Dr. Douglas McCloskey

Independent Consultant

Douglas McCloskey obtained his PhD in 2017 in bioengineering in the lab of Bernhard O. Palsson at the University of California, San Diego. During his studies, he was awarded the prestigious Siebel’s Scholars Foundation grant. Douglas McCloskey was a group leader at the NNF Center for Biosustainability (CfB) at the Technical University of Denmark from 2018 to 2021. At the CfB, he led a group of automation engineers, software engineers, and analytical chemists to develop Big –Omics data generation workflows and led a group of PhDs and Postdocs to develop advanced machine learning and biochemical modeling algorithms to learn from Big –Omics data. Douglas McCloskey was most Chief Scientific Officer and co-founder at AliveX Biotech from 2021 to 2022. At AliveX Biotech, he led the development of an advanced AI and systems biology platform for the identification of biomarkers and drug targets in cancer, autoimmunity, and neurology. Douglas McCloskey currently consults with various enterprises on the adoption and optimization of digitized and automated workflows using robotics and data science in biotechnology, agriculture, and precision medicine.

Deep Generative Modeling in Computational Mass Spectrometry and Metabolomics

Recent advances in deep generative modeling have enabled breakthroughs in realistic image, voice and text synthesis. Similar breakthroughs in chromatogram, mass spectra and metabolomics data generation are on the near horizon.

Webinar

Deep Generative Modeling in Computational Mass Spectrometry and Metabolomics

Webinar

Advances in Multiomics Screening of Microbes in Biotechnology

Design–build–test–learn (DBTL) cycles are commonly used in biotechnology and in natural product discovery as they provide a systemic and efficient framework for the metabolic engineering of microbes.

A novel platform that automates multiomics analyses of microbes could enable faster and better DBLT cycles for your research. In this webinar, the development and validation of the high-throughput, automated platform using model microbial organisms is described, as well as the recent work in applying the platform to cell factory and natural products engineering.