(72h) Dynamic Modeling of Molecular Motor Association with Axonal Amyloid Precursor Protein Vesicles | AIChE

(72h) Dynamic Modeling of Molecular Motor Association with Axonal Amyloid Precursor Protein Vesicles


Maurya, M. R. - Presenter, University of California San Diego
Subramaniam, S. - Presenter, University of California, San Diego

Mano Ram Maurya1, Lukasz Szpankowski1,2, Lawrence S.B. Goldstein3, Shankar Subramaniam4


            Targeted transport of
vesicles, organelles, and other types of intracellular cargo along microtubule
tracks is powered by kinesin and cytoplasmic dynein molecular motors.  Both
classes of motors can attach to the same cargo and thus their binding kinetics decides
the distribution of the cargo with these motors. In particular, we have been
studying the binding of these motors to amyloid precursor protein (APP)
vesicles in mouse hippocampal axons. It has been shown experimentally that several
units of each of kinesin and dynein proteins can be bound to APP vesicles. In
the present work, we have developed a 16-state biochemical reaction
network-based model to capture the dynamics of binding of kinesin and dynein to
APP vesicles. Each state represents an APP vesicle with a specified number of associated
kinesin and dynein motors, up to a maximum of three units for each. A set of nonlinear
coupled ordinary differential equations were generated describing the dynamic
mass-balance based on the reaction rates for association/disassociation of each
motor species. The system of differential equations was solved to compute the
steady-state contribution of each axonal APP vesicular cargo associated with a defined
arrangement of molecular motors. Since the rate parameters were not known a
, the endogenous association/dissociation rate of both kinesin and
dynein on APP vesicles was estimated through a nonlinear optimization
(data-fitting) approach. The resulting model predicted the dynamics in under-
and over-expression studies, has been validated against experimental data in
heterozygous knockout kinesin mutants, and facilitates prediction of transport
dynamics in systems not amenable to experimental manipulation. Furthermore,
the systems biology approach used here provides a framework to build detailed
predictive kinetic models to characterize the molecular states using
macro-level data, which can then be used to generate and test hypotheses leading
to the design of novel experiments and further refinement of such models.

Acknowledgements: We would like to
acknowledge the National Science Foundation (NSF) collaborative grant

Key words: mechanistic dynamic
modeling, parameter estimation, systems biology, vesicle transport, kinesin,

1 Equal effort.

2 Current address: Fluidigm
Corporation, 7000 Shoreline Court, Suite 100, South San Francisco, CA 94080.

3 Corresponding author. E-mail: lgoldstein@ucsd.edu, Phone: (858)
534-9700, Fax: (858) 246-0162.

4 Corresponding author. E-mail: shankar@ucsd.edu, Phone: (858) 822-0986, Fax: (858)