“The AIChE 35 Under 35 Award was founded to recognize young chemical engineers who have achieved greatness in their fields,” reads the 2020 award announcement. “The winners are a group of driven, engaged, and socially active professionals, representing the breadth and diversity that chemical engineering exemplifies.”
De la Fuente was named in the list’s “Bioengineering” category for his his lab’s work in machine biology. Their goal is to develop computer-made tools and medicines that will combat antibiotic resistance. De la Fuente has already been featured on several other young innovators lists, including MIT Technology Review’s 35 under 35 and GEN’s Top 10 under 40, both in 2019. His research in antibiotic resistance has been profiled in Penn Today and Penn Engineering Today, and he was recently awarded Penn Health-Tech’s inaugural NEMO Prize for his proposal to develop paper-based COVID diagnostic system that could capture viral particles on a person’s breath.
In addition to being named on the 2020 list, the honorees will receive a $500 prize and will be celebrated at the 2020 AIChE Annual Meeting this November.
Learn more about de la Fuente’s pioneering research on his lab website.
The paper-based tests could be integrated directly into facemasks and provide instant results at testing sites.
When Penn Health-Tech announced its Nemirovsky Engineering and Medicine Opportunity, or NEMO Prize, in February, the center’s researchers could only begin to imagine the impact the looming COVID-19 pandemic was about to unleash. But with the promise of $80,000 to support early-stage ideas at the intersection of engineering and medicine, the contest quickly sparked a winning innovation aimed at combating the crisis.
Judges from the University of Pennsylvania’s School of Engineering and Applied Sciences and Perelman School of Medicine awarded its first NEMO Prize to César de la Fuente, PhD, who proposed a paper-based COVID diagnostic system that could capture viral particles on a person’s breath, then give a result in a matter of seconds when taken to a testing site.
Similar tests for bacteria cost less than a dollar each to make. De la Fuente, a Presidential Assistant Professor in the departments of Psychiatry, Microbiology, and Bioengineering, is aiming to make COVID tests at a similar price point and with a smaller footprint so that they could be directly integrated into facemasks, providing further incentive for their regular use.
“Wearing a facemask is vital to containing the spread of COVID because, before you know you’re sick, they block your virus-carrying droplets so those droplets can’t infect others,” de la Fuente says. “What we’re proposing could eventually lead to a mask that can be infected by the virus and let you know that you’re infected, too.”
De la Fuente’s expertise is in synthetic biology and molecular-scale simulations of disease-causing viruses and bacteria. Having such fine-grained computational models of these microbes’ binding sites allow de la Fuente to test them against massive libraries of proteins, seeing which bind best. Other machine learning techniques can then further narrow down the minimum molecular structures responsible for binding, resulting in functional protein fragments that are easier to synthesize and manipulate.
The spike-shaped proteins that give coronaviruses their crown-like appearance and name bind to a human receptor known as ACE2. De la Fuente and his colleagues are now aiming to characterize the molecular elements and environmental factors that would allow for the most precise, reliable detection of the virus.
Once hailed as medical miracles, antibiotics are losing their effectiveness due to the rapid increase of bacterial immunity.
Researchers are scrambling to keep up with evolution, and they are currently exploring how machine learning can be applied to microbiology to develop more effective treatments.
In the past, researchers have studied bacteria behavior and used their findings to work against the natural patterns of bacterial life. In the 1980s, computer-assisted screening methods helped researchers in their efforts but few developments surfaced from their work. It seemed that there were no new antibiotics to be found using traditional methods, and pharmaceutical companies stepped away from funding antibiotic development in favor of more profitable drugs used to treat chronic conditions. But a new field of research shows a way forward, thanks to the massive advances in computing that have occurred over the intervening decades.
Among the pioneering researchers in this field is César de La Fuente, Presidential Assistant Professor in Psychiatry, Microbiology and Bioengineering. De La Fuente is accelerating the discovery of new antibiotics with his Drug Repurposing Hub, a library of more than 6,000 compounds that is using machine learning algorithms to seek out possible solutions for human disease. With his compound library, de La Fuente is able to examine drugs already approved by the FDA and hunt for new, more effective applications.
In addition to this work, de La Fuente and his colleagues are interested in using machine learning to innovate drug design itself. His lab uses a machine learning platform to generate new molecules in silico and perform experiments on them. Once the results of the experiments come in, they are fed back into the computer so the machine learning platform can continuously learn and improve its findings from the data.
In a recent interview with Katherine Harmon Courage in Quanta Magazine, de La Fuente said:
“The hypothesis is that nature has run out of inspiration in terms of providing us with new antibiotics. That’s why we think that machines … could diversify natural molecules to convert them to synthetic versions that would be much more effective.”
Positive results in first-in-U.S. trial of CRISPR-edited immune cells
Genetically editing a cancer patient’s immune cells using CRISPR/Cas9 technology, then infusing those cells back into the patient appears safe and feasible based on early data from the first-ever clinical trial to test the approach in humans in the United States. Researchers from the Abramson Cancer Center have infused three participants in the trial thus far—two with multiple myeloma and one with sarcoma—and have observed the edited T cells expand and bind to their tumor target with no serious side effects related to the investigational approach. Penn is conducting the ongoing study in cooperation with the Parker Institute for Cancer Immunotherapy and Tmunity Therapeutics.
“This trial is primarily concerned with three questions: Can we edit T cells in this specific way? Are the resulting T cells functional? And are these cells safe to infuse into a patient? This early data suggests that the answer to all three questions may be yes,” says the study’s principal investigator Edward A. Stadtmauer, section chief of Hematologic Malignancies at Penn. Stadtmauer will present the findings next month at the 61st American Society of Hematology Annual Meeting and Exposition.
Because of the opioid epidemic sweeping the nation, Moore notes that there’s a rapid search going on to develop non-addictive painkiller options. However, he also sees a gap in adequate models to test those new drugs before human clinical trials are allowed to take place. Here is where he hopes to step in and bring some innovation to the field, by integrating living human cells into a computer chip for modeling pain mechanisms. Through his research, Moore wants to better understand not only how some drugs can induce pain, but also how patients can grow tolerant to some drugs over time. If successful, Moore’s work will lead to a more rapid and less expensive screening option for experimental drug advancements.
New machine learning-assisted microscope yields improved diagnostics
Researchers at Duke University recently developed a microscope that uses machine learning to adapt its lighting angles, colors, and patterns for diagnostic tests as needed. Most microscopes have lighting tailored to human vision, with an equal distribution of light that’s optimized for human eyes. But by prioritizing the computer’s vision in this new microscope, researchers enable it to see aspects of samples that humans simply can’t, allowing for a more accurate and efficient diagnostic approach.
Led by Roarke W. Horstmeyer, Ph.D., the computer-assisted microscope will diffuse light through a bowl-shaped source, allowing for a much wider range of illumination angles than traditional microscopes. With the help of convolutional neural networks — a special kind of machine learning algorithm — Horstmeyer and his team were able to tailor the microscope to accurately diagnose malaria in red blood cell samples. Where human physicians typically perform similar diagnostics with a rate of 75 percent accuracy, this new microscope can do the same work with 90 percent accuracy, making the diagnostic process for many diseases much more efficient.
Case Western Reserve University researchers create first-ever holographic map of brain
A Case Western Reserve University team of researchers recently spearheaded a project in creating an interactive holographic mapping system of the human brain. The design, which is believed to be the first of its kind, involves the use of the Microsoft HoloLens mixed reality platform. Lead researcher Cameron McIntyre, Ph.D., sees this mapping system as a better way of creating holographic navigational routes for deep brain stimulation. Recent beta tests with the map by clinicians give McIntyre hope that the holographic representation will help them better understand some of the uncertainties behind targeted brain surgeries.
More than merely providing a useful tool, McIntyre’s project also brings together decades’ worth of neurological data that has not yet been seriously studied together in one system. The three-dimensional atlas, called “HoloDBS” by his lab, provides a way of finally seeing the way all of existing neuro-anatomical data relates to each other, allowing clinicians who use the tool to better understand the brain on both an analytical and visual basis.
Implantable cancer traps reduce biopsy incidence and improve diagnostic
Biopsies are one of the most common procedures used for cancer diagnostics, involving a painful and invasive surgery. Researchers at the University of Michigan are trying to change that. Lonnie Shea, Ph.D., a professor of biomedical engineering at the university, worked with his lab to develop implants with the ability to attract any cancer cells within the body. The implant can be inserted through a scaffold placed under the patient’s skin, making it a more ideal option than biopsy for inaccessible organs like lungs.
The lab’s latest work on the project, published in Cancer Research, details its ability to capture metastatic breast cancer cells in vivo. Instead of needing to take biopsies from areas deeper within the body, the implant allows for a much simpler surgical procedure, as biopsies can be taken from the implant itself. Beyond its initial diagnostic advantages, the implant also has the ability to attract immune cells with tumor cells. By studying both types of cells, the implant can give information about the current state of cancer in a patient’s body and about how it might progress. Finally, by attracting tumor and immune cells, the implant has the ability to draw them away from the area of concern, acting in some ways as a treatment for cancer itself.
People and Places
The Philadelphia Inquirer recently published an article detailing the research of Penn’s Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering, Cesar de la Fuente, Ph.D. In response to a growing level of worldwide deaths due to antibiotic-resistant bacteria, de la Fuente and his lab use synthetic biology, computation, and artificial intelligence to test hundreds of millions of variations in bacteria-killing proteins in the same experiment. Through his research, de la Fuente opens the door to new ways of finding and testing future antibiotics that might be the only viable options in a world with an increasing level of drug-resistant bacteria
Emily Eastburn, a Ph.D. candidate in Bioengineering at Penn and a member of the Boerckel lab of the McKay Orthopaedic Research Laboratory, recently won the Ashton fellowship. The Ashton fellowship is an award for postdoctoral students in any field of engineering that are under the age of 25, third-generation American citizens, and residents of either Pennsylvania or New Jersey. A new member of the Boerckel lab, having joined earlier this fall, Eastburn will have the opportunity to conduct research throughout her Ph.D. program in the developmental mechanobiology and regeneration that the Boerckel lab focuses on.
Cesar de la Fuente, Ph.D., a Presidential Assistant Professor in Psychiatry, Microbiology, and Bioengineering at Penn, recently published a literature review in Trends in Immunology entitled, “Emerging Frontiers in Microbiome Engineering.” The microbiome, in simple terms, consists of the genetic material of microorganisms in the gut, including bacteria, fungi, protozoa, viruses, and oral, vaginal, and skin microbiomes. Each human has a unique microbiome that depends both on predetermined factors like exposure to microorganisms within a mother’s birth canal or breastmilk in early life as well as environmental factors and diet in later life. The health of someone’s microbiome is extremely important, as an unhealthy microbiome with an imbalance of symbiotic and pathogenic microbes can make a person more susceptible to various diseases. The most common diseases or disorders associated with a problematic microbiome are rather far-reaching, including some of the most afflicting diseases of today like inflammatory bowel disease, diabetes, obesity, cardiovascular diseases, and neurological disorders.
In his recent literature review, de la Fuente provides an overview of microbiome engineering, and what the future might hold for the field. He defines microbiome engineering initially as a way of studying the “contribution of individual microbes and generating potential therapies against metabolic, inflammatory, and immunological diseases.” Currently, most treatments for issues with the microbiome are broad solutions like dietary adjustments to include more probiotics, antibiotics, or prebiotics, while more serious cases may require a fecal microbiota transplant. While these therapies may work for some patients, de la Fuente emphasizes the need for greater specificity in treatment targets and a need for precision in reprogramming existing microbial communities as an alternative to transplants.
De la Fuente highlights the current methods and tools in microbiome engineering such as the use of bacteriocins and bacteriophages to knock out specific bacteria within the microbiome. However, there are very few bacteriocins or bacteriophages commercially available on today’s market. Another common approach to microbiome engineering is in synthetic biology, or the use of “chassis” — a type of cell that maintains DNA constructs for different functions — to engineering interactions within the microbiome. De la Fuente continues his discussion of current methods by naming and describing several specific examples of these approaches, particularly in relation to synthetic biology options before moving on to examine future directions for these methods.
Before bringing up potential new frontiers for microbiome engineering, de la Fuente also outlines the way that microbiome engineering works in the first place, and dedicates sections of the review to the microbiome’s influence on its host’s immune system and how to engineer the microbiome to modulate that immune system. The main future methods for microbiome engineering that de la Fuente points out in his review include more precise regulation of gene expression through commensal organisms and the use of CRISPRi to find genes involved in bacterial maintenance. The conclusion of de la Fuente’s review brings up the notion of new personalized medicine or therapy for the microbiome that could come with further advances in the field. However, he also makes sure to bring up some still-outstanding questions about the human microbiome that require further research, most notably, what exactly makes a healthy human microbiome? Here’s hoping the research de la Fuente mentions can illuminate a path to the answer.
The Department of Bioengineering at the University of Pennsylvania is excited to welcome César de la Fuente, Ph.D., as an Assistant Professor of Bioengineering. De la Fuente, who is also an Assistant Professor in the Department of Psychiatry and Microbiology in the Perelman School of Medicine and was recently named a Penn Presidential Professor, is the principal investigator of the de la Fuente Lab with current projects including the development of computer-made antibiotics, microbiome engineering technologies, and synthetic neuromicrobiology tools.
Dr. de la Fuente has wanted to learn the mysteries of the world around him from a young age, from the origins of life and human consciousness to how diseases can affect the body. His dream of understanding the building blocks of life began to take shape when he enrolled as a graduate student at the University of British Columbia to study microbiology, immunology, and protein engineering. After earning his Ph.D. in these subjects, de la Fuente went on to complete a post doctorate in synthetic biology and computer science at the Massachusetts Institute of Technology (MIT).
Recently, MIT recognized Dr. de la Fuente on its “35 Innovators Under 35” list, which honored de la Fuente as one of the world’s top 35 innovators and as a pioneer for his use of technology to improve antibiotics. Furthermore, GEN recently listed Dr. de la Fuente on its “Top 10 Under 40” list of young leaders in the life sciences, noting his development of transformative biotechnologies as a potential solution to antibiotic resistance. De la Fuente refers to this latest research as “Machine Biology,” a crossover of life and technology that “brings together elements of machines in order to computerize biological systems.”
His creativity in the merging of so many domains of science echoes throughout de la Fuente’s general approach to research and academia as well. While he emphasizes a thinking-from-the-ground-up approach, he also feels that “heterogeneous groups make better ideas,” and thus strives to maintain diversity in his lab — currently his entire lab is made up of international students and postdocs. In the future, de la Fuente hopes to extend his love of mentorship to the classroom in a course exploring the intersection of microbiology and synthetic biology, overlapping in a way similar to his research. We can’t wait for all of the innovation and creativity in engineering that de la Fuente will undoubtedly bring to our department.
“It’s part of our ethos that technology can and should be a force for good. Our annual list of 35 innovators under 35 is a way of putting faces on that idea,” reads the 2019 award announcement. “This year’s list shows that even in our hard, cynical world, there are still lots of smart people willing to dedicate their lives to the idea that technology can make a safer, fairer world.”
De la Fuente was named in the list’s “Pioneers” category for his work researching antibiotics with a computational approach. Using algorithms, he creates artificial antibiotics to better understand how bacteria will evolve and how scientists can optimize treatments. De la Fuente, who was also recently featured in GEN’s Top 10 Under 40 list, further expands his search for medical solutions by extensively studying a variety of proteins, searching for molecules to develop into antimicrobials.
Included in the honor of being named on the 2019 Innovators List is an invitation for de la Fuente to speak at the EmTech MIT conference in September, an event that reflects on the potential impacts of the year’s biggest developments.
De la Fuente, who started at Penn earlier this year, was recognized because he “is pioneering the computerization of biological systems for the development of transformative biotechnologies designed to solve societal grand challenges such as antibiotic resistance.”