify novel target sites for antibiotics on bacterial ribosomal RNAs. This work involves assessing the energetics of antibiotic/rRNA binding and relating sequence conservation to phenotypes of rRNA mutations.

RECENT PUBLICATIONS

Kim N, Shin JS, Elmetwaly S, Gan HH, and Schlick T, RAGPOOLS: RNA-As-Graph-Pools A web server for assisting the design of structured RNA pools for i n vitro s election. Bioinformatics 2007 (In Press).

Kim N, Gan HH, Schlick T, A computational proposal for designing structured RNA pools for in vitro selection of RNAs.RNA 2007, 13(4):478-92.

Gevertz J., Gan HH, and Schlick T, In vitro RNA random pools are not structurally diverse: a computational analysis. RNA 2005, 11(6):853-63.

Laserson U, Gan HH, Schlick T, Predicting candidate genomic sequences that correspond to synthetic functional RNA motifs. Nucleic Acids Res. 2005, 33(18):6057-69.

Pasquali S, Gan HH, Schlick T. Modular RNA architecture revealed by computational analysis of existing pseudoknots and ribosomal RNAs. Nucleic Acids Res. 2005, 33(4):1384-98.

Kim N, Shiffeldrim N, Gan HH, Schlick T. Candidates for novel RNA topologies. J. Mol. Biol. 2004, 341(5):1129-44.

Gan HH, Fera D, Zorn J, Shiffeldrim N, Tang M, Laserson U, Kim N, Schlick T. RAG: RNA-As-Graphs database--concepts, analysis, and features. Bioinformatics. 2004, 20(8):1285-91.

Zorn J, Gan HH, Shiffeldrim N, Schlick T. Structural motifs in ribosomal RNAs: implications for RNA design and genomics. Biopolymers. 2004, 73(3):340-7.

Gan HH, Pasquali S, Schlick T. Exploring the repertoire of RNA secondary motifs using graph theory; implications for RNA design. Nucleic Acids Res. 2003, 31(11):2926-43.

Gan HH, Tropsha A, Schlick T. Lattice protein folding with two and four-body statistical potentials. Proteins. 2001, 43(2):161-74.

BOOK
Computational Methods for Macromolecules: Challenges and Applications - Proceedings of the 3rd  International Workshop on Algorithms for Macromolecular Modeling. Editors: T. Schlick and H.H. GAN, Lecture Notes in Computational Science and Engineering series, Springer-Verlag, Vol. 24,  2002.

RNA DATABASES/WEB SERVERS

1.RNA-AS-GRAPHS WEB RESOURCE

2. RAGPOOLS web server for designing RNA pools for in vitro selection of functional RNAs 

STUDENTS WHOSE RESEARCH I HAVE HELPED SUPERVISE (PAST AND PRESENT)
Sabera Asar, Shreyas D'Souza, Daniela Fera (U Penn), Jana Gevertz (Princeton), Uri Laserson (MIT), Giulio Quarta (NYU),  Namhee Kim (NYU), Frank Lalelazadeh (Cornell), Joyce Noah (Stanford), Samuela Pasquali (ESPCI, Paris), Jimmy Potter (Brown), Christian Rose, Evan Sherman (Columbia), Jin Sup Shin (NYU), Nahum Shiffeldrim (NYU), Joey Sofaer, Padmavati Sridhar (Columbia), Michael Tang (Princeton), Julie Zorn (UCSF).

September 20, 2007.