OUR RESEARCH

Feliza Bourguet was inspired to enter the field of molecular biology when she first learned about human genetics and the Punnett square, the diagram that indicates predicted genetic outcomes of offspring from cross breeding. Her interest in genes has not waned at all. Feliza’s day-to-day work at LLNL focuses on gene design for protein synthesis, expression, purification and analysis. Her research supports both the Physical and Life Sciences and Global Security Directorates at the Laboratory. “I’m particularly proud of my contributions on nanolipoprotein particle (NLP) projects, showing that NLPs can be used to characterize membrane-associated protein complexes and serve as scaffolds for component vaccines,” she says.   

Feliza and fellow researchers received a patent for developing antibiotics to fight antibiotic-resistant bacteria, known as superbugs, by using a pathogen’s own genes against itself. More recently, she and Laboratory scientists have used antibodies from the early 2000s SARS-1 outbreak to engineer antibodies for the virus causing COVID-19.  

Feliza has worked at LLNL since earning her M.S. in applied biosciences. “I enjoy working in a diverse scientific environment where people with a variety of backgrounds collaborate toward a common goal,” she says. “My colleagues are wonderful, and I love the work I do.” Outside the Laboratory, Feliza plays violin in the Livermore-Amador Symphony. 

Feliza earned an M.S. in applied biosciences and a B.S. in molecular and cell biology from the University of Arizona. 

Thomas Desautels is a staff scientist in biomedical machine learning within the Computational Engineering Division.

While at LLNL, Thomas has been a core contributor to the LLNL antibody and vaccine antigen design program. As principal investigator, he led software, HPC workflow, simulation and machine learning efforts to build and mature an antibody/antigen design system. Thomas also co-led the operational protein design efforts that created hundreds of candidate prophylactic and therapeutic antibodies against SARS-CoV-2.

Thomas joined LLNL in 2017 after working in industry on electronic health record monitoring and postdoctoral work in neural decoding. He has a Ph.D. and M.S. in mechanical engineering from the California Institute of Technology, where his thesis addressed spinal cord therapy via active learning for control of epidural electrostimulation. He holds a B.S. in biomedical engineering from the University of California, Davis.

Crystal Jaing is working to predict and detect future pathogen outbreaks using advanced genomic technologies such as the Lawrence Livermore Microbial Detection Array (LLMDA). In fact, Crystal and her team developed the LLMDA technology, which provides the most comprehensive microbial detection for more than 12,000 species of microbes. The LLMDA won a R&D 100 award in 2017 and the Federal Laboratory Consortium Excellence in Technology Transfer award in 2019. Further applications of the LLMDA include drug safety, food safety, biodefense, public health and the analysis of microbes from the International Space Station — a NASA-funded project in which Crystal served as principal investigator to evaluate the potential for pathogenic microbes that could cause health problems for astronauts.   

As a principal investigator for Global Security’s Biological Science and Security Program, Crystal actively manages and leads collaborations with academia, industry and government agencies to develop and apply genomics and pathogen detection technologies to solve problems in biosecurity, agricultural security, biosurveillance and public health.   

Crystal holds a Ph.D. in molecular biology and biochemistry from the Indiana University School of Medicine and a B.S. in chemistry from Nankai University. 

Dante Ricci leverages the power of artificial intelligence (AI) to address complex bioscience challenges. For example, he’s exploring ways to revolutionize medical countermeasure development through AI as part of the Generative Unconstrained Intelligent Drug Engineering (GUIDE) project, a multi-institutional effort aimed at developing tools that will enable a rapid response to emerging biothreats. He also leads an ambitious, collaborative effort to harness AI, integrated with mega-scale biological experimentation and LLNL’s supercomputing capabilities, to enable in silico design of fit-for-purpose biomolecules from scratch.

His journey to LLNL started with a serendipitous interaction during his time conducting research at Stanford University, which resulted in an opportunity to collaborate with LLNL scientists. He then conducted bioscience research in the private sector before joining LLNL in 2021.

Dante is passionate about the potential of AI to transform drug discovery, envisioning a future where antibodies can be identified within a week of detecting a new pathogen. And with LLNL’s expanding mission focus on bioresilience, he’s excited to be part of the Lab’s multidisciplinary research environment, where he can leverage powerful computational tools, along with new experimental capabilities — such as the Rapid Response Laboratory — to help develop a new generation of predictive and therapeutic tools to respond to biothreats.

According to Dante, the key to these ambitious research aims is collaboration. He serves on teams comprised of bioscientists and computational experts at LLNL, as well as external collaborators who are some of the world’s leading scientists in antibody design and discovery.

As a young person, Carlos Valdez excelled in art and soccer. But his first love was science. He says, “Science gave me a rational explanation for everything and a solution for every problem.” Today, Carlos is a synthetic chemist at the Laboratory’s Forensic Science Center (FSC).   

Carlos serves on a specialized team to identify unknown chemical, biological, radiological, nuclear and explosive threat substances. Because FSC chemists work with real-world samples from around the globe — often from environments where groups of people may have been exposed to the substance — acting quickly and accurately has a significant impact on human lives. Carlos’s role is to synthesize a sufficient amount of the sample material so his colleagues can use it for analysis and identification tests. In addition, Carlos leads a multidisciplinary team of scientists who discovered the first nerve-agent antidote that can cross the blood-brain barrier. Before joining LLNL, Carlos’ scientific career included experience at Florida International University, the National Cancer Institute, the University of California Berkeley, the Scripps Research Institute and Rigel Pharmaceuticals.    

Carlos holds a Ph.D. in organic chemistry from the University of California, Berkeley, and a B.S. in chemistry from Florida International University.  He has been recognized at the lab with many awards, including the 2022 Global Security Directorate Spot Award, the 2021 Global Security Directorate Silver and Gold Awards, the 2020 Physical and Life Sciences Directorate Award and a 2019 LLNL Mid-Career Research Award.

Nekesa Wanjala is a bio-automation engineer in the Materials Engineering Division. In this role, she works on various capability development projects for high-throughput biological experimentation at LLNL, focusing on biosecurity and therapeutics projects in collaboration with various institutions. Recently, she has been involved with the Generative Unconstrained Intelligent Drug Engineering (GUIDE) program — an interdisciplinary effort to accelerate drug development.

She began her career at Memorial Sloan Kettering Cancer Center in translational cancer research where she developed organoid models for the study and treatment of prostate cancer. Since then, she has led and contributed to developing high-throughput robotic diagnostic platforms for the early detection, diagnosis and treatment monitoring of cancer and other genetic ailments at Grail, Synthego, CareDx and Invitae.

Nekesa completed her master’s in bioengineering innovation and design at Johns Hopkins University and a B.S. in biomedical engineering at Yale University.