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.
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Research Philosophy
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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.
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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