*Funded 2016-2020 through NIH-NIGMS. PI with [Will Hancock](https://sites.psu.edu/hancocklab/) (Penn State), John Fricks (Arizona State), and Pete Kramer (RPI). Also funded 2018-2024 through the NSF/Simons Southeast Center for Mathematics and Biology. Senior personnel affiliated with the center and collaborating with Christine Payne (Duke). PI \& Director was Christine Heitsch (Ga.~Tech).* Many biomaterials (*intracellular cargo*) are transported throughout biological cells along filaments called *microtubules* by proteins called *molecular motors*. Microtubules are polarized, with a plus-end and a minus-end, and different motors are responsible for transporting cargo toward the different ends. Broadly speaking, kinesin motors move toward plus ends and dynein motors move toward minus ends. ![[Motor-Cargo Dixit.png|center|400]] ^{Cartoon of axonal transport from Dixit et al, 2008[^dixit2008] speculating on the impact of microtubule associated proteins on the distribution of intracellular cargo.} Individual intracellular cargoes, like organelles or vesicles, are likely to be bound to multiple molecular motors at all times. Meanwhile, one or more of these motors will be simultaneously bound to a microtubule, applying multiple forces to the cargo, resulting in motion that switches between bidirectional ballistic motion and interspersed paused states. It is natural to ask why all of this transport apparatus is needed. Why do cells not just rely on diffusion alone? One answer is that most cargo is produced in the cell nucleus and, relative to scale, the cargo must traverse tremendous distances before getting to the location where they are needed. The analogy goes like this: each motor step is approximately 10 nm; if we analogize this to a human step of being approximately 1 m, then the distance traveled by intracellular cargo can be the equivalent of traversing an entire US state by foot. In terms of transporting material, relying on diffusion would take far too long. The goal of mathematical modeling is to develop analytical tools to describe and make predictions for scenarios like the one described by [Dixit et al (2008)](https://www.science.org/doi/full/10.1126/science.1152993)[^dixit2008] (depicted in the figure above). The axons of neurons are long with microtubules that are aligned and have near-uniform polarity with plus-ends facing out toward the synapse. This means that kinesin motors tend to bring cargo to the synapse while dynein motors bring cargo toward the cell body. Imagine there is a protein that inhibits kinesin binding throughout the cell. Then material cannot make its full trip from the nucleus to the distal end of the cell, leading to cell dysfunction. > In principle, a *multi-scale mathematical model* should be able to relate microscale binding, unbinding, and stepping rates of motors to predict the overall distribution of cargo in the cell. My collaborator [Will Hancock](https://sites.psu.edu/hancocklab/) noticed across the experimental literature that whenever experimentalists knocked out one motor, rather than enhancing transport in the opposite direction, transport decreased overall. He termed this *the paradox of co-dependence among antagonistic motors* and this formed the basis of our multi-year collaborative grant project. ~~ More to come ~~ [^dixit2008]: Dixit, R., Ross, J. L., Goldman, Y. E., & Holzbaur, E. L. (2008). [Differential regulation of dynein and kinesin motor proteins by tau.](https://www.science.org/doi/full/10.1126/science.1152993) _Science_, _319_(5866), 1086-1089.