Machine Learning in Metabolic Engineering

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

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Machine Learning Applications for Mass Spectrometry-Based Metabolomics

Metabolites ◽

10.3390/metabo10060243 ◽

2020 ◽

Vol 10 (6) ◽

pp. 243 ◽

Cited By ~ 7

Author(s):

Ulf W. Liebal ◽

An N. T. Phan ◽

Malvika Sudhakar ◽

Karthik Raman ◽

Lars M. Blank

Keyword(s):

Machine Learning ◽

Mass Spectrometry ◽

Data Analysis ◽

Metabolic Engineering ◽

Data Representation ◽

Heterogeneous Data ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

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Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.612893 ◽

2021 ◽

Vol 9 ◽

Author(s):

Somtirtha Roy ◽

Tijana Radivojevic ◽

Mark Forrer ◽

Jose Manuel Marti ◽

Vamshi Jonnalagadda ◽

...

Keyword(s):

Machine Learning ◽

Metabolic Engineering ◽

Design Science ◽

Synthetic Data ◽

Machine Learning Algorithms ◽

Computational Tools ◽

Multimodal Data ◽

The Past ◽

Commodity Chemicals ◽

Testing Algorithms

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains.

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Machine learning for metabolic engineering: A review

Metabolic Engineering ◽

10.1016/j.ymben.2020.10.005 ◽

2020 ◽

Author(s):

Christopher E. Lawson ◽

Jose Manuel Martí ◽

Tijana Radivojevic ◽

Sai Vamshi R. Jonnalagadda ◽

Reinhard Gentz ◽

...

Keyword(s):

Machine Learning ◽

Metabolic Engineering

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Modeling regulatory networks using machine learning for systems metabolic engineering

Current Opinion in Biotechnology ◽

10.1016/j.copbio.2020.02.014 ◽

2020 ◽

Vol 65 ◽

pp. 163-170 ◽

Cited By ~ 1

Author(s):

Mun Su Kwon ◽

Byung Tae Lee ◽

Sang Yup Lee ◽

Hyun Uk Kim

Keyword(s):

Machine Learning ◽

Metabolic Engineering ◽

Regulatory Networks ◽

Systems Metabolic Engineering

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Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2020.608918 ◽

2020 ◽

Vol 8 ◽

Author(s):

Ahmad Bazli Ramzi ◽

Syarul Nataqain Baharum ◽

Hamidun Bunawan ◽

Nigel S. Scrutton

Keyword(s):

Machine Learning ◽

Natural Products ◽

Metabolic Engineering ◽

Synthetic Biology ◽

Production Capacity ◽

Biosynthetic Pathways ◽

Biosynthetic Genes ◽

Bioactive Natural Products ◽

Beneficial Uses ◽

Analytical Tools

Increasing demands for the supply of biopharmaceuticals have propelled the advancement of metabolic engineering and synthetic biology strategies for biomanufacturing of bioactive natural products. Using metabolically engineered microbes as the bioproduction hosts, a variety of natural products including terpenes, flavonoids, alkaloids, and cannabinoids have been synthesized through the construction and expression of known and newly found biosynthetic genes primarily from model and non-model plants. The employment of omics technology and machine learning (ML) platforms as high throughput analytical tools has been increasingly leveraged in promoting data-guided optimization of targeted biosynthetic pathways and enhancement of the microbial production capacity, thereby representing a critical debottlenecking approach in improving and streamlining natural products biomanufacturing. To this end, this mini review summarizes recent efforts that utilize omics platforms and ML tools in strain optimization and prototyping and discusses the beneficial uses of omics-enabled discovery of plant biosynthetic genes in the production of complex plant-based natural products by bioengineered microbes.

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