Technologies for design-build-test-learn automation and computational modelling across the synthetic biology workflow: A review
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Abstract Motivated by the need to parameterize and functionalize dynamic, multiscale simulations, as well as bridge the gap between advancing in silico and laboratory Synthetic Biology practices, this work evaluated and contextualized Synthetic Biology data standards and conversion, modelling and simulation methods, genetic design and optimization, software platforms, machine learning, assembly planning, automated modelling, combinatorial methods, biological circuit design and laboratory automation. This review also discusses technologies related to domain specific languages, libraries and APIs, databases, whole cell models, use of ontologies, datamining, metabolic engineering, parameter estimation/acquisition, robotics, microfluidics and touches on a range of applications. The discussed principles should provide a strong, encompassing foundation for primarily dry laboratory Synthetic Biology automation, reproducibility, interoperability, simulatability, data acquisition, parameterization, functionalization of models, classification, computational efficiency, time efficiency and effective genetic engineering. Applications impact the design-build-test-learn loop, in silico computer assisted design and simulations, hypothesis generation, yield optimization, drug design, synthetic organs, sensors and living therapeutics.
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AI-assisted design-build-test-learn (DBTL) workflows can integrate genome-scale metabolic models, omics datasets, enzyme information and high-throughput screening outputs to prioritise pathway variants, predict metabolic bottlenecks and reduce the number of experimental cycles required for strain optimisation.
“AI-assisted design-build-test-learn (DBTL) workflows can integrate genome-scale metabolic models, omics datasets, enzyme information and high-throughput screening outputs to prioritise pathway variants, predict metabolic bottlenecks and reduce the number of experimental cycles required for strain optimisation [221].”
From Cyanobacteria as a Photosynthetic Chassis for Metabolic Pathway Engineering with Heterologous Gene Expression (2026) · cites this paper as [221]
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Hunsberger, J. G., Simon, C. G., Zylberberg, C., Ramamoorthy, P., Tubon, T. C., Bedi, R., Gielen, K., Hansen, C., Fischer, L., Johnson, J. K., et al. (2020) · Stem Cells Translational Medicine · doi:10.1002/sctm.19-0389 · Cited by 21 · see citing works
Bio-design automation: software + biology + robots
Densmore, D. M., & Bhatia, S. P. (2013) · Trends in biotechnology · doi:10.1016/j.tibtech.2013.10.005 · Cited by 20 · see citing works
A Property-Driven Methodology for Formal Analysis of Synthetic Biology Systems
Konur, S., & Gheorghe, M. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2362531 · Cited by 19 · see citing works
Self-citationConventional Verification for Unconventional Computing: a Genetic XOR Gate Example
Konur, S., Gheorghe, M., Dragomir, C., Ipate, F., & Krasnogor, N. (2014) · Fundamenta Informaticae · doi:10.3233/fi-2014-1093 · Cited by 18 · see citing works
Self-citationJCVI-syn3.0 – A synthetic genome stripped bare!
Sleator, R. D. (2016) · Bioengineered · doi:10.1080/21655979.2016.1175847 · Cited by 17 · see citing works
Whitmore, L. S., Nguyen, B., Pınar, A., George, A., & Hudson, C. M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3025-9 · Cited by 17 · see citing works
Rosenhahn, E., O’Brien, T. J., Zaki, M. S., Sorge, I., Wieczorek, D., Rostasy, K. M., Vitobello, A., Nambot, S., Alkuraya, F. S., Hashem, M. O., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.06.008 · Cited by 17 · see citing works
Riboflow: Using Deep Learning to Classify Riboswitches With ∼99% Accuracy
Premkumar, K. A. R., Bharanikumar, R., & Palaniappan, A. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00808 · Cited by 16 · see citing works
Karagöz, Z., Rijns, L., Dankers, P. Y. W., van Griensven, M., & Carlier, A. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.12.025 · Cited by 16 · see citing works
Cellular 3D-reconstruction and analysis in the human cerebral cortex using automatic serial sections
Larsen, N. Y., Li, X., Tan, X., Ji, G., Lin, J., Rajkowska, G., Møller, J., Vihrs, N., Jon Sporring, A., Sun, F., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02548-6 · Cited by 14 · see citing works
Meta-Stochastic Simulation of Biochemical Models for Systems and Synthetic Biology
Sanassy, D., Widera, P., & Krasnogor, N. (2014) · ACS Synthetic Biology · doi:10.1021/sb5001406 · Cited by 13 · see citing works
SMGen: A Generator of Synthetic Models of Biochemical Reaction Networks
Riva, S. G., Cazzaniga, P., Nobile, M. S., Spolaor, S., Rundo, L., Besozzi, D., & Tangherloni, A. (2022) · Symmetry · doi:10.3390/sym14010119 · Cited by 9 · see citing works
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Matzko, R. O., Mierlă, L., & Konur, S. (2023) · Genes · doi:10.3390/genes14010154 · Cited by 6 · see citing works
Self-citationIntegration of SABIO-RK in workbenches for kinetic model design
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Genetic Circuit for the Early Warning of Lung Cancer using iBioSim
Wang, X., Wang, X., Jiang, J.-B., Sun, J., Pang, S.-S., He, H.-H., & Wang, W. (2016) · ITM Web of Conferences · doi:10.1051/itmconf/20160709019 · Cited by 2 · see citing works
A 3D Multicellular Simulation Layer for the Synthetic Biology CAD Infobiotics Workbench Suite
Matzko, R. O., Mierlă, L., & Konur, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-07802-6_17 · Cited by 2 · see citing works
Self-citationWinBEST-KIT: Biochemical Reaction Simulator for Analyzing Multi-Layered Metabolic Pathways
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Flagged first, then by citations
Cited by
Sorted by citations
Ezeako, E. C., Nwiloh, B. I., Odo, M. C., & Ozougwu, V. E. (2025) · Biochemical Engineering Journal · doi:10.1016/j.bej.2025.109777 · Cited by 14 · see citing works
Wang, Y., Ma, Y., Chang, Y.-H., Yan, J., Zhang, J., Cai, M.-Y., & Wei, K. (2025) · Biology · doi:10.3390/biology14121665 · Cited by 10 · see citing works
A Guide in Synthetic Biology: Designing Genetic Circuits and Their Applications in Stem Cells
Elnaggar, K. S., Gamal, O., Hesham, N., Ayman, S., Mohamed, N., Moataz, A., Elzayat, E. M., & Hassan, N. (2025) · SynBio · doi:10.3390/synbio3030011 · Cited by 10 · see citing works
High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology
Martinez, J. P. O., & Speight, R. E. (2026) · FEMS Yeast Research · doi:10.1093/femsyr/foag003 · Cited by 11 · see citing works
Chakraborty, C., Bhattacharya, M., & Lee, S.-S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1739303 · Cited by 10 · see citing works
DNA synthesis and assembly: From oligonucleotides to complete genomes
Liu, C., Yang, Y., Zhuang, W., Zhang, L., Wang, G., Hu, J., Yao, Y., Ye, C., & Hu, W. (. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.168771 · Cited by 6 · see citing works
Collier, T., da Roza, P. A., Goold, H. D., Singh, S. P., Paulsen, I. T., Walker, R. S. K., & Pretorius, I. S. (2026) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2026.2615047 · Cited by 4 · see citing works
Chatterjee, S., Saha, P., Ganguly, P., & Patel, A. K. (2026) · Revista Brasileira de Farmacognosia · doi:10.1007/s43450-026-00765-w · Cited by 3 · see citing works
Salcedo-Arancibia, F., Gutiérrez, M., & Chavoya, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35050 · Cited by 3 · see citing works
Improving governance in the age of synthetic biology, artificial intelligence, and diverging threats
Sabra, D. M., Frieß, J. L., Giese, B., & Jeremias, G. (2026) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2026.1705143 · Cited by 2 · see citing works
Walshe, J., & Saha, S. K. (2026) · Current Issues in Molecular Biology · doi:10.3390/cimb48060638 · Cited by 1 · see citing works
“…(DBTL) workflows can integrate genome-scale metabolic models, omics datasets, enzyme information and high-throughput screening outputs to prioritise pathway variants, predict metabolic bottlenecks and reduce the number of experimental cycles required for strain optimisation [221].”
In-silico-driven SynCom engineering with cyanobacteria for efficient pesticide degradation: A review
Raj, A., & Shahid, M. (2026) · Algal Research · doi:10.1016/j.algal.2026.104636 · Cited by 1 · see citing works
Enzyme Engineering: From Classical Strategies to AI-Driven Biocatalyst Design
Ullah, M., Rizwan, M., Andoh, V., Raza, A., Naeem, M., Waheed, M. I., & Chen, Y. (2026) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.6c04272 · Cited by 1 · see citing works
McLennan, R. K. J., Freemont, P. S., & Pretorius, I. S. (2026) · Nature Communications · doi:10.1038/s41467-026-77338-x · Cited by 1 · see citing works
Application of synthetic biology in novel drug delivery system
Goswami, R., Santra, S., Rana, M., Pal, B., Chakraborty, M., Basu, B., & Prajapati, B. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33649-2.00005-8
Application of xenonucleotide in synthetic biology
Goswami, R., Saha, S., Halder, A., Sarkar, N., Kumar, A., Roy, K., & Chakraborty, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33649-2.00009-5
An introduction of synthetic biology and important milestones
Das, S., & Chatterjee, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33649-2.00015-0
Advances and trends in the bidirectional transformation between biological data and knowledge
Rong, L. I., Ge, J., ZHANG, X.-B., ZHANG, Y.-J., Da-Ming, C. H. E. N., & Tao, C. (2026) · Shengming kexue · doi:10.3724/cbls.2026032
Ghadekar, P. P., Joshi, A., Wagh, S., & Zendage, V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14772-1_12
Kell, D. B., & Roberts, I. (2026) · Chemical Society Reviews · doi:10.1039/d5cs01387a
Design and Characterization of Genetic Parts in Synthetic Biology
Abdelaal, A. S., & Sorroche, F. G. (2026) · Springer eBooks · doi:10.1007/978-981-95-3957-4_3
Singh, S., Baghel, K., & Prajapati, V. K. (2026) · Springer eBooks · doi:10.1007/978-981-95-3957-4_17
Current Progress and Future Outlook for Synthetic Gene Circuits in Cardiovascular Therapy
Khalilitousi, M., Dhingra, A., Rohani, L., & Weiss, R. (2026) · Biomolecules · doi:10.3390/biom16050754
Computational design of microbial cell factories for sustainable biomanufacturing
Liu, L., Li, C., & Li, F. (2026) · Green Energy & Environment · doi:10.1016/j.gee.2026.07.001
Wang, S., Li, Y., Shi, Y., Xu, B., & Huang, M. (2026) · Fermentation · doi:10.3390/fermentation12090402
Jia, Z., Luo, Y.-Q., He, Y., Duan, Y., Huang, X.-F., Li, X.-Y., Bu, Q., Zhang, X., Hu, T., & Zhou, S.-M. (2026) · SynBio · doi:10.3390/synbio4030018
MicroRNA circuits and regulatory networks: emerging therapeutic paradigm
Goswami, S., & Singh, S. (2026) · RNA Biology · doi:10.1080/15476286.2026.2731890
Web servers and bioinformatics tools for cancer research: A review
Saini, C., Baweja, B., Vats, P., Patidar, P., Jangir, K., Kumar, N., Kumar, A., & Nema, R. (2026) · Journal of Biomedical Research · doi:10.7555/jbr.40.20260370
- Authors
- Matzko, R., & Konur, S.
- Affiliations
- University of Bradford (GB)
- Corresponding author
- Matzko, Richard
- Konur, Savas
- Venue
- Network Modeling Analysis in Health Informatics and Bioinformatics
- Published
- 2024-05-03
- Volume
- 13
- Issue
- 1
- ISSN
- 2192-6670 (ISSN-L)
- OpenAlex
- W4396617949
- Cited by
- 28
- References
- 106
- Citations by year
- 2026: 20 · 2025: 7 · 2024: 1
- FWCI
- 3.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Network Modeling Analysis in Health Informatics and Bioinformatics (publishedVersion, cc-by)
- 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
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Keywords
- synthetic biology, design-build-test-learn, computational modelling, data standards, data interoperability, genetic circuit design, genetic algorithm optimization, machine learning, assembly planning, combinatorial methods, whole-cell model, ontology (information science), metabolic engineering, parameter estimation, microfluidics, robotics, domain-specific languages, laboratory automation, reproducibility, in silico design
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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