Modeling Protein Interactions and Assembly with Single-Particle Reaction-Diffusion in Solution and the Membrane
February 23, 2015
Dr. Margaret Johnson
We recently developed a free-propagator reweighting (FPR) algorithm that solves reaction-diffusion (RD) dynamics of systems at single particle resolution both accurately and efficiently. The full spatio-temporal resolution of this RD method allows us to model complex biological systems where details like spatial gradients or particle assembly render simple rate-based kinetics insufficient. The treatment of proteins at single-particle resolution also allows us to begin building in further molecular details into the physics of the binding interactions, such as multiple domains in proteins, rotational and orientational effects, and interaction potentials. We have recently used our method to quantify the effect of membrane recruitment in altering the equilibrium and time-scales of protein binding interactions relative to their behavior in solution, which has implications for understanding the mechanisms of clathrin-coat formation in the early stages of endocytosis. Finally, we have used our RD simulations and new theoretical results to characterize the limitations of modeling reactions dynamics in 2D using simple rate-based kinetics. The use of rate equations assumes a well-mixed system over the lengthscale of the (sub)volume being modeled, and additionally assumes a single rate-constant parameterizes association between binding partners. In 2D, however, the second assumption of a single characteristic rate constant is not generally true. Understanding when this approximation breaks down is critical both for accurate simulations and for robust parameterization of experimental binding data. We determine in what regimes a single rate is appropriate, and when additional parameterization is necessary. Because the use of rate equations provide an efficient and widely used tool for simulating reaction dynamics, we introduce a concentration dependent rate constant in 2D as an approximate representation of the more microscopic dynamics dependent on the multi-parameter model.
Neuromorphic Applications And Neurorobotics: A Large-Scale Cortical Model For Visually Guided Navigation
March 2, 2015
Jeffrey L. Krichmar
March 16, 2015
Dr. Michael A. Burman,
Fear and anxiety disorder have a lifetime incidence of over 25% of the population. Although the neural circuitry involved in fear conditioning in mature organisms is well understood, the development of these circuits is less well studied. However, the extant literature does suggest that the third and forth weeks of life in rodents appear to be a time of significant change in both the cognitive and behavioral mechanisms of fear as well as the underlying neurobiology. The current experiments further examine the behavioral and neurological changes that occur during this period, with a focus on medial temporal lobe cortex, the hippocampus and the amygdala. Here, I will discuss two sets of experiments. The first examines ontogeny of contextual fear conditioning by separating the contextual and aversive learning. These experiments will conclude that some aspects of hippocampus-dependent learning may be occurring earlier than previously believed. The second set of experiments examines immediate early gene (IEG) expression in the amygdala, hippocampus, perirhinal cortex, and hypothalamus during auditory and contextual fear conditioning and expression. These studies will suggest that the amygdala and perirhinal cortex are likely sites of continuing development during the periweaning period.
March 23, 2015
Dr. Eric Betzig
As our understanding of biological systems as increased, so has the complexity of our questions and the need for more advanced optical tools to answer them. For example, there is a hundred-fold gap between the resolution of conventional optical microscopy and the scale at which molecules self-assemble to form sub-cellular structures. Furthermore, as we attempt to peer more closely at the dynamic complexity of living systems, the actinic glare of our microscopes can adversely influence the specimens we hope to study. Finally, the heterogeneity of living tissue can seriously impede our ability to image at high resolution, due to the resulting warping and scattering of light rays. I will describe three areas focused on addressing these challenges: super-resolution microscopy for imaging specific proteins within cells down to near-molecular resolution; plane illumination microscopy using non-diffracting beams for noninvasive imaging of three-dimensional dynamics within live cells and embryos; and adaptive optics to recover optimal images from within optically heterogeneous specimens.
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