Bulletin of the American Physical Society
2017 Annual Meeting of the APS Mid-Atlantic Section
Volume 62, Number 19
Friday–Sunday, November 3–5, 2017; Newark, New Jersey
Session E1: From living matter to the brain |
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Chair: Kathleen McEnnis, New Jersey Institute of Technology Room: 235, Campus Center, NJIT |
Saturday, November 4, 2017 10:00AM - 10:36AM |
E1.00001: Self-driven phase transitions in living matter Invited Speaker: Joshua Shaevitz The soil dwelling bacterium {\it Myxococcus xanthus} is an amazing organism that uses collective motility to hunt in giant packs when near prey and to form beautiful and protective macroscopic structures comprising millions of cells when food is scarce. I will present an overview of how these cells move and how they regulate that motion to produce different phases of collective behavior. Inspired by recent work on of active matter, I will discuss experiments that reveal how these cells generate nematic order and how they actively tune the P\'eclet number of the population to drive a phase transition from a gas-like flocking state to an aggregated liquid-droplet state during starvation [Preview Abstract] |
Saturday, November 4, 2017 10:36AM - 10:48AM |
E1.00002: Interplay of physical constraints and chemotaxis in fibroblasts' directional decision Quang Long Pham, David Chege, Timothy Dijamco, roman s Voronov Fibroblast migration in engineered tissue pores depends on physicochemical balance between physical constraints and chemotactic signals transported via diffusion. Herein, the interplay of the two factors in directing the chemotaxis of individual fibroblasts in tissue-like micropores was examined. We employed two microfluidic models: (1) an array of straight channels of different widths (12-75 \textmu m), (2) a bifurcation of two channel widths (15 and 45 \textmu m). Cell velocity and direction were assessed using the 1st model while the directional decision was studied using the 2nd. Diffusive chemical gradient was modeled with flow-free transport physics. In the presence of a stable gradient, cells migrated steadily from the sink to the source of chemoattractant. Interestingly, migration speed is independent of the channel size. When cells reach a bifurcation of similar chemical gradient, they explored both directions by two leading edges but then retract from the smaller channel. Over 95{\%} of the time, they selected fatter channel. When encountering a bifurcation with a high gradient in the small channel and low in the large one, cells biased to small channel, suggesting that directional choice was dominated by chemical signal, not by physical constraints. [Preview Abstract] |
Saturday, November 4, 2017 10:48AM - 11:00AM |
E1.00003: Harnessing uncertain data for structure prediction of proteins and peptide binding Alberto Perez Physics based simulations based on Molecular Dynamics have long held the promise to produce accurate free energies, pathways, mechanisms and structures by filling in the {\AA}ngstrom by {\AA}ngstrom and picosecond-to-picosecond details that cannot be observed experimentally. In practice, such calculations have been too computationally expensive but for the smallest systems . We have developed an advanced sampling technique called MELD (Modeling Employing Limited Data) that can harness problematic data (noisy, ambiguous or sparse) coming from different sources (experiment or general knowledge). We have used it to accurately predict the structures of several proteins in a blind test event called CASP and to predict binding poses and relative binding free energies of p53 derived peptides to the proteins MDM2 and MDMX. During this talk I will present an overview of the methodology and its applications. [Preview Abstract] |
Saturday, November 4, 2017 11:00AM - 11:12AM |
E1.00004: Modeling the micro-dynamic environment of tissue scaffolds via representative volume and full domain Lattice-Boltzmann Method simulations Femi Kadri, Cortes Williams, Vassilis Sikavitsas, Roman Voronov In tissue engineering, stresses generated by culture media flow through scaffolds play an important role in stimulating cell/tissue growth. Unfortunately, scaffolds have complex internal structures which make experimental stress calculation difficult. Rather,~computational models based on scaffold geometries are utilized. However,~simulating~these models is~also~computationally-expensive. Therefore,~approximations based on~smaller representative volumes (RV)~obtained from full scaffolds are~often~used. Although this approach saves computation time and bypasses the inherent complexity of simulating large domains,~simulation outcomes can be very different from full geometry simulations due to smaller domain and inconsistent use of boundary conditions implemented by both approaches. Since there is no established guide or reference for estimating the error associated with the RV approximation, this study attempts to fill that void. To achieve this, Lattice-Boltzmann method is used to simulate fluid flow through 3D reconstructions of \textmu CT scaffold images for both RV and full scaffolds. Wall shear stress estimates obtained in both cases are~compared~for scaffolds~of varying pore sizes~and porosities. Results show that while both approaches generate significantly different flow fields and localized shear stresses, mean shear stresses estimates are of same magnitude. [Preview Abstract] |
Saturday, November 4, 2017 11:12AM - 11:48AM |
E1.00005: How the brain escapes the curse of dimensionality Invited Speaker: Dmitri Chklovskii Our visual system recognizes objects so effortlessly that we tend to overlook the computational complexity of the task. In fact, object recognition requires classifying images, which can be viewed as data points in the very high-dimensional space of all possible images. If the probability density of images were uniform over this space such task would be exponentially hard in the number of dimensions (pixels in an image). Object recognition is tractable only because the probability density of natural images concentrates on locally low-dimensional structures called manifolds. However, the mechanism with which the brain learns such manifolds is unknown. Here, we propose a theory for learning data manifolds using biological hardware. We postulate a principled objective function and derive an optimization algorithm implemented by fast (electrical activity) and slow (synaptic plasticity) dynamics in neural networks. Our theory is a step towards understanding the brain and building artificial intelligence. [Preview Abstract] |
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