What role does metabolomics play in turning multiomic data into actionable biological insight?
Metabolomics shows what is actively happening in the system. It is the functional snapshot that often brings the rest of the multiomic data into focus. Genomics and proteomics can point to changes, but metabolomics helps interpret their meaning. It moves teams from observation to understanding, enabling clearer and more confident decisions.
How does your background in metabolomics shape the way you approach client challenges?
My job is to make sure the study design, the data we generate, and the end goals are all working toward the same objective. Our clients don’t just want more data; they want a study that moves their program forward. So, I start by clarifying what success looks like (e.g., de-risking a target, building confidence in a mechanism, picking a lead, or advancing a biomarker strategy). Once we’re aligned on that, I translate the goal into a fit-for-purpose plan with the right scope, endpoints, and an interpretation path that supports confident business decisions.
That approach is grounded in my hands-on experience applying metabolomics for my own research. I generated and used metabolomics data to understand system-level changes that influence host–pathogen interactions. Because I’ve done this work myself, I pay attention to the practical details that protect data quality and ensure a strong study.
What are the common gaps you see in multiomic study design, and how does metabolomics help address them?
A clear roadmap should precede data generation. One of the biggest gaps I see is teams generating large quantities of multiomic data without a clear plan for how it will be used, integrated, and interpreted. This creates rework and slows everything down. More data isn’t automatically better, so the study must be right-sized to the question and the intended outcome.
Metabolomics also improves efficiency by providing functional context that clarifies multiomic signatures, enabling meaningful insights to be reached quickly without making the study unnecessarily complex. And because each omics layer has different sample-handling requirements, early planning for sample collection and storage is another efficiency lever – it protects data quality up front and prevents costly downstream troubleshooting.
How do you translate complex scientific data into strategic recommendations for clients?
I start by grounding everything in the decision that needs to be made. From there, I focus on distilling the data into what is most relevant and actionable. Not every detail needs to be surfaced, just the pieces that directly inform next steps.
My goal is to simplify complexity without losing meaning. When done well, the data becomes a tool for direction rather than something overwhelming. At the end of the day, the value of the data is in how it is used, and my role is to help make that path clear.
How does collaborating across internal teams (Field Metabolomics Scientists, R&D, bioinformatics, operations, etc.) strengthen client outcomes?
Collaboration across internal teams is essential because each group brings a different perspective, and strong outcomes depend on how well those cross-disciplinary perspectives are aligned. My role is to connect those pieces in a way that keeps the focus on the client’s question and the decision they need to make.
By working closely across teams, we ensure the study is scientifically sound and designed to deliver high-quality, clear, actionable data. When that alignment is in place, it leads to more confident decisions and ultimately better outcomes for the client.
