Publications by authors named "Christina Warrender"

The exponential increase in available neural data has combined with the exponential growth in computing ("Moore's law") to create new opportunities to understand neural systems at large scale and high detail. The ability to produce large and sophisticated simulations has introduced unique challenges to neuroscientists. Computational models in neuroscience are increasingly broad efforts, often involving the collaboration of experts in different domains.

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Intracellular molecular machines synthesize molecules, tear apart others, transport materials, transform energy into different forms, and carry out a host of other coordinated processes. Many molecular processes have been shown to work outside of cells, and the idea of harnessing these molecular machines to build nanostructures is attractive. Two examples are microtubules and motor proteins, which aid cell movement, help determine cell shape and internal structure, and transport vesicles and organelles within the cell.

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Infection with Mycobacterium tuberculosis (Mtb) is characterized by localized, roughly spherical lesions within which the pathogen interacts with host cells. Containment of the infection or progression of disease depends on the behavior of individual cells, which, in turn, depends on the local molecular environment and on contact with neighboring cells. Modeling can help us understand the nonlinear interactions that drive the overall dynamics in this system.

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In this paper, we use both mathematical modeling and simulation to explore homeostasis of peripheral immune system effector cells, particularly alveolar macrophages. Our interest is in the distributed control mechanisms that allow such a population to maintain itself. We introduce a multi-purpose simulator designed to study individual cell responses to local molecular signals and their effects on population dynamics.

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