Dynamic Modeling of Molecular Motor Association with Axonal Amyloid Precursor Protein Vesicles

Mano Ram Maurya¹, Lukasz Szpankowski^1,2, Lawrence S.B. Goldstein³, Shankar Subramaniam⁴

ABSTRACT

            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 priori, 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 STC-0939370.

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

 

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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) 822-3752.