# Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026

*Canonical URL: https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026*

## What topics and trends defined most-cited Microbial Metabolic Engineering and Bioproduction research in the Class of 2026?

Most-cited 2024 microbial metabolic engineering research is dominated by genome-scale metabolic modeling, enzyme function prediction, and engineered model chassis including Saccharomyces cerevisiae and Escherichia coli. Trend shifts highlight rapid growth in artificial intelligence for enzyme design, machine learning models, and microbial consortia, alongside declining focus on legacy bioenergy processes like carbon dioxide fixation.

## At a glance

| Fact | Value |
| --- | --- |
| Field | Microbial Metabolic Engineering and Bioproduction |
| Cohort label | Class of 2026 (2024 publications) |
| Papers analyzed | 5,979 |
| Papers ranked | 20 |
| Top topics in ranked papers | Genome-scale metabolic modeling, Enzyme function prediction, Saccharomyces cerevisiae, Microbial consortia, Heterologous expression |
| Publication window | Jan 1, 2024 – Dec 31, 2024 |
| Eligibility | Research articles; reviews excluded |
| Citation window | 18 months post-publication |
| 18m citation range | 37–120 |
| Data source | OpenAlex · Retrieved Jul 2026 |
| License | CC BY 4.0 |

## Rankings

| Rank | Title | Authors | Corresponding authors | Affiliation | Journal | 18m citations | DOI |
| ---: | --- | --- | --- | --- | --- | ---: | --- |
| 1 | Engineering natural microbiomes toward enhanced bioremediation by microbiome modeling | Zhepu Ruan, Kai Chen, Weimiao Cao, Lei Meng, Bingang Yang, Mengjun Xu, Youwen Xing, Pengfa Li, Shiri Freilich, Chen Chen, Yanzheng Gao, Jiandong Jiang, Xihui Xu | Yanzheng Gao, Jiandong Jiang, Xihui Xu | Nanjing Agricultural University, China | Nature Communications | 120 | 10.1038/s41467-024-49098-z |
| 2 | Expanding chemistry through in vitro and in vivo biocatalysis | Elijah N Kissman, Max B Sosa, Douglas C Millar, Edward J Koleski, Kershanthen Thevasundaram, Michelle C Y Chang | Michelle C. Y. Chang | University of California Berkeley, United States | Nature | 112 | 10.1038/s41586-024-07506-w |
| 3 | Engineered enzymes for the synthesis of pharmaceuticals and other high-value products | Manfred T. Reetz, Ge Qu, Zhoutong Sun | Manfred T. Reetz, Ge Qu, Zhoutong Sun | Max-Planck-Institut für Kohlenforschung, Germany | Nature Synthesis | 94 | 10.1038/s44160-023-00417-0 |
| 4 | Complete biosynthesis of QS-21 in engineered yeast | Yuzhong Liu, Xixi Zhao, Fei Gan, Xiaoyue Chen, Kai Deng, Samantha A Crowe, Graham A Hudson, Michael S Belcher, Matthias Schmidt, Maria C T Astolfi, Suzanne M Kosina, Bo Pang, Minglong Shao, Jing Yin, Sasilada Sirirungruang, Anthony T Iavarone, James Reed, Laetitia B B Martin, Amr El-Demerdash, Shingo Kikuchi, Rajesh Chandra Misra, Xiaomeng Liang, Michael J Cronce, Xiulai Chen, Chunjun Zhan, Ramu Kakumanu, Edward E K Baidoo, Yan Chen, Christopher J Petzold, Trent R Northen, Anne Osbourn, Henrik Scheller, Jay D Keasling | Jay D. Keasling | University of California, Berkeley, United States | Nature | 72 | 10.1038/s41586-024-07345-9 |
| 5 | Combined transcriptomic and metabolomic analyses of temperature response of microalgae using waste activated sludge extracts for promising biodiesel production | Xueting Song, Fanying Kong, Bing-Feng Liu, Qingqing Song, Nan-Qi Ren, Hong-Yu Ren | Nanqi Ren, Hong‐Yu Ren | Harbin Institute of Technology, China | Water Research | 66 | 10.1016/j.watres.2024.121120 |
| 6 | Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures | Yidong Song, Qianmu Yuan, Sheng Chen, Yuansong Zeng, Huiying Zhao, Yuedong Yang | Yuedong Yang | Sun Yat-sen University, China | Nature Communications | 63 | 10.1038/s41467-024-52533-w |
| 7 | A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol | Michael A Reiter, Timothy Bradley, Lars A Büchel, Philipp Keller, Emese Hegedis, Thomas Gassler, Julia A Vorholt | Michael Reiter, Julia A. Vorholt | ETH Zurich, Switzerland | Nature Catalysis | 61 | 10.1038/s41929-024-01137-0 |
| 8 | Automated in vivo enzyme engineering accelerates biocatalyst optimization | Enrico Orsi, Lennart Schada von Borzyskowski, Stephan Noack, Pablo I Nikel, Steffen N Lindner | Steffen N. Lindner | Technical University of Denmark, Denmark | Nature Communications | 55 | 10.1038/s41467-024-46574-4 |
| 9 | H2 production from coal by enriching sugar fermentation and alkane oxidation with hyperthermophilic resistance microbes in municipal wastewater | Huaiwen Zhang, Yaojing Qiu, Tairan Liu, Xinya Yang, Ruixiao Yan, Heng Wu, Anjie Li, Jian Liu, Yahong Wei, Yiqing Yao | Yahong Wei, Yiqing Yao | Northwest A&F University, China | Chemical Engineering Journal | 54 | 10.1016/j.cej.2024.151487 |
| 10 | Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0 | Yu Chen, Johan Gustafsson, Albert Tafur Rangel, Mihail Anton, Iván Domenzain, Cheewin Kittikunapong, Feiran Li, Le Yuan, Jens Nielsen, Eduard J Kerkhoven | Eduard J. Kerkhoven | Chalmers University of Technology, Sweden | Nature Protocols | 49 | 10.1038/s41596-023-00931-7 |
| 11 | Unlocking the potential of enzyme engineering via rational computational design strategies | Lei Zhou, Chunmeng Tao, Xiaolin Shen, Xinxiao Sun, Jia Wang, Qipeng Yuan | Jia Wang, Qipeng Yuan | Beijing University of Chemical Technology, China | Biotechnology Advances | 48 | 10.1016/j.biotechadv.2024.108376 |
| 12 | Computational scoring and experimental evaluation of enzymes generated by neural networks | Sean R Johnson, Xiaozhi Fu, Sandra Viknander, Clara Goldin, Sarah Monaco, Aleksej Zelezniak, Kevin K Yang | Aleksej Zelezniak, Kevin Yang | New England Biolabs, United States | Nature Biotechnology | 47 | 10.1038/s41587-024-02214-2 |
| 13 | Strategies to enhance production of metabolites in microbial co-culture systems | Lichun Guo, Bingwen Xi, Liushen Lu | Liushen Lu | Chinese Academy of Fishery Sciences, China | Bioresource Technology | 42 | 10.1016/j.biortech.2024.131049 |
| 14 | mVOC 4.0: a database of microbial volatiles | Emanuel Kemmler, Marie Chantal Lemfack, Andrean Goede, Kathleen Gallo, Serge M T Toguem, Waqar Ahmed, Iris Millberg, Saskia Preissner, Birgit Piechulla, Robert Preissner | Emanuel Kemmler | Charité - University Medicine Berlin, Germany | Nucleic Acids Research | 41 | 10.1093/nar/gkae961 |
| 15 | TRYing to evaluate production costs in microbial biotechnology | Oliver Konzock, Jens Nielsen | Jens Nielsen | Chalmers University of Technology, Sweden | Trends in biotechnology | 40 | 10.1016/j.tibtech.2024.04.007 |
| 16 | Efficient synthesis of squalene by cytoplasmic-peroxisomal engineering and regulating lipid metabolism in Yarrowia lipolytica | Yang Ning, Mengsu Liu, Ziyun Ru, Weizhu Zeng, Song Liu, Jingwen Zhou | Jingwen Zhou | Jiangnan University, China | Bioresource Technology | 38 | 10.1016/j.biortech.2024.130379 |
| 17 | MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction | Jingjing Wang, Zhijiang Yang, Chang Chen, Ge Yao, Xiukun Wan, Shaoheng Bao, Junjie Ding, Liangliang Wang, Hui Jiang | Jingjing Wang | State Key Laboratory of NBC Protection for Civilian, China | Briefings in Bioinformatics | 38 | 10.1093/bib/bbae387 |
| 18 | Yeast metabolism adaptation for efficient terpenoids synthesis via isopentenol utilization | Guangjian Li, Hui Liang, Ruichen Gao, Ling Qin, Pei Xu, Mingtao Huang, Min-Hua Zong, Yufei Cao, Wen-Yong Lou | Yufei Cao, Wen‐Yong Lou | South China University of Technology, China | Nature Communications | 38 | 10.1038/s41467-024-54298-8 |
| 19 | Unveiling the deterministic dynamics of microbial meta-metabolism: a multi-omics investigation of anaerobic biodegradation | Xingsheng Yang, Kai Feng, Shang Wang, Mengting Maggie Yuan, Xi Peng, Qing He, Danrui Wang, Wenli Shen, Bo Zhao, Xiongfeng Du, Yingcheng Wang, Linlin Wang, Dong Cao, Wenzong Liu, Jianjun Wang, Ye Deng | Ye Deng | Chinese Academy of Sciences, China | Microbiome | 37 | 10.1186/s40168-024-01890-1 |
| 20 | Engineering a xylose fermenting yeast for lignocellulosic ethanol production | Yi-Wen Zhang, Jun-Jie Yang, Feng-Hui Qian, Kate Brandon Sutton, Carsten Hjort, Wen-Ping Wu, Yu Jiang, Sheng Yang | Sheng Yang | Chinese Academy of Sciences, China | Nature Chemical Biology | 37 | 10.1038/s41589-024-01771-6 |

## Topic trends

### What Topics Define the Class of 2026?

The Class of 2026 in microbial metabolic engineering and bioproduction is strongly anchored by computational modeling, predictive biochemistry, and robust cellular chassis development. At the core of the field, genome-scale metabolic modeling and enzyme function prediction lead in publication density, reflecting a field-wide emphasis on in silico strain design prior to wet-lab implementation. Workhorse microbial hosts remain central to experimental validation, with Saccharomyces cerevisiae and Escherichia coli appearing across numerous top-ranked studies for high-yield bioproduction and pathway optimization.

Complementing single-organism strain design is a major focus on synthetic microbial consortia and heterologous expression systems. Researchers are increasingly constructing multi-species communities and division-of-labor networks to distribute complex metabolic pathways and relieve cellular metabolic burdens. Additionally, directed evolution, metabolomics, and adaptive laboratory evolution are widely deployed to refine enzyme kinetics, overcome pathway bottlenecks, and increase tolerance toward industrial stress. Together, these topics showcase a highly integrated paradigm that fuses computational flux modeling, machine-learning-assisted enzyme design, and advanced strain engineering to accelerate industrial biomanufacturing.

*Leading research themes*

### How Did Topics Shift from the Class of 2025 to the Class of 2026?

Comparing the Class of 2025 to the Class of 2026 reveals significant momentum toward predictive artificial intelligence and synthetic microbial community engineering. The most prominent surge occurred in Saccharomyces cerevisiae strain engineering, which saw a nearly seven-fold increase in top-paper representation, alongside a more than four-fold rise in directed evolution and computational enzyme prediction. Emerging topics such as protein language models, machine learning for enzyme design, multi-omics integration, and central carbon metabolism optimization entered the top tier of research focus for the first time, reflecting a paradigm shift toward data-driven bioengineering.

Conversely, traditional bioenergy and environmental application topics experienced relative declines. Topics including carbon dioxide fixation, techno-economic analysis, biomass conversion, and biodiesel production dropped substantially in representation compared to the previous cohort. This transition indicates a strategic shift across the microbial metabolic engineering community away from legacy bulk biofuel evaluation and toward high-value biochemical synthesis, precision enzyme engineering, and host-consortium co-culture systems.

*How topics shifted year over year*

## Cite this ranking

```
Pepkio Research Index (PRI). Topics and Trends in Most Cited Microbial Metabolic Engineering and Bioproduction Papers, Class of 2026. https://pri.pepkio.com/top-papers/microbial-metabolic-engineering-and-bioproduction/2026. Accessed 2026-07-29.

Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459
```