Penn Engineering Events

CBE PhD Dissertation Defense | Polymer Mechanics and Dynamics in Polymer Nanoparticle Composites
2020-11-09 13:00 - 15:00 (America/New_York) | Zoom – Email CBE for link
Abstract:
“Polymer nanoparticle composites (PNCs) have become an important topic of research due to their highly tunable macroscopic properties. Compared to the pure polymers, PNCs exhibit increase in mechanical strength, altered thermodynamic properties, and simultaneous improvement in permeability and selectivity in small molecule transport. Understanding the fundamental physics that control the behavior of both components in the PNCs can provide insights to the material design. In PNCs, the microscopic variations often dominate the behavior at the macroscopic level. Previous research has shown significant deviations in both polymer conformation and dynamics near the nanoparticle (NP) surfaces from bulk polymers. However, the heterogeneous nature of the PNCs makes understanding microscopic details using macroscopic experiments a difficult task. Therefore, computational methods have been employed to investigate the polymer conformation, mechanics, and dynamics at the molecular level. In this dissertation, I use PNCs with two levels of NP loading to investigate the origins of the various changes in properties. First, I use molecular dynamics (MD) simulations to examine a class of PNCs with ultra-high NP loading, in which the volume fraction of NPs is near the random-close-pack limit ( >50% ). This class of PNCs can be produced with either partial or complete filling of polymers. In these PNCs, the polymer chains are highly confined due to the large number of NP surfaces, thus are drastically altered in their conformation and dynamics. The second PNC system studied in this dissertation has a dilute amount of well-dispersed NPs to avoid NP-NP interaction and polymer confinement. In this case, I use both MD simulations and classical density functional theory (cDFT) for fluids to understand the role of NP-polymer interactions, solid curvature, and polymer molecular weight.”

PSOC Webinar: “Chromatin motion and DNA repair” (Pierre-Alexandre Vidi)
2020-11-09 12:00 - 13:00 | PA
“Chromatin motion and DNA repair”
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu

Doctoral Dissertation: "Iron Imaging in Myocardial Infarction Reperfusion Injury" (Brianna Moon)
2020-11-09 11:30 - 12:30 | PA
The Department of Bioengineering at the University of Pennsylvania and Dr. Walter Witschey are pleased to announce the Doctoral Dissertation Defense of Brianna Moon. The Defense will take place via Zoom. Contact moonbri@seas.upenn.edu for the link and passcode.

Title: Iron Imaging in Myocardial Infarction Reperfusion Injury
Date: November 9th, 2020
Time: 11:30am EST

PICS: "Python Workshop"
2020-11-07 10:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The Penn Institute for Computational Science (PICS) will be hosting a Python workshop on Saturday, November 7 from 10:00am – 3:30pm via Zoom. This course is designed to be an introduction to programming in Python. In this workshop you will learn to write clean, readable, and fast Python code with a focus on graphics-based programming.
In order to attend please click here and RSVP. The workshop will be capped at 30 people. Students enrolled in the PICS Certificate program will have priority enrollment.

SIG Seminar:"Computational Design for the Next Manufacturing Revolution"
2020-11-06 13:00 - 14:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Abstract: 3D printers are radically transforming the aerospace and automotive industries. Whole-garment knitting machines allow the automated production of complex apparel and shoes. Manufacturing electronics on flexible substrates enable a new range of integrated products for consumer electronics and medical diagnostics. These advances demonstrate the potential for a new economy of on-demand production of objects of unprecedented complexity and functionality. In my talk, I argue that the field of computational design is essential for the next revolution in manufacturing. To build increasingly functional, complex and integrated products, we need to create design tools that allow their users to efficiently explore high-dimensional design spaces by optimizing over a set of performance objectives that can be measured only by expensive computations.

ESE Seminar: "Accelerating MRI with Deep Learning"
2020-11-06 12:00 - 13:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
Magnetic Resonance Imaging (MRI) can be accelerated by sampling below the Shannon-Nyquist rate via compressed sensing techniques. In this talk, I will consider the problem of optimizing the under-sampling pattern in a data-driven fashion, which has been an open problem for over a decade. For a given sparsity constraint, our method optimizes the under-sampling pattern and reconstruction model, using a computationally efficient end-to-end deep-learning strategy. We call our method Learning-based Optimization of the Under-sampling PattErn, or LOUPE. Our experiments with brain and knee MRI scans show that the LOUPE-derived pattern can yield significantly more accurate reconstructions compared to standard under-sampling schemes. I will also present results for prospectively collected in-vivo images that demonstrate the practical utility of LOUPE in speeding up MRI scans.

MSE Seminar: "From Atom to System: Advanced Characterization for Next Generation Energy Storage Materials"
2020-11-05 10:45 - 11:45 | PA
Seminar details forthcoming.

CBE Seminar: "Delineating Mechanisms of BMP-mediated Patterning and Organization During Development Through Integrative Experiment and Simulation"
2020-11-04 15:00 - 16:00 | Zoom – Email CBE for link
Abstract

The emergence of coordinated cellular, tissue, and organismal responses rely on complex biological networks. While the connectivity of the networks is increasingly defined, understanding the dynamics of the system and the emergence of coordinated responses at multiple spatial and temporal scales is an unsolved problem in many systems. This is a challenge addressed by all living systems that utilize highly conserved chemical reaction networks that coordinate single cell behavior into tissues and into organisms. To study the coordination, we rely on the back and forth between simulation and experiments. Specifically, we developed and applied integrative simulations reaction-advection-diffusion partial differential equation models to discern the mechanism of Bone Morphogenetic Protein (BMP) signaling along the dorsal/ventral (DV) axis in zebrafish embryos, and in the mouse developing organ or Corti. In each of these systems, gradients of BMP signaling form through the interplay of BMP ligands interacting with other secreted factors that shape the gradient. We developed methods to quantify inputs and outputs of BMP signaling activity, developed AI tools to segment every cell in a developing embryo, and used the data to build and test mathematical models of different biological hypotheses of gradient formation and differentiation. In zebrafish embryonic development, we found that a source-sink mechanism is most consistent with formation of the ventral-to-dorsal gradient of BMP signaling; and in the developing organ of Corti in mouse, we found that the BMP gradient forms an information-maximizing linear profile sufficient for multiple boundary specification along the developing organ. These systems highlight how the back and forth between experiment and simulation drives an integrative understanding of developmental systems.

PICS Alumni Spotlight: "Zahera Jabeen"
2020-11-04 12:00 - 13:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The PICS Alumni Spotlight is an opportunity for current students to talk and network with alumni. These talks begin with the speaker discussing their education and career paths and then answering questions from students.

MEAM/GRASP Seminar: "Saltatorial Locomotion on Terrain Obstacles"
2020-11-03 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Robots often struggle to move through complicated environments from cluttered living spaces to treetop canopies where humans and animals flit with ease. Jumping is an exciting locomotion mode that can enable small ground-based robots to maneuver around large obstacles and gaps in complicated environments. A high-power jumping robot can rapidly traverse obstacles, but the resulting fast and forceful stance phases are challenging for control and estimation. In this talk, I will present my work developing a small monopedal jumping robot, Salto-1P: Saltatorial Locomotion on Terrain Obstacles. Salto-1P achieved superlative jumping performance and demonstrated the first robotic wall jump, extreme-acceleration hopping, precise control to traverse obstacles much larger than the robot’s size, and onboard estimation and control to operate outdoors and execute balanced landings.

PSOC Webinar: Emily Hatch
2020-11-02 12:00 - 13:00 | PA
Talk title TBC
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu

PICS Seminar: "Shock-induced turbulent mixing and interactions with flexible panels through simulations"
2020-10-30 14:00 - 15:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
Two fundamental challenges that arise in the development of air-breathing supersonic combustion ramjet engines (scramjets) for hypersonic flight are: 1) the rapid mixing of fuel and oxidizer that must occur prior to combustion, and 2) the coupling between the engine structure and the flow dynamics. Interactions of shock waves and turbulence that characterize the flow inside scramjets play a key role in both mixing enhancement and aerostructural coupling. We present ongoing efforts on the high-fidelity numerical simulation of these two phenomena.
Scalar mixing under canonical shock-turbulence interactions will be addressed first by means of Direct Numerical Simulation, evaluating the effects of variations in the relevant physical parameters: shock and turbulence Mach numbers, Reynolds number, and Schmidt numbers. The analysis will highlight changes along the shock-normal direction of scalar variance and dissipation-rate budgets, flow topology, and alignments of the scalar gradient with vorticity and strain-rate eigendirections.
Then, we will focus on interactions of shock waves reflecting off turbulent boundary layers that develop along the walls of the scramjet. Rigid and flexible walls will be considered, by coupling a wall-modeled large-eddy simulation solver for the fluid flow with an elastic solid structural solver that accounts for geometric nonlinearities. We will emphasize strong shock/boundary-layer interactions resulting in mean flow separation and low-frequency unsteadiness that can interact with natural frequencies of the structure.

2020 CBE Graduate Student Symposium
2020-10-30 11:00 - 17:00 | Zoom
The Graduate Student Symposium will be held on October 30th, 11:00AM-5:00PM EDT. We are excited to share the research conducted in our department with each other and invited industrial representatives, with 14 oral talks and 36 poster presentations from soft matter, biological and biomedical engineering, energy, and catalysis. Information on the program and abstracts can be found on the GSS website.
Given the virtual format, we are requiring students and faculty to register prior to the event. This ensures that you receive the Zoom links for talks as well as have access to the poster presentations and happy hour. Please register for the symposium by October 15th so that we have an accurate head count for planning purposes. We welcome any faculty or research groups within Penn to attend the symposium if it aligns with their research and professional interests.
As always, any questions can be directed to cbegss@seas.upenn.edu. We look forward to seeing everyone there!
GSS Co-Chairs
Katie, Ali, Adam, Tianyi

Doctoral Dissertation: "Computational Investigations of Neuronal Network Responses to Traumatic Brain Injury"
2020-10-29 09:00 - 10:00 | PA
The Department of Bioengineering at the University of Pennsylvania and Dr. David Meaney are pleased to announce the Doctoral Dissertation Defense of Samantha Schumm. The public is welcome to attend the Zoom meeting via the details below.

Title: Computational Investigations of Neuronal Network Responses to Traumatic Brain Injury
Date: Thursday, October 29th, 2020
Time: 9:00am EST

Join Zoom Meeting https://upenn.zoom.us/j/94009572364
Meeting ID: 940 0957 2364

CBE Seminar: "Antibiotic Discovery by Means of Computers"
2020-10-28 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
Until now, the natural world has supplied us with antibiotics. Bacteria, however, are increasingly resistant to these drugs. The next generation of antibiotics will likely come not from nature but from computer-based discovery. Computer-driven approaches have the potential to outperform humans, as demonstrated for pattern recognition of images and text. In order for machines to discover novel drugs and optimize antimicrobial properties, they have to be able to understand, read and write molecules. In this talk, I will describe our efforts in developing computational approaches for antibiotic discovery. I will discuss how we trained a computer to execute a fitness function following Darwin’s algorithm of evolution to select for structures that interact with bacterial membranes, yielding the first artificial antimicrobials that kill bacteria both in vitro and in animal models. My lab has also developed pattern recognition algorithms to mine the human proteome, identifying throughout the body thousands of antibiotics encoded in proteins with unrelated biological function. Computer-made drugs may help to replenish our arsenal of effective drugs.

CIS Seminar: "Advances in Machine Learning Systems Research"
2020-10-27 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
A long-standing grand challenge in computing is to enable machines to act autonomously and intelligently: to rapidly and repeatedly take appropriate actions based on information in the world around them. Driven by trends in the data economy, rapid progress in AI, and an increasingly programmable physical world we are at an inflection point that demands a new class of AI system. This new class of systems goes beyond training models at scale, to connecting models with the world, rendering predictions in real-time under heavy query load, adapting to new observations and contexts. These systems will need to be composable and elastically scalable to accommodate new technologies and variations in workloads. Operating in the physical world, observing intimate details of our lives, and making critical decisions, these systems must also be secure.
In this talk, I will present work in my group exploring advances in systems for prediction serving, autonomous driving, and how model design and system design interact. In particular, I will discuss some of the key trade-offs between time, accuracy, convergence, throughput, and security that govern how we design systems, train models, and make predictions that meet the demands of real-world applications.

ESE Seminar: "High-Frequency Power Conversion with Wide-Bandgap Semiconductors"
2020-10-27 13:00 - 14:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
With the commercialization of wide-bandgap power semiconductors, multi-MHz switching frequencies are more compelling and critical to meet new applications demanding leaps in power density and efficiency. In the past, studies of these converters reported significant gaps between measured and modeled performance, often attributed to dynamic RDS,ON in GaN HEMTs. In particular, the power semiconductors – which often drive thermal constraints – dissipated much more power than expected, rendering designs based on simulated values unusable. In soft-switched converters, which dominate at MHz frequencies, the semiconductor’s output capacitor is resonantly charged and discharged once per switching cycle. Recently, multiple papers have found significant losses from this process in silicon and wide-bandgap devices, explaining the unexpected power dissipation. With these losses known, the MHz-frequency design space can be reopened – if designers are careful about semiconductor selection. In this talk, I will discuss how to select the right device across material (GaN, SiC, or Si), device technology (superjunction or trench), size (lower RDS,ON is not always better), and, in some cases, manufacturer. Further, I will show how this selection drives thermal design, input voltage selection, and novel circuit topologies in a variety of high-performance demonstrations from 6.78 MHz all the way to 40.68 MHz.

MEAM Seminar: "Data-driven Physics Discovery and Scale Bridging in Materials"
2020-10-27 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
In this talk I will provide an overview of our recent work in data-driven methods—mainly machine learning—to enhance computational materials physics models. This body of work has proceeded along two main fronts. The first is system inference, where we seek to identify physical mechanisms via their mathematical signatures as differential or algebraic operators. Our approach of Variational System Identification leverages the weak form of partial differential equations to identify the physics underlying pattern formation, and the deformation mechanisms of soft materials. The framework of Variational System Identification has to address several challenges specific to experimental characterization of materials, such as data that is noisy, sparse, originates from different specimens, and spans dynamics to steady state regimes. The second front is in scale bridging, which we approach in the context of determining free energy functions. We have developed Integrable Deep Neural Networks, and active learning algorithms to combine data generated by Density Functional Theory calculations with cluster expansions and Monte Carlo computations to obtain free energy density functions. These are used in mechano-chemically coupled continuum methods to predict the evolution of microstructure in alloys.

PSOC Webinar: Joel Bader
2020-10-26 12:00 - 13:00 | PA

PICS Seminar: "Simulating solids like fluids: A fully Eulerian approach to fluid-structure interaction"
2020-10-23 14:00 - 15:00 | PA
Abstract: Fluids and solids tend to be addressed using distinct computational perspectives. Solid deformation is most commonly simulated with Lagrangian finite-element methods, whereas fluid flow is amenable to Eulerian-frame approaches such as finite difference and finite volume methods. Problems that mix fluid and solid behaviors simultaneously present interesting numerical challenges. Here we focus on fluid-structure interaction (FSI) problems, and discuss an emerging method called the Reference Map Technique, which allows us to simulate deformable solids on a fixed Eulerian grid. The key is to store and update the reference map field on the grid, which tracks the inverse motion. Using this technique to represent the solid phase, we can solve all phases of an FSI problem on a single fixed grid using fast update procedures very similar to those used in two-phase Navier-Stokes fluid simulations. Various solid constitutive behaviors can be used, such as nonlinear elasticity and plasticity. Systems of many submerged and interacting solids can be simulated, and, by activating the solids internally, we can simulate systems of “soft swimmers”. Incompressibility and rigidity constraints can be applied in all phases by adopting Eulerian projection approaches commonly used in CFD. The addition of the reference map field to the grid also presents certain benefits when computing level-set interface advection, including a procedure to guarantee mass conservation.

PICS Colloquium: "Data-driven model reduction and multiscale modal decomposition for complex chaotic systems"
2020-10-23 14:00 - 15:00 | PA
Many complex nonequilibrium systems, including turbulent flows, are characterized by chaotic dynamics, a large number of degrees of freedom, and hierarchical, multiscale structure in space and time. In two vignettes, we describe some recent work aimed at developing and applying machine learning and data science tools for systems displaying these characteristics.
The first vignette builds on the idea that while partial differential equations are formally infinite- dimensional, the presence of energy dissipation drives the long-time dynamics onto a finite-dimensional invariant manifold sometimes called an inertial manifold (IM). We describe a data-driven framework to represent chaotic dynamics on this manifold and illustrate it with data from simulations of the Kuramoto-Sivashinsky equation. A hybrid method combining linear and nonlinear (neural-network) dimension reduction transforms between coordinates in the full state space and on the IM. Additional neural networks predict time evolution on the IM; this can be done in either the discrete-time (difference equation) or continuous-time (ordinary differential equation) setting. The formalism accounts for translation invariance and energy conservation, and substantially outperforms linear dimension reduction, reproducing very well key dynamic and statistical features of the attractor.
The second vignette addresses how to represent flow or other fields with multiscale structure. We describe a method, inspired by wavelet analysis, that adaptively decomposes a dataset into an hierarchy of structures (specifically orthogonal basis vectors) localized in scale and space: a “data-driven wavelet decomposition”. This decomposition reflects the inherent structure of the dataset it acts on. In particular, when applied to turbulent flow data, it reveals spatially localized, self-similar, hierarchical structures. It is important emphasize that self-similarity is not built into the analysis, rather, it emerges from the data. This approach is a starting point for the characterization of localized hierarchical turbulent structures that we may think of as the building blocks of turbulence. It will also find application to other systems, such as atmospheres, oceans, biological tissues, active matter and many others, that display multiscale spatiotemporal structure.

SIG Seminar: "Physics-based Animation at Pixar"
2020-10-23 12:00 - 13:00 | PA
Pixar’s films rely heavily on physically simulated effects such as the motion of hairs, cloth, and water, to cite a few. Whereas physical accuracy is of paramount importance in engineering applications, the most important aspects of feature film productions are directability and speed. In this talk, I’ll survey our recent work in this area, including robust hyperelastic materials model for volume simulation and high-performance physically-based sculpting techniques.

BE Seminar: "High-throughput Screening of a Combinatorial CAR Co-stimulatory Domain Library" (Kyle Daniels)
2020-10-22 15:00 - 16:00 | PA
This event will be held virtually on zoom. Check email for zoom link or email ksas@seas.upenn.edu.
CAR T cells—T cells engineered to express a chimeric antigen receptor that redirects their function to a specific antigen—have proven to be an effective therapy for certain B cell cancers, but many issues remain in order to apply CAR T cells to a broader range of cancers. The activity of CAR T cells can be modulated by varying their co-stimulatory domains. Most CARs use co-stimulatory domains from natural proteins such as 41BB or CD28, each of which contains motifs that recruit unique signaling molecules and elicit a corresponding T cell response. One strategy to achieve increased control over T cell function is to engineer synthetic co-stimulatory domains composed of novel combinations of motifs from natural co-stimulatory proteins. We constructed libraries of CARs containing synthetic co-stimulatory domains and screened these library in primary human T cells for the ability to promote proliferation, degranulation, and memory formation. The results of the screens give insights into how signaling motifs dictate cell function and offer clues on how to engineer co-stimulatory domains that promote desired CAR T cell functions.

ESE Seminar: "New Tools for Better Understanding Social Networks"
2020-10-22 11:00 - 12:00 | Zoom – Email ESE for Link jbatter@seas.upenn.edu
We shall examine in our talk concepts and tools for the analysis of social networks. We shall present in particular YouTube and Twitter. For YouTube, we point at shortcomings in ways to measure audience retention and propose new concepts to better quantify desirable properties. We then present a geo-linguistic analysis of Twitter based on daily periodograms and use these to study the evolution of languages as seen in Twitter.

MSE Seminar: "Polymer Informatics: Current Status & Critical Next Steps"
2020-10-22 10:45 - 11:45 | PA
The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational, experimental and informatics methodologies. Here, we describe the role played by computational and experimental data generation and capture, polymer fingerprinting, machine-learning based property prediction models, and algorithms for designing polymers meeting target property requirements. These efforts have culminated in the creation of an online Polymer Informatics platform (https://www.polymergenome.org) to guide ongoing and future polymer discovery and design [1-3]. Challenges that remain will be examined, and systematic steps that may be taken to extend the applicability of such informatics efforts to a wide range of technological domains will be discussed. These include strategies to deal with the data bottleneck, new methods to represent polymer morphology and processing conditions, and the applicability of emerging AI algorithms for materials design.

CBE Seminar: "Engineering Pathways Across Biological Barriers"
2020-10-21 15:00 - 16:00 | Zoom – Email CBE for link
Abstract
Engineering pathways across biological barriers is entering a new era with the rapid advancement of computational resources. My research group focuses on developing multi-scale simulation methods to elucidate the interfacial phenomena associated with biological barriers that play a role in life-threatening diseases, such as Alzheimer’s, cancer, and chronic infections. Our goal is to influence this experimentally-dominated research field by providing mechanistic, structural, and molecular insights into the barrier functions that were computationally unattainable prior to our work. In past few years, we have made breakthroughs in three research domains: elucidated the molecular architecture of the blood-brain barrier and developed strategies to enhance the barrier’s permeability for treatment of the neurodegenerative diseases; developed telodendrimer-based nanocarriers for efficient delivery of approved anticancer drugs for treatment of solid tumors; and designed an online computational platform to screen libraries of small molecules for their permeability across bacterial membranes, and determine their use as antibiotics for treatment of chronic infections. All three research domains have ties with experimental groups to ensure the validity of our research findings. In my talk, I will elaborate on our computational methods, present the key results, and provide a perspective on the long-term research goals of the group.

PICS Alumni Spotlight: "Whelton Miller"
2020-10-21 12:00 - 13:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The PICS alumni Spotlight is an opportunity for students to learn from and network with alumni.

CIS Seminar: "Theoretical Reflections on Quantum Supremacy"
2020-10-20 15:00 - 16:00 | Zoom – Email CIS for link, cherylh@cis.upenn.edu
The recent demonstration of quantum supremacy by Google is a first step towards the era of small to medium scale quantum computers. In this talk I will explain what the experiment accomplished and the theoretical work it is based on, as well as what it did not accomplish and the many theoretical and practical challenges that remain. I will also describe recent breakthroughs in the design of protocols for the testing and benchmarking of quantum computers, a task that has deep computational and philosophical implications. Specifically, this leads to protocols for scalable and verifiable quantum supremacy, certifiable quantum random generation and verification of quantum computation.

MEAM Seminar: "Engineering Solutions for Tough Problems in Trauma: From Occlusion Balloons to Decision-Support"
2020-10-20 10:30 - 12:00 | Zoom – Email MEAM for Link, peterlit@seas.upenn.edu
Acute hemorrhage and hemorrhagic shock result in approximately 60,000 annual deaths in the United States. The vast majority of these deaths are in severely injured patients, but experts in trauma care believe many of these deaths can actually be prevented. Efforts focused on injury prevention and pre-emptive intervention have produced some improvements in survival. However, for those who sustain severe injuries, a range of engineering solutions could mitigate the risk of death from hemorrhage.
In this talk, I will review the current epidemiology of acute hemorrhage and our understanding of hemorrhagic shock. I will then explore several recent innovations designed to stop acute hemorrhage and guide teams in real-time management: balloon aortic occlusion and hemorrhage decision support. These innovations will be discussed in the context of the regulatory hurdles that must be navigated to bring these and other similar innovations to the bedside.

PSOC Webinar: “A three dimensional extracellular matrix supports organoid phenotype by reducing cortical actin tension to maintain endoplasmic reticulum function” (FuiBoon Kai)
2020-10-19 12:00 - 13:00 | PA
“A three dimensional extracellular matrix supports organoid phenotype by reducing cortical actin tension to maintain endoplasmic reticulum function”
Physical Sciences in Oncology Center PSOC@Penn
Fall 2020 Webinar Series Mondays @ Noon (EST)
For webinar links, please contact manu@seas.upenn.edu
