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

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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
Cell Division: Mechanical Integrity with Dynamic Parts

12pm, Glandt Forum

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While we have made remarkable progress unraveling the mechanics of individual molecules, our understanding of how they give rise to those of larger scale biological structures is still poor. A big challenge is that unlike the macroscopic structures that we commonly engineer, biological structures are dynamic and self-organize. Our lab asks: how do the mechanics of molecules give rise to those of the large macromolecular machines that power cell division? To segregate chromosomes, the spindle must be flexible and dynamic, and yet must robustly maintain its mechanical integrity and function. How it does so is not understood. I will describe our current efforts to understand how a dynamic spindle bears the load of chromosome movement, how it monitors and maintains its core architecture, and how it robustly attaches to chromosomes. In doing so, I will describe approaches we are developing to measure spindle forces inside cells, rewire spindle architecture, and control the attachment of chromosomes to the spindle. I will highlight examples of how simple molecular-scale design mechanical features can lead to robust cellular function.

 

December 8
Manu Prakash
TBA

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TBA

January 18
Sydney Shaffer
Cellular Memory and Rare Cell Variability in Cancer

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Targeted therapies for cancer are a promising class of drugs that inhibit the specific molecular alterations that underlie the uncontrolled proliferation seen in cancer. The primary shortcoming of targeted therapy is disease relapse, which is driven by a subpopulation of cells that are resistant to these drugs. This phenomenon is generally thought to be genetic in origin; however, our recent work on melanoma shows that non-genetic cellular plasticity may provide a mechanism of resistance to these therapies. Furthermore, we showed that through the addition of the drug itself, cells transition from this transient plasticity into a new, stably resistant cell state via cellular reprogramming, suggesting that the time an individual cell exists in a state is important for producing the divergent resistance phenotype. However, there are currently no methods available to quantify the timescale of these fluctuations for the whole transcriptome. Thus, broadly generalizing this concept of timescales for cellular plasticity, we developed a novel method for genome-wide quantification of the timescales of gene expression memory based on a modern version of the ingenious Luria-Delbrück fluctuation analysis. In melanoma, this method revealed the gene expression state of rare cells resistant to targeted therapy. In a completely new model, triple negative breast cancer, this method revealed a novel rare subpopulation of cells exhibiting resistance to chemotherapy. More generally, this method has the potential to reveal other new phenotypes associated with rare cell biology including metastasis and the early stages of stem cell differentiation. Taken together, our findings in melanoma and novel methods for studying cellular plasticity outline a framework for using single-cell technologies for applications in basic biology and clinical medicine.

January 25
Kirsten Frieda
Seeing Cell Histories with MEMOIR

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Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. However, many biological processes occur in optically opaque or physically inaccessible contexts such that they cannot be directly imaged. To overcome these challenges, we have developed a synthetic system for lineage tracking and event recording called MEMOIR. This system enables cells to store information in their genomes’ in a format that can later be read out in situ in single cells by seqFISH. We demonstrate how this technology can be used to infer lineages based on shared mutations and how it is compatible with other same cell measurements. MEMOIR can thus provide a wealth of historical information about individual cells in their native spatial environments and is applicable across diverse biological systems.


   
 

February 1
Rosalind Picard
TBA

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TBA

 

April 19
Viviana Gradinaru
TBA

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TBA