Alumna combines engineering, medical expertise to alleviate PPE shortage

By Daniel Li

Dr. Aditi Sharma, a UC San Diego bioengineering alumna and resident physician at the UC Irvine dermatology department, is combining her engineering skills and medical expertise to solve one of the ongoing challenges of the COVID-19 pandemic: a shortage of protective masks for healthcare workers. 

Sharma and several colleagues developed a method to fabricate face masks out of discarded surgical tool sterilization wraps, and launched a project that aims to create 10,000 of these masks for healthcare workers. Their project was featured in the Los Angeles Times.

Their mask is made from recycled Halyard H600, a material used for surgical equipment sterilization, with straps made of recycled Gemini surgical wrap material. 

Their repurposed sterilization wrap mask has up to 86.5 percent filtration rate; this is lower than the 95 percent of N95 masks, but more than three times more effective than ordinary cloth masks, which many health care workers have been forced to use due to insufficient personal protective quipment (PPE). Their goal is to be able to reserve N95 masks for medical personnel working directly with known COVID-19 patients.

In just two months, Sharma and her team have made over 2,000 face masks, and are looking to expand the project to the entire state of California and hopefully the rest of the country. 

“We're hoping that ultimately not only will healthcare providers have the masks, but maybe even people in the community can have access to them as well,” Sharma said. “In terms of getting towards that 10,000 goal, I think probably in the next couple of weeks we should be there, between manufacturers in the local community who are willing to help us and volunteer groups who are willing to help.”

Sharma graduated from UC San Diego in 2009 with a degree in biomedical engineering, and then received her medical degree at the Medical College of Virginia. During her time at UC San Diego, Sharma had the opportunity to work at Pfizer as a research assistant and participate in the Amgen Scholars Program over the summer. These two experiences sparked her curiosity in immunology and inspired her to conduct research on biological warfare and bioterrorism under Dr. Anthony Fauci at the National Institutes of Health.

Sharma's masks, made from repurposed sterilization wrap.

After a one-year stint at the NIH, Sharma shifted gears and worked as an engineer at the World Health Organization to improve access to medical devices for low income individuals. She explained that her background in engineering has given her a unique approach to medicine. 

“I think something that is kind of fundamental to engineering is asking, “Is this the most efficient process and how can we improve it?” Sharma said. “I think sometimes in medicine, we accept what is told to us-- that this is how it is. And I find that that engineering side of me is constantly saying, ‘What can we do better?’”

When Sharma came back into the medical field, her main goal was to find a way to integrate the fields of public health, engineering, and medicine in her work; this project has allowed her to do so and help contribute to the fight against COVID-19. Sharma encourages students to take advantage of all the resources that UC San Diego offers and to dream big.  

“What I loved about UC San Diego is there are so many resources,” Sharma said. “I used to go to the Teaching and Learning Commons...and I remember really learning linear algebra very well because I had that extra support system. I am also grateful for applying for those job opportunities that I never thought I would get. I think it set me up for the rest of my career.”

Dispatches from a pandemic: graduate students create COVID virus simulations

By Daniel Li

As it became clear in late February that COVID-19 was not going anywhere, four UC San Diego graduate students were planning their final project for the Numerical Analysis for Multiscale Biology course, which uses math to simulate biological processes.

The mechanical engineering and bioengineering students—Parker Dow, Cathleen Nguyen, Clara Posner, and Patrick Wall—decided to put their skills to a new use, and build a predictive model analysis that could bridge from the molecular biology of the SARS-CoV-2 virus to the epidemiology of the spread of infection through the population. The team started the three-week project in early March.

“A lot of times, when working on basic cell biology research, it can seem kind of removed from the bigger picture of what’s happening in the world,” Posner said. “But working on this coronavirus project is a lot more motivating since it can help with this current crisis that’s affecting us all.”

The idea to focus the project on COVID-19 was first brought up by Dow. According to Dow, he had started to see new scientific literature related to the novel coronavirus come out and it became increasingly apparent that some of the data could be used for computer modeling.

“I floated the idea to the group because I’d seen in a paper that they got a new structure of the coronavirus binding protein,” Dow said. “Our group started to do a bit more research on it and discovered that the scientific community had been publishing things daily, so we all wanted to take a stab at it.”

Each student focused on a different level of the project: cellular, molecular, and population scale. Wall created an alveolus in the lung with the MCell modeling tool to figure out the virus's rate of spread. Dow analyzed viral binding kinetics using BrownDye software. Posner used Virtual Cell (VCell) to create a transforming growth factor (TGF)-beta signaling induced lung fibrosis model. Nguyen focused on creating a population infection model using Vcell at the population level.

Two of the tools—Browndye and MCell—that the team used to model their systems were developed in-house at UC San Diego. Several local scientists, including UC San Diego Project Scientist Gary Huber and Salk Institute Staff Scientist Tom Bartol, were actively involved and helped guide them through the project. 

“The course instructors went above and beyond,” Wall said. “It was really helpful to reach out to them and ask for their expert knowledge. They also were instrumental in getting our models to run properly.”

This hands-on course is one of seven lab courses offered by the Interfaces Graduate Training Program in Multi-scale Biology that involves students from 11 graduate programs at UC San Diego and is directed by Professor Andrew McCulloch from the Department of Bioengineering.

 “The scientific challenges of addressing the COVID-19 pandemic are so daunting because they span from the scale of the spike protein on the virus, to the cellular and pathophysiological responses of the infected human to the population of the globe. Problems like these require the kinds of novel multi-scale approaches and interdisciplinary teamwork that the Interfaces program was designed to teach and encourage.

According to Wall, one of the challenges when they first started was that the data surrounding COVID-19 was sparse. To tackle this, the team looked at similar viruses, such as SARS, and used data from that to generate initial models. 

“The 2002 SARS virus was also a coronavirus outbreak. These viruses are so similar,” Wall said, “we were able to use a lot of the data that was generated in the mid 2000s to early 2010s on the SARS coronavirus and extrapolate our modeling based off of that.”

Nguyen added that because the coronavirus was evolving in real time, there were a lot of unknowns and the team was forced to make assumptions throughout the project. 

“Everyday you’re receiving new information about the pandemic and want to apply it to the models,” Nguyen said. “You make a lot of assumptions and those assumptions are changing based on new information. You're changing your inputs, your process, and with every simplification you make, you lose some accuracy in the models.”

Nguyen enjoyed how she was able to work together with students of different engineering backgrounds.

“I’m more of a mechanical engineering background, but the rest of my team members have more of a bioengineering background,” Nguyen said. “And the novelty comes when you’re trying to work on a multiscale project with people who have different expertise and skills.”

Discovery of High-Entropy Ceramics via Machine Learning

by Kevin Kaufmann and Prof. Kenneth Vecchio

Materials are an essential part of our world; they have enabled us to build cities, treat disease, and communicate across the world in real time. For centuries, material scientists have been working to build our material library and to discover new materials with greater performance and better property trade-offs. Over the past few decades, however, the rate at which new materials are being discovered has been slowing continuously. This is due to several factors including increasingly stricter regulations, more challenging performance metrics, and increasingly more expensive empirical development strategies.

The reduced rate of material discovery is also in part because many of the simplest combinations have been investigated, and the number of remaining possible combinations is quite extensive. For example, if random combinations of five elements from the periodic table are combined in equal amounts, there would be 1078 possible combinations to choose from. This example ignores the fact that different numbers of elements can be combined, not just five, and that they do not have to be combined in equal ratios. For perspective, there are estimated to be 1066 atoms in the Milky Way galaxy. So, in terms of a “big data” challenge, materials development of complex composition alloys represents perhaps the biggest big data paradigm. There is a clear need for a method to narrow the search space to only the most promising candidates for a given application. Intuition and expensive trial and error strategies will not be sufficient for investigating this immense chemical space, and more informed computational methods must be developed and employed.

Our team of nanoengineers in Professor Kenneth Vecchio’s lab at UC San Diego is developing tools for screening large numbers of materials in a rapid fashion. The first step in our work is creating unique identifiers for each material, akin to a fingerprint of the material. In the same way no two fingerprints are alike, every individual material possible can be reduced to a simple but unique set of attributes. These identifiers describe the material composition in a way that supports computation work leveraging a subset of artificial intelligence called machine learning. The machine learning tools learn the underlying science that relates these attributes to various material properties. Typically, machine learning requires enormous initial datasets to learn from before it becomes a useful tool.

However, the method that our team developed is designed around the fact that material development problems frequently have less than 100 data points at the outset. After learning about the initial dataset, the machine learning algorithm suggests new materials with the goal of maximizing performance. Each time the materials suggested by the algorithm are fabricated and tested, this new information is made available to the algorithm, creating a learning loop.

The data-driven method that our team has developed was recently demonstrated for predicting the synthesizability of single crystal structure (e.g. rock-salt structured) carbide ceramic materials containing five metal cations, also known as high entropy carbides. High entropy carbides constitute a subset of the complex concentrated alloys class of materials described previously, as they have the added uniqueness of becoming more stable at increasing temperatures, which is unlike most engineering materials. The researchers focused their study on what are called non-intuitive compositions, in which three of the five metal cations are chromium, molybdenum, and tungsten, none of which form a rock-salt structure at room temperature in a one metal atom to one carbon atom ratio.

The initial dataset contained all available data: 56 high entropy carbide materials with synthesizability calculated by computationally expensive density functional theory (DFT). None of the 56 known compositions contained chromium, one of the three metal cations of interest. While DFT can compute a few compositions per month, the machine learning model was able to learn from the 56 materials and make predictions on 70 new materials in less than one day.

Seven materials, four predicted to succeed and three predicted to fail, were experimentally fabricated and analyzed to assess the validity of the predictions. Rather surprisingly, several five-cation metal carbide compositions were discovered, wherein three of the five cations were chromium, molybdenum, and tungsten—the elements that don’t form the rocksalt monocarbide structure—and yet these compositions were experimentally shown to successfully form the rock-salt structure. Furthermore, all seven experimentally studied compositions resulted in single or multi-crystal structure materials in exact agreement with the machine learning predictions. The ability for the machine learning model to perform exceedingly well in such a non-intuitive chemical space, a composition space which contained no prior data to learn from, further demonstrates the unique strength of this approach. Our team expects the machine learning framework to be a useful tool in the development of other materials such as alloys, battery components, or pharmaceuticals.

This work is published in Nature Partner Journals (npj) Computational Materials, May 1, 2020.

Read the paper here: https://rdcu.be/b3UdG

DOI: 10.1038/s41524-020-0317-6

Jessica Sandoval: graduate student, ROV pilot, researcher

Filming: Erin Ranney. Editing: Daniel Sosa-Cobo

When Jessica Sandoval isn’t building robot components and microplastic detectors at the University of California San Diego, she drives a remotely operated underwater vehicle for an organization founded by Robert Ballard--the man who discovered the Titanic’s wreck.

This spring, Sandoval was part of a team of scientists working to understand plastic degradation in the ocean whose research was featured in The New York Times. The team of engineers and marine biologists at the UC San Diego Scripps Institution of Oceanography is studying how microplastics and microfibers enter and spread in the environment, particularly the ocean. Sandoval developed an instrument called the Automated Microplastics Identifier that gets these microfibers to fluoresce, making it easier to detect them and study them. She also developed software to quantify the amount of plastic in each sample and generate information on the features of the plastics using image recognition.

“It is an exciting first step, using automation technologies to assist with the monitoring of this prevalent marine pollutant,” said Sandoval, who began developing this technology as an undergraduate student at MIT. “With such technologies, we can more easily process samples from across the globe and generate a better understanding of microplastic distribution.”

Sandoval is also a PhD student in the Bioinspired Robotics and Design Lab of Professor Mike Tolley, developing new robotic technologies inspired by insects, animals and nature. In October, she was part of a team that developed a better suction cup inspired by a fish with extraordinary gripping capabilities, called a clingfish. By studying how the clingfish is able to strongly yet gently stick to both smooth and rough surfaces, Sandoval and other engineers in Tolley’s lab were able to develop an innovative suctioncup capable of delicately lifting objects like eggs or shells. Sandoval was the first author of the paper published in the journal Bioinspiration and Biomimetics.

Because she pilots an ROV on the research ship Exploration Vessel Nautilus, she got to test a prototype of her suction cup in the field during one of the ship’s missions. The job is an ideal combination for Sandoval.

“I am fascinated by marine biology and the technology that allows us to observe and measure it,” she said in an interview on the Nautilus’ website. “The ocean provides an imagination’s playground in which there is much to be explored and discovered. This excitement of the not yet known definitely sparked my interest in ocean exploration. That and the incredible plethora of marine biodiversity that exists in our oceans.”

Mechanical engineer recognized by Society for Industrial and Applied Mathematics

Jorge Cortes, a professor in the Department of Mechanical and Aerospace Engineering has been inducted as a 2020 Fellow by the Society for Industrial and Applied Mechanics.
Cortes is being recognized for contributions to the control and optimization of network systems.

The fellows were nominated for their exemplary research as well as outstanding service to the community. Through their contributions, SIAM Fellows help advance the fields of applied mathematics and computational science.

Cortés' research interests are on distributed coordination algorithms, autonomous robotic networks, adversarial networked systems, mathematical control theory, geometric mechanics and geometric integration. The recent emergence of low-cost, highly-autonomous vehicles with control, communication, sensing, and computing capabilities has paved the way for the deployment of robotic sensor networks in a wide range of applications. Controlled motion coordination of these networks will have far-reaching implications in the monitoring of natural phenomena and the enhancement of human capabilities in hazardous and unknown environments. Motivated by these scenarios, Professor Cortes' research program is developing systematic methodologies to control autonomous, reliable, and adaptive mobile networks capable of operating in unknown and dynamic environments.

Ruth J. Williams, from the UC San Diego Department of Mathematics, is also being recognized for contributions to the study of stochastic processes and their applications.

Full SIAM release here: https://sinews.siam.org/Details-Page/siam-announces-class-of-2020-fellows

Metabolic and genetic basis for auxotrophies in Gram-negative species

By Yara Seif

While some bacteria survive independently, others reduce their metabolic expenditures by utilizing the nutrients available to them in their environment. These bacteria choose to adapt the concept of simple living or “less is more,” meaning one can survive on minimal requirements (we could definitely learn from them). Auxotrophy, a.k.a nutritional dependencies, are a characteristic of host adaptation. They are hard to characterize experimentally because there are too many nutrients to choose from, and also because they differ from one strain to another.

In a study published Mar. 5 in PNAS, we develop a computational workflow that uses both flux balance analysis and comparative genomics to predict nutrient requirements de novo and from sequences alone.

In our workflow, we compare the gene content across several strains of bacteria, and build metabolic networks tailored to each genetic background. Next, we simulate for growth on a minimal medium, and when that cannot be achieved, we run our algorithm called AuxoFind, to search for possible nutrients that would restore growth in silico.

Metabolic networks were tailored to the gene content of different bacteria and nutrient dependencies were predicted and validated experimentally. Image courtesy of Systems Biology Research Group

We find that when the same gene is missing, the nutrient requirements change across species, because they have different metabolic networks and combinations of alternative pathways. We also observed that the absences are manifested as a result of a large range of genetic modifications going from simple and small mutations (like single nucleotide polymorphisms) to large and complex genetic changes (whole genome rearrangements and multi-gene deletions).

The significance of this work is as follows:

Patients with certain diseases (such as Crohn’s disease or cystic fibrosis) tend to be chronically infected with bacteria. Over time, these bugs become more vicious because they slowly adapt to the in vivo environment. Understanding how these adaptations occur is a first step towards devising therapeutic solutions.

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Yara Seif is a UC San Diego bioengineering Ph.D. student. As a member of Bernhard Palsson's Systems Biology Research Group, she studies the metabolism of bacterial strains as well as the evolution of metabolic traits across strains especially in relation to their lifestyle. Her research so far has included multi-strain genomic and metabolic analysis of gram-negative strains using a combination of constraint-based metabolic modeling, comparative genomics and machine learning.

Barrett Romasko: structural engineer

 By Daniel Li

Barrett Romasko’s path in college has been full of exploration. Romasko, a senior majoring in structural engineering with a focus on aerospace structures, applied to UC San Diego without knowing much about the different applications of structural engineering, assuming it only involved civil engineering structures. His willingness to seek out new opportunities — through on-campus activities, classes, and internships — has been a contributing factor in helping him figure out his interests and goals for the future. 

On campus, Romasko is heavily involved in the UC San Diego Society of Civil and Structural Engineers (SCSE), which has three technical project teams that students can join to get hands-on structural engineering experience: steel bridge, concrete canoe, and seismic design. Romasko has been part of the steel bridge project team since his sophomore year –he was the team’s welding lead his junior year and is currently the project manager. 

The steel bridge project challenges students to design, fabricate, and construct a scaled model bridge that stays competitive in terms of the lightest weight, greatest stiffness, and fastest construction speed. The students start preparing their bridge each fall and bring it to the annual Pacific Southwest Conference each year to see how it stacks up to the competition.

The steel bridge team with their bridge.

“We start the design process in fall quarter, which generally consists of using a lot of design software and analysis,” Romasko said. “Winter quarter is dedicated to fabrication, so the team takes the design to a machining space and manufactures each component of the bridge. The last stage is construction, which is when we practice assembling each member of the bridge according to the regulations that we received in preparation for the competition.”

According to Romasko, the hardest part of the competition is getting all the components fabricated by the competition in April. That was compounded this year, as the team had to find a new location to fabricate their bridge, as the location they’d been using for 18 years was no longer available. Romasko and his co-project manager got to work and were able to come up with a solution.

Despite unexpected challenges, Romasko has enjoyed working on the steel bridge project the past three years. His favorite parts about steel bridge: the teamwork and hands-on learning aspect.

“I really like steel bridge because you get to apply what you learn in class to a real project and work with so many cool, motivated people,” Romasko said. “You also start to understand important industry concepts such as fabrication and tolerancing.”

Romasko encourages students to get involved in student groups as early as possible, and stresses the importance of finding organizations that are not only career focused, but also fun. 

“Joining the steel bridge project has introduced me to so many new people that I wouldn’t have met otherwise,” he said. “It has been a good way for me to make friends who share like-minded interests.”

In addition to their hands-on technical projects, SCSE organizes two main community outreach events each year: Seismic Outreach and Esperanza International. 

Members of the steel bridge team.

“Seismic outreach consists of us going to schools to teach elementary and middle school students about how to design for seismic safety and teach them about earthquakes,” Romasko said. “The goal is to get these students more interested in STEM fields. We also have another event where we go down to Rosarito in Mexico with an organization called Esperanza International, and put our engineering skills to use as we help build houses for the less fortunate.”

In addition to his involvement in SCSE, Romasko is a research assistant in Professor Machel Morrison’s lab, where he works on projects related to metallography and mechanics of materials. He’s also nabbed several internships over the summers, working at the Naval Surface Warfare Center in 2018 and General Atomics in 2019. 

“Internships are valuable because you can get direct experience in the industry,” Romasko said. “The internships that I have done really allowed me to see what I could do with my major and what I don’t want to do with my major. For example, at General Atomics, I was a manufacturing engineering intern; after the summer, I realized that although it was a great learning experience, I wouldn’t want to do it as a career. I feel that it is important for everyone to explore different areas to find what they’re most passionate about, and even more importantly, to find what they aren’t passionate about.”

Romasko came to UC San Diego thinking that he was going to follow the civil structures route in the structural engineering department, but during his internship at the Naval Surface Warfare Center, he realized that aerospace structures were more interesting to him. Without that internship, Romasko said he fears he would never have changed to the aerospace structures focus.

Romasko is returning to UC San Diego to complete a master’s degree in structural engineering this fall. In the future, he hopes to work abroad for a couple years, either in Australia, Europe, or New Zealand.

“I would love to work outside of the United States for two to three years doing something related to aerospace structures,” Romasko said. “One of my dream companies to work at is Virgin Galactic, which specializes in developing commercial spacecraft.”