A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria
Open accessAbstract
Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k -means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.
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Integration of multi-omics data with genome-scale metabolic models and machine learning has demonstrated that cross-omic features explain cyanobacterial metabolic adaptation to fluctuating light and nutrient conditions that transcriptomics alone cannot reveal.
“The integration of multi-omics data with GEMs and machine learning has further demonstrated that cross-omic features explain cyanobacterial metabolic adaptation to fluctuating light and nutrient conditions that transcriptomics alone cannot reveal, illustrating the power of this combined approach for informing rational engineering decisions [164].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [164]
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Experimental — accuracy not yet measuredGenome-scale metabolic models, flux analysis, and multi-omics datasets can be employed to predict interactions between introduced pathways and native carbon, energy, and redox metabolism.
“Genome-scale metabolic models, flux analysis, and multi-omics datasets can be employed to predict interactions between introduced pathways and native carbon, energy, and redox metabolism [164].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [164]
The Introduction describes integrating genome-scale metabolic models with omics data and using flux balance analysis to improve metabolic and phenotypic predictions. The Results’ pathway-level analysis examines native carbon- and energy-related metabolism. However, the paper does not show these methods predicting interactions between introduced pathways and native carbon, energy, and redox metabolism; its analysis focuses on cyanobacterial responses to environmental conditions. (LLM, conf=0.84)
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Synthetic Biology Meets Machine Learning
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A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling
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Future Prospects and Emerging Technologies
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- Authors
- Vijayakumar, S., Rahman, P. K. S. M., & Angione, C.
- Affiliations
- Teesside University (GB)
- NovaBiotics (United Kingdom) (GB)
- Corresponding author
- Angione, Claudio
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 23
- Issue
- 12
- Pages
- 101818
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7744713
- OpenAlex
- W3109520851
- Cited by
- 69
- References
- 80
- Citations by year
- 2026: 15 · 2025: 14 · 2024: 12 · 2023: 10 · 2022: 12 · 2021: 6
- FWCI
- 2.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Teesside University Research Portal (Teesside University) (submittedVersion, cc-by)
- Funders
- Università degli Studi di Padova
- Pennsylvania State University
- UK Research and Innovation
- Biotechnology and Biological Sciences Research Council (CBMNet-PoC-D0156)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- Synechococcus sp. PCC 7002, cyanobacteria, genome-scale metabolic models, metabolic adaptation, photosynthesis, energy metabolism, light intensity, salinity, LASSO regularization, principal component analysis, K-means clustering, biofuel production
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