Research
Bioinformatics and Computational Biology
Our laboratory applies systems biology approaches to investigate spatial and temporal features of molecular and cellular processes. We are interested in integrating high-dimensional data sets to explore alternative hypotheses, to explain observed patterns, and ultimately to form testable hypotheses that can be validated experimentally. The goal is to obtain a quantitative understanding of complex biological systems.
Genomic Overload - Bioinformatics connects the dots
Meiosis
Meiosis is essential for sexual reproduction in eukaryotes. It is a conserved process in which diploid cells undergo one round of DNA replication followed by two rounds of chromosome segregation to produce haploid cells. Although the general chromosome behavior during meiosis is conserved in a range of organisms, the detailed molecular mechanisms and time frame required for each meiotic stage varies by sex and species. Budding yeast with heterozygosity at the mating-type locus can finish meiosis in hours under a nutrient-depleted environment. Whereas in mammals, germ cells in gonads may take from days to decades to accomplish meiosis with the support of neighboring somatic cells through hormonal cues. Male meiosis occurs continuously and asynchronously from puberty. In females, the entire oogonial population initiates meiosis synchronously in fetal ovaries and becomes arrested near the end of prophase I before birth. A small cohort of arrested oocytes then resumes meiosis during each ovulation after puberty. We are applying bioinformatics data mining and dynamic modeling to investigate functional and regulatory pathways controlling meiotic initiation and progression in yeast and mammals.
Temperature-sensitive mutations
Temperature-sensitive (Ts) mutants provide a powerful tool to investigate gene functions in vivo. Forward genetics screens for Ts mutants are laborious and time-consuming. Reverse genetics through computationally prioritizing residue substitutions can be more efficient to obtain Ts mutants. We are applying machine-learning methods to predict Ts mutants resulting from amino acid substitutions, and to investigate the molecular mechanisms of Ts mutants.