Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review

Aug 3, 2026

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Advances in genome sequencing and metabolic engineering enable reengineering of cellular functions, while the growth of omics data shifts molecular biology toward data-driven workflows. This review consolidates research published from 2020 to 2023 that combines constraint-based modeling with machine learning to optimize valuable metabolites. Studies are collected from Web of Science and Scopus using terms such as machine learning, flux balance analysis, and metabolic engineering, yielding 13 records. The paper summarizes current in silico approaches, discusses integration methodology, highlights research gaps, and proposes future directions for hybrid optimization strategies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Metabolic engineering and metabolite optimization

Constraint-based modeling (CBM) and its variants

Challenges in CBM and traditional optimization

Hybrid approaches: machine learning with CBM

Role of omics data and big-data driven analysis

(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 14, No. 10, 2023 Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review Kauthar Mohd Daud 1 , Ridho Ananda2 , Suhaila Zainudin3 , Chan Weng Howe4 , Kohbalan Moorthy5 , Nurul Izrin Binti Md Saleh6 Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia 1 , 2 , 3 Institut Teknologi Telkom Purwokerto, Indonesia2 UTM Big Data Centre, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor Malaysia4 Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang Malaysia5 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia6 Abstract—The advances in genome sequencing and metabolic engineering have allowed the reengineering of the cellular function of an organism. Furthermore, given the abundance of omics data, data collection has increased considerably, thus shifting the perspective of molecular biology. Therefore, researchers have recently used artificial intelligence and machine learning tools to simulate and improve the reconstruction and analysis by identifying meaningful features from the large multi-omics dataset. This review paper summarizes research on the hybrid of constraint-based models and machine learning algorithms in optimizing valuable metabolites. The research articles published between 2020 and 2023 on machine learning and constraintbased modeling have been collected, synthesized, and analyzed. The articles are obtained from the Web of Science and Scopus databases using the keywords: “Machine learning”,“flux balance analysis”, and “metabolic engineering”. At the end of the search, this review contained 13 records. This review paper aims to provide current trends and approaches in in silico metabolic engineering while providing research directions by highlighting the research gaps. In addition, we have discussed the methodology for integrating machine learning and constraint-based modeling approaches. Keywords—Flux balance analysis; genome-scale metabolic model; machine learning; metabolic engineering I. INTRODUCTION Microorganisms have been used in industrial sectors such as food processing, chemical manufacturing, pharmaceuticals, fermentation, and others. Advances in genome sequencing have resulted in several innovations that allow researchers to gain in-depth knowledge and information about an organism. One of these advancements is metabolic engineering, which reengineers the cellular function of an organism. In the 1990s, metabolic engineering was introduced to describe recombinant DNA technology for optimizing microbial activity [1] . Metabolic engineering aims to optimize the synthesis of desired metabolites by directing the metabolic flow and the fluxes toward the desired metabolites. The designs are categorized into two types: [1] targeting metabolic network components, such as gene/reaction knockout/knock-in, and [2] enhancing the metabolic network by altering it using network reconstruction tools or incorporating new non-native pathways into the host. Over the previous few decades, there has been a noticeable breakthrough, such as incorporating adenosylcobinamide phosphate biosynthesis from Rhadobacter capsulatus into the E.coli strain, which improves the vitamin B 12 to 307 µg/g [2] . In another case, the yeast was engineered to improve the production of rubusoside and rebaudiosides, leading to 1368.6 mg/L and 132.7 mg/L, respectively [3] . Although metabolic pathway optimization technologies have shown promise, an incomplete understanding of the connection between target cell phenotype and genotype impedes their further development. This results in the prevalent utilizati

FAQs

What problem does this review address about producing valuable metabolites?

It addresses how to optimize the production of valuable metabolites using hybrid strategies that integrate constraint-based modeling with machine learning to improve reconstruction and analysis from large multi-omics datasets.

How were the reviewed studies selected and how many records were included?

The studies were collected from Web of Science and Scopus using keywords such as machine learning, flux balance analysis, and metabolic engineering, and the review ended with 13 records.

What are the main challenge(s) in constraint-based modeling that motivate hybrid methods?

A key issue is that desired fluxes may not be limited to a single solution due to network redundancy, and selecting appropriate reactions/genes for knockout is difficult, laborious, and time-consuming.

Aug 3, 2026

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