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Bioinformatics and Complex Networks

Ph.D. Univ. of Michigan, 1996.

Classes Research Publications



In recent years, the number of new genomic and proteomic sequences being produced in laboratories has increased by several orders of magnitude. This explosion of new sequences produces a demand for methods capable of efficiently characterizing these sequences and of synthesizing this information into useful knowledge in the domains of biological complexity and human medicine. To extract such knowledge from large quantities of experimental data, we applied statistical modeling and machine learning algorithms to derive, cluster, and visualize the relationship between proteins or genes in their sequence, structure, function, and evolution. We also used network theory to determine the significance of proteins or genes and the flow of dynamic information. Finally, we constructed interactive online graphic database of these biological networks for scientific and educational purposes. Our current research focuses on biological networks of GPCRs, of enzymes, of aging genes, and of cancer genes.

Most social, biological, and technological entities form complex networks, which display non-trivial topological features and connection patterns between their elements. Massive datasets collected from these networks allow us to delineate the community structure of these networks, investigate their significant elements, and study the flow of information through a network. We applied graph theory, network analysis, and machine learning algorithms to study a great variety of complex networks, including social networks, scientific networks, neural networks, and financial networks.

The function of the nervous system critically relies on the synaptic connections among neurons. Studies on lower mammals have demonstrated that, in almost all peripheral and central nervous systems studied so far, synapses between neurons are established from the early developmental stages and synchronized neuron activities are generated in the developing networks. It has been shown that immature pyramidal neurons of the rat hippocampus start to receive sequentially established synaptic inputs around birth and the hippocampal network generates periodic synchronized neuronal discharges during the first two postnatal weeks. Such a synchronized activity drives synchronized oscillations of intracellular calcium and provide conditions for Hebbian plasticity in developing synapses. Recent experimental advances, including real-time imaging of living neurons, have provided physical insight into the molecular and cellular processes that guide synaptogenesis in the developing nervous systems. Therefore, based on physically modeling experimental observations, computer assisted simulations of my recent studies can help us understand the synchronized activities in a developing neuron network and the plasticity of the neuron network affected by learning.