Inferring Mechanism of Action of an Unknown Compound from Time Series Omics Data

• Published in: Computational Methods in Systems Biology pp. 238 - 255
• Main Authors: Vertes, Akos, Arul, Albert-Baskar, Avar, Peter, Korte, Andrew R., Li, Hang, Nemes, Peter, Parvin, Lida, Stopka, Sylwia, Hwang, Sunil, Sahab, Ziad J., Zhang, Linwen, Bunin, Deborah I., Knapp, Merrill, Poggio, Andrew, Stehr, Mark-Oliver, Talcott, Carolyn L., Davis, Brian M., Dinn, Sean R., Morton, Christine A., Sevinsky, Christopher J., Zavodszky, Maria I.
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2018
• Series: Lecture Notes in Computer Science
• ISBN: 9783319994284; 331999428X
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-99429-1_14

Identifying the mechanism of action (MoA) of an unknown, possibly novel, substance (chemical, protein, or pathogen) is a significant challenge. Biologists typically spend years working out the MoA for known compounds. MoA determination is especially challenging if there is no prior knowledge and if there is an urgent need to understand the mechanism for rapid treatment and/or prevention of global health emergencies. In this paper, we describe a data analysis approach using Gaussian processes and machine learning techniques to infer components of the MoA of an unknown agent from time series transcriptomics, proteomics, and metabolomics data. The work was performed as part of the DARPA Rapid Threat Assessment program, where the challenge was to identify the MoA of a potential threat agent in 30 days or less, using only project generated data, with no recourse to pre-existing databases or published literature.