The COVID-19 pandemic has sparked a wave of new research and development at the Lab, and Nisha Mulakken is very busy. The biostatistician has enhanced the Lawrence Livermore Microbial Detection Array (LLMDA) system with detection capability for all variants of SARS-CoV-2. The technology detects a broad range of organisms—viruses, bacteria, archaea, protozoa, and fungi—and has demonstrated novel species identification for human health, animal health, biodefense, and environmental sampling scenarios.

The LLMDA relies on high performance computing to compare DNA sequences. Nisha added 60-base pair probes to the LLMDA to target the SARS-CoV-2 genome, then ran multiple algorithms in parallel over the 41,450 SARS-CoV-2 reference sequences and compared those regions against genomes from all other known viruses. She states, “After optimizing for various parameters, I settled on a set of 78 probes out of over 77,000 candidates that should be able to uniquely identify any SARS-CoV-2 genome as well as distinguish some strains of SARS-CoV-2 from others.” Given the pandemic’s urgency, Nisha accomplished this optimization in about a month using LLNL’s Corona supercomputing cluster and funding from the CARES Act. (Watch her discuss the project on a video episode of The Data Standard Podcast.)

Another timely project requires analysis of mutations in SARS-CoV-2 proteins to support future discovery of therapeutic compounds. Nisha, who works in the Global Security Computing Applications Division, explains, “By comparing the frequency of mutations between binding sites and surrounding regions, classifying mutations by the type of protein secondary structure they are found in, and evaluating the solvent accessibility of a mutation based on whether it is exposed or buried within protein models, we created a prototype pipeline that can rank viral protein candidates as drug targets.” This work builds on an analysis pipeline originally designed for the Zika virus.

These types of real-world challenges—where biosecurity, bioinformatics, statistical analysis, and computer science intersect—have long motivated Nisha, and she has seized the Lab’s opportunities in these fields. “Every year, I take on projects that are very different from things I’ve done before. I can work on the same team for a long time without ever getting bored,” she says. Her Lab career has spanned 2000 to 2008 and 2017 to the present.

Nisha first heard about the Lab’s internships when she was an undergrad studying genetics at UC Davis. LLNL computer scientist Tom Slezak invited her to attend a day-long bioinformatics course he taught at the university. “I was the only undergraduate and only woman in the room,” she recalls. With a recommendation from a teaching assistant and encouragement from Tom, Nisha became an LLNL intern after graduation in 2000.

As a UC Berkeley graduate student in biostatistics, Nisha returned to the Lab for three more summers. “The internships solidified my vision for my future career. At the start of my first summer, I had taken only one Java programming class. I learned about databases, biostatistics for gene expression analyses, building a system for finding microbial DNA signatures, and various other topics each summer. The internships taught me that I needed to strengthen my computer science and statistics skills in order to make the most of a career in the multidisciplinary field of bioinformatics,” she states.

Nisha enjoys applying computational techniques to the biological world. She explains, “It’s exciting to see how the tools and technology available to solve these problems have advanced throughout the years, creating new scaling problems but also creating new opportunities to tackle difficult problems.” Nisha is also motived by applications that improve society or human life. She was a Lab intern on September 11, 2001. “Working on the biosecurity mission took on new meaning for me after that day,” she says.

Nisha has mentored several students in bioinformatics projects over the years. “The opportunities we are exposed to early in our careers can shape the limits we place on ourselves and our approaches to challenges we encounter throughout our careers,” she states, emphasizing the importance of normalizing women’s presence in technical fields. Through internship opportunities and supportive mentoring, she adds, “Women can contribute their unique talents to solving scientific problems instead of being intimidated before giving themselves a fair chance.”

In 2020, Nisha mentored Duke University graduate student Emilia Grzesiak in LLNL’s Data Science Summer Institute (DSSI). Their project applied machine learning to trace CRISPR technology vectors to the source lab. Nisha explains, “The CRISPR process creates small signatures within the vectors that are not easy to detect without the aid of targeted algorithms. Emilia used a convolutional neural network to identify the source lab from the patterns in the vector sequences. She replicated the results from literature and dramatically improved the accuracy using different model optimizations.”

Nisha was named the DSSI’s co-director in 2021. She says, “I hope the students will experience the Lab’s collaborative culture, learn about academic topics and practical applications they may not have been exposed to yet, and genuinely enjoy getting to know each other and their mentors.”

—Holly Auten