COMPUTATIONAL BIOLOGY & BIOINFORMATICS
- Computational Systems Biology: We explore ideas of systems analysis of biological systems by developing mathematical models and relevant algorithms.
- Comparative network analysis provides better understanding of underlying cellular mechanisms of biological functions, which helps us further develop methods to identify conserved pathways for evolution study; identify robust disease biomarkers for complex disease diagnosis/prognosis; and reconstruct regulatory networks to study their long-term dynamical behavior. We are currently investigating efficient algorithms to solve these challenging problems.
- Network intervention is one of ultimate objectives of studying systems biology. Degenerative diseases including cancer are systems impairments due to the failure of normal cellular function. To develop effective intervention therapeutics, it is important to study the complex molecular interactions at systems level. As engineering, especially, system engineering and control theory, has had important successes to adaptively control large engineering or artificial systems’ behavior even with only limited information, we are interested in developing stochastic models that underlies many system engineering successes to design network intervention strategies for effective future gene therapies of complex diseases including cancer, diabetes, etc.
- Biomedical Image Analysis: We are also interested in developing efficient algorithms for biomedical image analysis, including image segmentation, shape analysis and indexing..