Hiroyuki Kuwahara

H. Kuwahara, I. Mura. An efficient and exact stochastic simulation method to analyze rare events in biochemical systems Journal of Chemical Physics, October 22, 2008, Volume 129, Issue 16, doi:10.1063/1.2987701.
[.pdf]
Copyright (2008) American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in H. Kuwahara and I. Mura, J. Chem. Phys. 129, 165101 (2008) and may be found at Kuwahara & Mura, J. Chem. Phys. 129, 165101 (2008).

H. Kuwahara, C. Myers. Production-passage-time approximation: A new approximation method to accelerate the simulation process of enzymatic reactions Journal of Computational Biology, September 1, 2008, 15(7): 779-792. doi:10.1089/cmb.2007.0135.
[.pdf]

H. Kuwahara, C. Myers, M. Samoilov, N. Barker, and A. Arkin. Automated abstraction methodology for genetic regulatory networks. Trans. on Comput. Syst. Biol. VI, pages 150-175, 2006. Invited.
[ .pdf ]

H. Kuwahara and C. Myers. Production-passage-time approximation: A new approximation method to accelerate the simulation process of enzymatic reactions. Iin Eleventh Annual International Conference on Research in Computational Molecular Biology, 2007 (RECOMB 2007).
[ .pdf ]

N. Nguyen, H. Kuwahara, C. Myers, and J. Keener. The design of a genetic muller C-element. In The 13th IEEE International Symposium on Asynchronous Circuits and Systems, 2007 (ASYNC 07) Best Paper Award.

H. Kuwahara, C. Myers, and M. Samoilov. Abstracted stochastic analysis of type 1 pili expression in E. coli. In The 2006 International Conference on Bioinformatics and Computational Biology, 2006.
[ .pdf ]

With the aid of model abstractions, biochemical networks can be analyzed at different levels of resolution: from low-level quantitative models to high-level qualitative ones. Furthermore, an ability to change the level of abstraction can be very useful when dealing with many biological systems, including gene regulatory networks. These systems typically have too many components and states to be practically studied using all-inclusive low-level models, yet they often manifest enough dynamical and functional complexity, making an entirely high-level qualitative representation similarly inadequate - thus necessitating a search for some intermediate level of abstraction. Finally, while most abstractions used in modeling of biochemical networks have traditionally been performed manually, doing so accurately in a large system is a tedious and time-consuming process that is highly susceptible to errors during model transformation. To address these issues, we have developed a methodology and implemented an automated modeling and analysis tool with variable abstraction level capabilities. In this paper, we use it for the analysis of switching in Type 1 pili expression dynamics and, in particular, for the problem of estimating the effect of H-NS and Lrp regulatory protein levels on phase variation rates in E. coli. Such behavior is notoriously difficult to study due to the size of the associated gene regulatory network and the characteristically stochastic dynamics involved, which result in very high analytical and computational demands. Here, we show how, by using our system, we are able to automatically abstract the switch network and accurately predict E. coli afimbriation rates, while, at the same time, accelerating the required computations by up to two orders of magnitude.

N. Barker, C. Myers, and H. Kuwahara. Learning genetic regulatory network connectivity from time series data. In The 19th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, 2006.
[ .pdf ]

H. Kuwahara, C. Myers, N. Barker, M. Samoilov, and A. Arkin. Asynchronous abstraction methodology for genetic regulatory networks. In The Third International Workshop on Computational Methods in Systems Biology, 2005.
[ .pdf ]

In order to efficiently analyze a large scale system in an automated and objective manner, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small scale complexity found in genetic regulatory network models, while broadly preserving the large scale system behavior. Our approach is to first reduce the number of reactions through a quasi-steady-state approximation-based algorithm. Second, it represents the exact molecular state of the system by a set of reduced Boolean (or n-ary) discrete levels. This results in a chemical master equation that is approximated by a Markov chain with a much smaller state space providing significant simulation time acceleration and computability gains.

H. Kuwahara, C. Myers, N. Barker, M. Samoilov, and A. Arkin. Automatic abstraction methodology for genetic regulatory networks. In The 1st Annual Mountain West Biomedical Engineering Conference, 2005.

N. Barker, C. Myers, and H. Kuwahara. Learning genetic regulatory network connectivity from time series data. In The 1st Annual Mountain West Biomedical Engineering Conference, 2005.