Collective Behaviour of Molecular Transport Motor Systems

Motor-Cargo Dynamics
Pareto Frontier

With Professor David Sivak

Within the cells in every plant and animal on earth, countless microscopic molecular machines are in constant motion. These machines convert between different types of energy, transport materials, and assemble complex structures. The behaviour of molecular machines, unlike the everyday machines we’re used to interacting with, is characterized by random fluctuations — they do useful work only on average. My research is focused on understanding these machines; both why they’ve evolved the way they have, and how we might go about designing better ones in the future.

Related Publications:

Collagen Fibrils: Growth, Structure, and Mechanical Properties

Fibril Cartoon
Cross-Link Cartoon

With Professors Andrew Rutenberg and Laurent Kreplak

Collagen is the single most abundant protein in the human body. Individual collagen molecules spontaneously self-assemble in vivo into fibrils: long molecular ropes which are a key structural component in many different animal tissues (the prototypical example is tendons, but collagen fibrils can also be found in tissues such as skin, bone, cartilage, and cornea). Within these fibrils collagen molecules are highly ordered, leading to microscopically visible features such as periodic axial density modulations and high angular surface twist. To understand the self-assembly of collagen fibrils in vivo we derived a nonequilibrium growth model for cross-linked fibrils, which predicts internal structure and radius control mechanisms consistent with experimental observations. We also extended results from the neoclassical theory of nematic rubber elasticity to the double-twist molecular director field of collagen fibrils in order to model the effects of applied strain on the complex macromolecular structures predicted by our growth model.

Related Publications:

Stochastic modelling of cellular Salmonella infection

Infection Cartoon

With Professor Andrew Rutenberg

Salmonella bacteria are a prototypical labaratory pathogen exhibiting a particularly interesting behavioural trait: the ruffling mechanism. To better understand the impact of the ruffling on the statistics and dynamics of bacterial invasion, we developed an agent-based stochastic model for invasion of host cells by Salmonella bacteria. Using this model we were able to explain recent experimental data, determine environmental conditions under which stochastic effects are most significant, and gain quantitative insight into cooperative invasion behaviour.