research

 

   

Systems Biology

 

  • Research Philosophy

Systems biology is an academic field that seeks to integrate different levels of information to understand how biological systems function. By studying the relationships and interactions between various parts of a biological system (e.g., gene and protein networks involved in cell signaling, metabolic pathways, organelles, cells, physiological systems, organisms etc.) it is hoped that eventually an understandable model of the whole system can be developed.
Systems biology begins with the study of genes and proteins in an organism using high-throughput techniques to quantify changes in the genome and proteome in reponse to a given perturbation.  High-throughput techniques to study the genome include microarrays to measure the changes in mRNAs. High-throughput proteomics methods include mass spectrometry, which is used to identify proteins, detect protein modifications, and quantify protein levels. In contrast to much of molecular biology, systems biology does not seek to break down a system into all of its parts and study one part of the process at a time, with the hope of being able to reassemble all the parts into a whole.

Using knowledge from molecular biology, the systems biologist can propose hypotheses that explain a system's behavior. Importantly, these hypotheses can be used to mathematically model the system. Models are used to predict how different changes in the system's environment affect the system and can be iteratively tested for their validity. New approaches are being developed by quantitative scientists, such as computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists, to improve our ability to make these high-throughput measurements and create, refine, and retest the models until the predicted behavior accurately reflects the phenotype seen.

  • Current Research

Molecular Network Reconstruction: An important area of research is protein-gene network reconstruction by the reverse engineering techniques based on the high throughput data in the systems biology framework. We aim to combine the microarray data, protein interaction data and mass spectrometry data in the system level to elucidate the relationship between genes and proteins. In such a manner, we developed a novel method to combine multiple
time-course microarray datasets from different conditions for inferring gene regulatory networks. We have developed GRNInfer (Gene Network Reconstruction tool) based on linear programming and a decomposition procedure. The proposed method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability.

Protein-Protein Interaction Inference:
To elucidate protein interaction networks is one of the major goals of functional genomics for whole organisms. So far, various computational methods have been proposed for inference
of protein-protein interactions. Based on the association method, we developed an association probabilistic method to infer protein interactions directly from the experimental data, which outperformed other existing methods in terms of both accuracy and efficiency despite its simple form. Specifically, we show that the association probabilistic
method achieves the highest accuracy among the existing approaches for the measures of root-mean square
error and the Pearson correlation coefficient, and also runs much faster than the Linear Programming-based
method, by experimental dataset in Yeast.

 

  • Future Research

(1). Nonlinear dynamical models and nonlinear analysis of biological systems, in particular at the molecular level. 
(2). Quantitative simulation of cellular dynamics
(3). Molecular communication
(4). Inferring gene regulatory network with multi-domain interactions by integrative databases
(5). Finding motifs and conserved substructures of protein interaction networks