Abstracts Keynote & Plenary

Wensheng Bian, Jianwei Cao, Haitao Ma, Xiaojun Liu, Chunfang Zhang
Institute of Chemistry, Chinese Academy of Sciences.
Zhongguancun North First Street 2,100190 Beijing, P. R.
China.
Conical intersections (CIs) play a key role in electronically nonadiabatic
processes [1]. From gas
phase
to condensed phase, from small systems with three or four atoms to biomolecules such as DNA
and protein, the ultrafast nonadiabatic processes induced by CIs are ubiquitous. For example, in the
study of green fluorescent protein, as long as very short excited-state lifetime is observed, it is always
attributed to an ultrafast internal conversion resulting from CI. The C(1D)H2 system exhibits
interesting nonadiabatic dynamical features, including Renner-Teller effects and multiple conical
intersections. It thus serves as an important prototype for ultrafast nonadiabatic processes. CIs and
geometric phase effects among the 11A', 21A', 31A', 11A'', and 21A'' states are carefully investigated
and valuable insights are obtained.
Molecular dynamics simulations have become a powerful tool for the study of biological
macromolecules. On the other hand, classical mechanics- based methods have been shown to be
very useful in elucidating detailed atomic-level mechanisms. Here, quasiclassical trajectory (QCT)
calculations are performed to reveal new atomic-level mechanisms of polyatomic abstraction and
exchange reactions. The accurate global ab initio potential energy surface constructed by us [3] is
used, which describes both the H+SiH4 abstraction and exchange reactions. Our QCT studies
reveal interesting features of detailed dynamical quantities and underlying new reaction
mechanisms. We designate the new mechanisms for exchange found by us as torsion-tilt and
side-inversion. The abstraction reaction is shown to be a combination of rebound and stripping.
These findings are important for acquiring a deeper understanding of reaction dynamics.
[1] X.
Liu, W. Bian, X. Zhao, X. Tao, J. Chem. Phys. 2006, 125, 074306.
[2] M. Wang, X. Sun, W. Bian, J. Chem. Phys. 2008, 129, 084309.
[3] J. Cao, Z. Zhang, C. Zhang, K. Liu, M. Wang, W. Bian,
Proc. Natl. Acad .Sci. U.S.A. 2009, in press (doi:10.1073/pnas.0903934106).
PL-002
Towards Predictive
Stochastic Dynamical Modeling of Cancer Genesis and Progression
P. Ao
Laboratory of Systems Biomedicine of Ministry of
Washington, Seattle, WA 98195,
te for Systems Biology, 1441 N. 34 St., Seattle, WA 98103, USA.
1,2,
D. Galas 3
, L. Hood 3
, L. Yin 4
, X.-M. Zhu 5
1 Shanghai Center for Systems Biomedicine, Key
Education, Shanghai Jiao Tong University, Shanghai 200240, China
2 Departments of Mechanical Engineering and Physics, University of
USA
3 Institu
4 School of Physics, Peking University, Beijing 100871, China
5 GenMath, Corp. 5525 27th Ave.N.E., Seattle, WA 98105, USA.
Here we wish to advance an evolutionary and stochastic dynamics formulation of carcinogenesis.
The novel biological hypothesis behind such formulation has been stated in our previous
publication [1]: Cancer as an intrinsic robust state of the endogenous network not optimized for
the interest of whole organism. More explicitly, the molecular and cellular agents, such as
oncogenes and suppressor genes, and related growth factors, hormones, cytokines, etc, form a
nonlinear, stochastic, and collective dynamical network, the endogenous molecular‐‐cellular
network. This endogenous network may be specified by the expression or activity levels of a
minimum set of endogenous agents, resulting in a highdimensional stochastic dynamical system.