top.JPG (20988 bytes)

Welcome to my Web site! 


Chi-Ming Chen 

Biophysics and Complex Networks

Ph.D. Univ. of Michigan, 1996.

Classes Research Publications



The three dimensional structures of proteins play an important role in determining their biological functions. Although
tremendous efforts have been invested in studying the protein folding problem, it is so far unclear about the folding kinetics
and also difficult in predicting proteins' structure. Much less is known about membrane proteins than soluble proteins. Until now,
only a dozen or so Membrane proteins have known crystallographic structures. Membrane proteins perform important and diverse
functions in living cells, such as regulation, communication, and assisting the folding of other membrane proteins. In my previous
studies, I have proposed a lattice model to predict the structure and folding dynamics of membrane proteins. This model correctly
predicts the observed structures of gramicidin dimers and sensory rhodopsin I. Currently an off-lattice model is also proposed to
predict accurate structures of membrane proteins. Such a predicted structure of a membrane protein can be used to investigate its
biological functions by studying its ligand binding affinity.

Developing new methods and instruments that permit fast polynucleotide sequencing has attracted considerable attention
recently. A device for rapid DNA decryption will allow quick identification of pathogens to save many lives during an epidemic
or a bioterrorist attack. Moreover, doctors would be able to diagnose a disease, judge possible risks, and design a special
treatment plan based on knowledge about which disease-related genes a patient carries. Although DNA sequencing has important
medical applications, present methods in sequencing polynucleotides are slow, costly, and inaccurate. A comparison of
the Celera and Ensembl predicted gene sets has revealed little (20 %) overlap in novel genes. Therefore it is highly desired to
develop accurate sequencing methods that are fast and inexpensive. An interesting idea in sequencing DNA is to monitor the variation
of ionic current due to an applied electric field which drives single-stranded polynucleotides through a nanopore in a thin film.
Preliminary results of this method have shown its capability to distinguish long stretches of the same nucleotides, such as 30
adenines followed by 70 cytosines. Nevertheless, to make this method work for sequencing real DNA at single nucleotide
resolution, the translocation time of each nucleotide should be increased by a factor of 1000 and thermal fluctuations should be
suppressed to allow sequence analysis. Based on computer simulations, I have proposed a novel method to resolve the above
difficulties in nanopore sequencing of polynucleotides, from which a fast, inexpensive, and accurate device can be designed.

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.



Useful Links: