Associate Professor, Ritsumeikan APU
Institute of Information Technology
1-1 Jumonjibaru, Beppu-shi, Oita-ken
874-8557 Japan
One of the primary research areas in
Bioinformatics is the prediction of biopolymer native structure (the
so-called ‘folding problem’). One of the most popular approaches for
addressing this problem is the development of a statistical
thermodynamic model, which combines a statistical picture of biopolymer
interaction with experimentally measured energetic parameters, in order
to assess the relative occupancies of the various conformations
accessible to a biopolymer of interest. In my research, this approach
is applied to model the hybridization of nucleic acid mixtures. Because
the fields of DNA computing and nanotechnology provide
a wealth of interesting and relatively well-defined nucleic acid
mixtures for study, my attention has been focused on the modeling of
DNA-computing-based systems and biotechnologies. My two systems of
focus have been: (1) the
DNA chip, especially Tag-Antitag Systems, and (2) Whiplash PCR, a
simple autonomous DNA computer.
My recent published results include (please see Publications,
for articles): (1) development of an equilibrium chemistry/statistical
thermodynamic model for estimating the degree of undesired, or ‘error’
hybridization for the DNA Chip; (2) development of an evolutionary
approach
to apply this model to the solution of the associated ‘inverse folding
problem’ for Tag-Antitag Systems (i.e., select DNA sequences which
minimize
error hybridization); (3) development of a theoretical model for
assessing
the impact of hairpin backhybridization on the efficiency of Whiplash
PCR. In this model, statistical thermodynamics was applied to assess
the probability of successful extension during each effective
polymerase-DNA
encounter, while the overall iterative extension process of each DNA
hairpin was modeled as a Markov chain; (4) development and simulation
of PWPCR, an enhanced-efficiency version of Whiplash PCR, which
inhibits
backhybridization by targeted PNA2/DNA triplex formation; (5)
development and partial simulation of a PWPCR-based architecture for
implementing an
in vitro Genetic Program for solving instances of the NP-complete
problem, Hamilonian Path; (6) development and partial simulation of a
PWPCR-based Genetic Program for the in vitro evolution of proteins with
a high affinity to a molecular target of interest, via constrained
shuffling of protein ‘pseudomodules’. Current work is focused on
theoretical refinement and simulation,
as well as direct experimental validation of the predictions of these
models.