BE Seminars & Events

Current Seminar Series: 2017-2018

Bioengineering Seminars are held on Thursdays at 12PM in 337 Towne Building unless otherwise noted below. For all Penn Engineering events, visit the Penn Calendar.

October 19
Amina Ann Qutub
Neural Cell Communication During Growth & Regeneration

12 PM

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Decoding how neurons form can advance our understanding of how the brain makes new memories, degenerates in disease, and self-repairs after injury. Neural differentiation appears species-specific, and single neurons can take on unique functions. These observations make it critical to study neural differentiation in human cells at the single cell level. However the differentiation process is immensely complex, and dependent on spatial and temporal cues from neighboring cells. Neural progenitor cells transform into neurons through intricate coordination of chemical, mechanical and electrical cell-cell communication. While studies have focused on one or two of these modes of communication, how the three are interconnected: chemical signaling, spatial patterning and electrical activity, has yet to be well understood.

My overall research vision is to understand the principles guiding this coordinated neural cell communication in order to improve human health. To accomplish this, my lab develops methods in data science, multiscale modeling, and live imaging that allow us to identify how changes at the molecular level affect single cell dynamics and cell-cell interactions – and ultimately how cellular dynamics impact or signal changes in health. The work introduced in this talk will illuminate not only fundamental questions about how neural cells switch their modes of communication to form electrically functional neuronal networks, it also has the potential to identify new markers of neurological diseases and mechanisms underlying brain repair.

November 9
Dr. Srinivas Turaga, HHMI Janelia Research Campus
From Biological Neural Networks to Artificial Neural Networks

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In this talk, I will describe how we developed deep learning based computational tools to solve two important problems in neuroscience: predicting the activity of a neural network from measurements of its structural connectivity, and inferring the connectivity of a network of neurons from measurements and perturbation of neural activity.

1. Are measurements of the structural connectivity of a biological neural network sufficient to predict its function? We constructed a simplified model of the first two stages of the fruit fly visual system, the lamina and medulla. The result is a deep hexagonal lattice convolutional neural network which discovered well-known orientation and direction selectivity properties in T4 neurons and their inputs. Our work is the first demonstration, that knowledge of neural connectivity can enable in silico predictions of the functional properties of individual neurons in a circuit, leading to an understanding of circuit function from structure alone.

2. Can we infer neural connectivity from noisy measurement and perturbation of neural activity? Population neural activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect. We built on recent advances in variational autoencoderes to develop a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. Our model produces excellent spike inferences at 20K times real-time, and predicts connectivity for mouse primary visual cortex which is consistent with known measurements.

November 16
Grace Hopper Lecture Series
Claudia Fischbach
Engineering Approaches to Study Emerging Roles of ECM Dynamics in Cancer
10:30am, Wu & Chen Auditorium

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 Microenvironmental conditions contribute to the pathogenesis of cancer, and aberrant extracellular matrix (ECM) remodeling plays a key role in this process. However, our understanding of the specific mechanisms by which the ECM promotes cancer is relatively limited. Our lab focuses on the integration of materials science, tissue engineering, and cancer biology approaches to test the role of ECM biological and physical properties in cancer initiation and progression. More specifically, we characterize the effect of tumors on ECM composition, structure, and mechanics and investigate the relevance of these changes to tumor cell behavior both in vitro and in vivo. Additionally, we are evaluating whether obesity, a condition commonly associated with an increased risk and worse clinical prognosis for cancer, may promote tumorigenesis by mimicking tumor-like ECM dynamics.

 

December 7
Sophie Dumont
TBA

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TBA

 

December 8
Manu Prakash
TBA

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February 1
Rosalind Picard
TBA

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April 19
Viviana Gradinaru
TBA

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TBA