Niels Klitgord, Ph.D.
Microbiome & Metabolic Specialist
Dr. Niels Klitgord is a Computational Biologist who brings 20 years of industry and academic research experience to Monoceros Biosystems. He received his Bachelor of Science degree in molecular biology from the University of New Mexico in 2000, after which he took a position as a fly geneticist at Exelixis in South San Francisco, where he helped run large scale fly screens to identify oncology targets. Moving to Boston in 2004, he made his transition into computational biology studying the networks of protein-protein interactions in the lab of Dr. Mark Vidal. Dr. Niels Klitgord’s love for microbiology was ignited in the Lab of Dr. Daniel Segrè where he earned his PhD in Bioinformatics in 2011 studying and modeling the inter-species interactions of microbial communities through the lens of metabolism. Dr. Niels Klitgord postdoctoral work was in the lab of Dr. Trent Northen, focusing on the interactions of bacterial communities in desert soils, and their role in the carbon cycle. Following his post-doc, he spent a brief stint at BioRad as a bioinformatic staff scientist supporting development of their digital biology platforma and assays. From there he transitioned back to the microbiome, taking a position at Human Longevity setting up microbiome NGS analysis workflows and reporting tools. Niels moved to Viome in 2016, a company focused on the systems of diet, health and the microbiome, to found and lead their bioinformatic group. In 2019 he became the bioinformatic director at Prescient Metabiomics to lead a team that built a computation platform enabling NGS diagnostic testing of complex diseases such as Colorectal Cancer from the microbiome.
Moving to Monoceros Biosystems in mid 2020, Dr. Niels Klitgord is passionate about enabling the understanding of links between biological systems with a focus on the microbiome and human health and the goal of identifying actionable relationships. His broad experience with microbiome, computational platforms, high throughput NGS data, network analysis, statistical and machine learning techniques has enabled him to bridge together the work of researchers in the areas of microbiology, clinical, metabolic and NGS which has led to both the successful development of products, more than 30 peer reviewed articles, and several patents.