Leveraging knowledge engineering and machine learning for microbial bio-manufacturing
Abstract
Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O2). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learningmore »
- Authors:
-
- Washington Univ. in Saint Louis, Saint Louis, MO (United States)
- Iowa State Univ., Ames, IA (United States)
- Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); DOE, Agile BioFoundry, Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Washington Univ., St. Louis, MO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office; USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- OSTI Identifier:
- 1510752
- Alternate Identifier(s):
- OSTI ID: 1529157; OSTI ID: 1564528
- Grant/Contract Number:
- AC02-05CH11231; SC0018324; MCB 1616619; DESC0018324
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Biotechnology Advances
- Additional Journal Information:
- Journal Volume: 36; Journal Issue: 4; Journal ID: ISSN 0734-9750
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; Deep learning; Design-build-test-learn; Genome scale modeling; Metabolic burdens
Citation Formats
Oyetunde, Tolutola, Bao, Forrest Sheng, Chen, Jiung -Wen, Martin, Hector Garcia, and Tang, Yinjie J. Leveraging knowledge engineering and machine learning for microbial bio-manufacturing. United States: N. p., 2018.
Web. doi:10.1016/j.biotechadv.2018.04.008.
Oyetunde, Tolutola, Bao, Forrest Sheng, Chen, Jiung -Wen, Martin, Hector Garcia, & Tang, Yinjie J. Leveraging knowledge engineering and machine learning for microbial bio-manufacturing. United States. https://doi.org/10.1016/j.biotechadv.2018.04.008
Oyetunde, Tolutola, Bao, Forrest Sheng, Chen, Jiung -Wen, Martin, Hector Garcia, and Tang, Yinjie J. Thu .
"Leveraging knowledge engineering and machine learning for microbial bio-manufacturing". United States. https://doi.org/10.1016/j.biotechadv.2018.04.008. https://www.osti.gov/servlets/purl/1510752.
@article{osti_1510752,
title = {Leveraging knowledge engineering and machine learning for microbial bio-manufacturing},
author = {Oyetunde, Tolutola and Bao, Forrest Sheng and Chen, Jiung -Wen and Martin, Hector Garcia and Tang, Yinjie J.},
abstractNote = {Genome scale modeling (GSM) predicts the performance of microbial workhorses and helps identify beneficial gene targets. GSM integrated with intracellular flux dynamics, omics, and thermodynamics have shown remarkable progress in both elucidating complex cellular phenomena and computational strain design (CSD). Nonetheless, these models still show high uncertainty due to a poor understanding of innate pathway regulations, metabolic burdens, and other factors (such as stress tolerance and metabolite channeling). Besides, the engineered hosts may have genetic mutations or non-genetic variations in bioreactor conditions and thus CSD rarely foresees fermentation rate and titer. Metabolic models play important role in design-build-test-learn cycles for strain improvement, and machine learning (ML) may provide a viable complementary approach for driving strain design and deciphering cellular processes. In order to develop quality ML models, knowledge engineering leverages and standardizes the wealth of information in literature (e.g., genomic/phenomic data, synthetic biology strategies, and bioprocess variables). Data driven frameworks can offer new constraints for mechanistic models to describe cellular regulations, to design pathways, to search gene targets, and to estimate fermentation titer/rate/yield under specified growth conditions (e.g., mixing, nutrients, and O2). This review highlights the scope of information collections, database constructions, and machine learning techniques (such as deep learning and transfer learning), which may facilitate "Learn and Design" for strain development.},
doi = {10.1016/j.biotechadv.2018.04.008},
journal = {Biotechnology Advances},
number = 4,
volume = 36,
place = {United States},
year = {Thu May 03 00:00:00 EDT 2018},
month = {Thu May 03 00:00:00 EDT 2018}
}
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