Hao Li, Ph.D.
Assistant Professor, Department of Biochemistry and Biophysics



Contact Information:
haoli@genome.ucsf.edu
Phone: 502-8187
Fax: 514-2617
Box 2542, HSE-201
Byers Hall, Room 403


Other websites:
Lab Website

Publications

Development of theoretical and computational tools to extract biological information from genome sequences and the large quantity of data generated from experiments facilitated by various genome projects

Developing novel algorithms for analyzing the regulatory regions of genomes and transcriptional regulation on a genomic scale
The genetic programs coded in the regulatory regions of a genome specify when and where different genes should be turned on or off. Such information is essential for understanding development, tissue specificity, and cellular response to the environment. However, the development of computational tools for analyzing regulatory regions has lagged behind those for gene discovery and protein sequence comparison. Recently, we have developed several novel algorithms to identify multiple regulatory elements from genome sequences (see publications ). We will further develop these algorithms to increase their sensitivity and specificity. Several important generalizations will be made. We will also develop methods for comparing the regulatory regions of orthologous genes across species. In the past, comparative study of proteins across species has revealed many insights into protein function and evolution. My lab is exploring the potential of comparative study of noncoding regions for deciphering regulatory information. We will develop methods for comparing the regulatory regions of closely related as well as distant species. we will also carry out quantitative analysis of genome-wide gene expression data to extract relevant regulatory elements and determine their logical interrelations.

Developing tools for analyzing gene regulatory networks using gene expression and protein-protein interaction data
DNA micro-array has been widely used to monitor genome-wide gene expression. It has also been used to probe biological pathways by measuring the genome-wide change of gene expression due to various genetic and environmental perturbations. One ongoing project in my lab is to identify putative transcription factor binding sites and potential target genes using DNA micro-array data. The long term goal is to develop methods for reconstructing regulatory pathways using gene expression data in conjunction with large scale protein-protein interaction data (e.g., from genome-scale two hybrid screens).

Protein sequence and structure analysis
One important task in functional genomics is to determine the functions of novel genes. However, one still cannot reliably predict the 3D structure of a protein from its amino acid sequence. Previously, we have analyzed the protein folding problem from a different perspective by asking why nature only selects about 1000 folds to use as protein folds. We have proposed a designability principle for protein structure selection based on simple model studies (see publications ). The principle states that a protein structure should be designable by a huge number of sequences and therefore must satisfy strong constraints. My lab will investigate whether the designability principle is valid for real proteins and what are its consequences on protein design and structure prediction. We are also developing new approaches to predicting protein-protein interaction and protein binding interface using sequence and structure information.

Recent Publications:
Building A Dictionary for Genomes: Identification of Presumptive Regulatory Sites by Statistical Analysis Proc. Natl. Acad. Sci. vol 97, 10096 (2000), with Bussemaker and Siggia

Regulatory Element Detection Using Correlation with Expression. Nature Genetics vol 27, 167 (2001) with Bussemaker and Siggia

Regulatory Element Detection Using A Probabilistic Segmentation Model
Proceedings of ISMB 2000, with Bussemaker and Siggia

Ising Model in Physics and Statistical Genetics
American Journal of Human Genetics vol 69, 853 (2001), with Majewski and Ott

Designability, Thermodynamic Stability and Dynamics in Protein Folding: A Lattice Model Study. J. Chem. Phys. vol 110, 1252 (1999), with Melin et al.

Dynamics and stress in gravity driven granular flow C. Denniston & H. Li, Phys. Rev. E 59, 3289 (1999).

Are Protein Folds Atypical? Proc. Natl. Acad. Sci. 95, 4987 (1998), with Tang and Wingreen

Nature of Driving Force for Protein Folding: A Result From Analyzing the Statistical Potential
Phys. Rev. Lett. 79, 765 (1997), with Tang and Wingreen

Emergence of Preferred Structures in a Simple Model of Protein Folding. Science 273, 666 (1996), with Tang, Wingreen and Helling