Title: Machine learning framework for assessment of microbial factory performance

Abstract

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonablymore » high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).« less

Authors:

Oyetunde, Tolutola  ^[1];

• Search DOE PAGES for author "Oyetunde, Tolutola"
• Search DOE PAGES for ORCID "0000-0002-3875-8613"

Liu, Di ^[1]; Martin, Hector Garcia  ^[2];

• Search DOE PAGES for author "Martin, Hector Garcia"
• Search DOE PAGES for ORCID "0000-0002-4556-9685"

Tang, Yinjie J. ^[1]

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+ Show Author Affiliations

1. Washington Univ., St. Louis, MO (United States). Dept. of Energy, Environmental and Chemical Engineering
2. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); DOE Agile BioFoundry, Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Biological Systems and Engineering Division; Basque Center for Applied Mathematics, Bilbao (Spain)

Publication Date:

Tue Jan 15 00:00:00 EST 2019

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Washington Univ., St. Louis, MO (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)

OSTI Identifier:

1510764

Grant/Contract Number:  

AC02-05CH11231; SC0018324; MCB 1616619

Resource Type:

Accepted Manuscript

Journal Name:

PLoS ONE

Additional Journal Information:

Journal Volume: 14; Journal Issue: 1; Journal ID: ISSN 1932-6203

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; machine learning; metabolic engineering; genetic engineering; principal component analysis; genomic databases; cell metabolism; fermentation; simulation and modeling