Name: Julie Cass
Lab Information: Daniel Lab
Title: Sarcomere breathing: does flow within contracting myofibrils influence substrate delivery?
Abstract:
Sarcomeric contractions with constant lattice spacing necessitate flow to maintain conservation of mass as the sarcomere shortens in its axial direction. These contractions are driven by (and therefore depend on) ATP hydrolysis. However, the availability of ATP to the sarcomere is limited by diffusion in the crowded space of the lattice of thick and thin filaments. We asked whether the flow resulting from periodic contractions influences ATP availability in the sarcomere. Drawing on published estimates of diffusion rates and enzyme kinetics, we modeled the substrate delivery to the interior of myofibrils for the diffusion reaction convection equation. Despite the linearity and reversibility of the contraction-driven low Reynolds number flow, we found that convection has non-linear effects on the resulting substrate availability. Our models predict that flow improves ATP concentrations in the sarcomere over diffusion alone by during early contraction cycles, suggesting that flow plays an important role in sarcomere energetics.alone by during early contraction cycles. We compared the asymptotic effects of flow after many cycles, integrated over the cross section at the m-line, and found that convection consistently enhances ATP consumption and substrate availability, suggesting that flow plays an important role in sarcomere energetics.
Intro:
Julie obtained her PhD in Paul Wiggin's lab at UW Physics, using quantitative image analysis to study bacterial chromosome segregation dynamics. She joined the Daniel Lab in 2017 to develop mathematical models for muscle energetics, using differential equations of transport.
Name: David Grossnickle
Lab: Greg Wilson
Title: Jaw rule: mammalian jaw morphologies correlate with diet and evolve toward trait optima
Abstract:
Although studies commonly examine correlations between tooth shape and diet using diverse mammalian samples, comparable analyses of jaw morphologies and diet across Mammalia are rare. This is surprising because mandibular shape may offer considerable insight into the diets and evolutionary histories of mammals, including fossil lineages. I test the correlation between jaw shape and diet by applying phylogenetic comparative methods to jaw measurements and dietary information for 200 modern mammalian species. Results indicate that the distance between the jaw joint and angular process is an especially powerful predictor of diet, increasing with greater herbivory. This distance reflects the attachment area sizes of muscles that are particularly important for mastication of plant materials. Further, I compare the fit of multiple evolutionary models to the morphological data, finding strong support for the presence of unique selective regimes associated with herbivory and carnivory. Thus, this study presents novel data concerning mammalian jaw correlates of diet and offers new evidence on mammalian macroevolutionary patterns.
Intro:
Dave obtained his PhD in evolutionary biology at the University of Chicago, studying under Zhe-Xi Luo and Ken Angielczyk. His research focuses on macroevolutionary patterns and functional morphologies of early mammals. He recently joined Greg Wilson’s lab to investigate mammalian evolution across the Cretaceous-Paleogene mass extinction event.
Name: Kameron Decker Harris
Lab information: Working in the lab of Bing Brunton and Raj Rao on ECoG recordings, along with analysis techniques for these and other neuroscience data.
Title: Optimal synaptic connectivity
Abstract:
I will talk about my recent paper which examined a couple of similarly structured but different brain circuits. Synaptic connectivity varies widely across neuronal types: cerebellar granule cells receive five orders of magnitude fewer inputs than the Purkinje cells they innervate. Similar circuits, including the insect mushroom body, also exhibit large divergences in connectivity. In contrast, the number of inputs per neuron in cerebral cortex is more uniform and large. We investigated how the "dimension" of a representation in a population of neurons depends on how many inputs each neuron receives and how this affects associative learning. Our theory predicts that the dimensions these representations are maximized at synaptic connectivities which match those observed anatomically, showing that sparse connectivity is sometimes superior to dense connectivity. When input synapses are subject to supervised plasticity, however, dense wiring becomes advantageous. This is a possible explanation for the differences between "cerebellar" and cortical structures.
