Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering
Abstract
The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 02, 2019
- Source ID
- 10.1002/biot.201800416
Entities
People
- Hal S. Alper
- Kristin V. Presnell
Organizations
- Air Force Office of Scientific Research
- University of Texas at Austin