Chris Petzold

Divisions

Biological Systems and Engineering

• Process Engineering & Analytics

Biography

Chris Petzold is a staff scientist within Berkeley Lab’s Biological Systems and Engineering Division. He earned his Ph.D. in Chemistry from Purdue University, specializing in gas-phase ion chemistry and mass spectrometric methods development. Following his doctoral studies, he conducted post-doctoral research at UC-Berkeley, applying mass spectrometry to the fields of glycomics and lipidomics. In 2005, Chris joined Jay Keasling’s research group to lead efforts in understanding the systemic impacts of metabolic engineering on host microbes.

As the Director of the Functional Genomics Research Group at the Joint BioEnergy Institute (JBEI), Chris focuses on developing mass spectrometric solutions to the complex challenges of biofuel and bioproduct manufacturing. His expertise lies in bridging high-throughput analytical technologies with advanced metabolic engineering to enable the rational design and optimization of novel biosynthetic pathways.

Research Interests

Bioanalytical Mass Spectrometry, Proteomics, Analytical Chemistry, Metabolic Engineering

Current Projects & Expertise

• Automated DBTL Workflows: Integrating laboratory automation for reproducible data acquisition to enable machine learning (ML) and Artificial Intelligence to rapidly navigate vast parametric design spaces. Recent work includes guiding the development of automated methods for microbial culture growth, sampling, data acquisition, and analysis.
• High-Throughput Screening (HTS) & RapidFire-MS: Implementing high-throughput mass spectrometry (RapidFire-MS) for the HTS of microbial libraries. This includes quantifying diverse targets such as isoprenol, isoprenol acetate, 3-hydroxypropionic acid (3-HP), and various other bioproducts to accelerate biomanufacturing timelines.
• Advanced Proteomics Methods: Implementing high-throughput targeted and shotgun proteomics to validate gene perturbations (e.g., CRISPRi effectiveness) and uncover biological mechanisms driving production improvements.
• Data Management & Interoperability: Leading JBEI efforts in biological data management to enable AI/ML workflows. This includes developing robust systems for automated data import, ensuring data interoperability through metadata capture and strong ontologies, and fostering data sharing.