AI-Powered Synthetic Biology: Current Situation, Challenges, and Future Perspectives
Abstract
Synthetic biology has evolved from a set of engineering aspirations to an operationally sophisticated discipline, and artificial intelligence (AI) is its fastest-growing accelerant. This review traces that convergence across six interlocking domains: systems-level biological modeling, de novo protein engineering, metabolic and microbial programming, multi-omics data integration, regulatory element design, and clinical translation. For each domain, we survey established results, integrate findings from 2010–2026 literature, and articulate the trajectories that will define the next decade. Emerging themes include physics-informed neural networks for mechanistically constrained biological modeling, drug design, and federated learning architectures that allow global omics collaboration without centralizing sensitive data, self-driving laboratories that close the Design-Build-Test-Learn loop with minimal human intervention, and large language models that accelerate hypothesis generation from scientific literature. Alongside these opportunities, the review gives equal weight to the governance challenges they create: dual-use risks amplified by generative sequence design, the reproducibility crisis in AI-driven biodesign, and the equitable distribution of autonomous experimentation capacity. The overarching argument is that the promise of synthetic biology, making biological design as deliberate and reliable as any mature engineering discipline, is closer than ever, but will only be realized if technical ambition is matched by scientific rigor, transparent governance, and inclusive access.
© 2026 Izem Olcay Sahin, Nuriye Gokce, Duygu T. Yildirim, A. Baki Yildirim, Hilal Akalın, Donald Martin, Tommaso Beccari, Oscar Vicente, Iza Radecka, Fideline Tchuenbou-Magaia, Robert S. Marks, Ratnesh Lal, Satya Prakash, Adam Mechler, Mario Petrov Milkov, Svetlana Fotkova Georgieva, Ilia Iliev, Kosi Gramatikoff, Ed Judge, Milica Markovic, Radka Kaneva, Galina Aleksieva Yaneva, Nadya Vasileva Agova, Nikoleta Dobromirova Ivanova, Mariya Kiryakova Tsvetkova, Ivelin Rosenov Iliev, Michel Salzet, Kisung Ko, Michele Maffia, Chiara Coppola, Matteo Bertelli, Isabelle Fournier, Lejla Pojskic, Qun Sun, Lembit Nei, Reynir Armgrisson, Gary Henehan, Daumantas Matulis, Dijana Plaseska-Karanfilska, K. Santacruz-Gomez, Isabel Belo, Štefánia Hrončeková, Sehime G. Temel, Ole K. Greiner-Tollersrud, Dana Tapaloaga, Andreas Janecke, Ivana Marova, Benedetta Spedicato, Polona Žnidaršič-Plazl, Igor Plazl, Ariola Bacu, Anargyros N. Moulas, Alexander Kilchevsky, M. Sait Dundar, P. Bartolini, Anita Slavica, Francisco Fuentes, Jose Carlos Lorenzo Feijoo, Andrés Izquierdo Romero, Helal Ragab Moussa, Amin Hejazi, Attya Bhatti, Bajram Berisha, Alma Kokhmetova, Nikolai Zhelev, Juraj Krajcovic, Viktor Nedovic, Alla Salmina, Mark Nujiten, İrem Kalay, Majeti Narasimha Vara Prasad, Luis Izquierdo Lopez, Dominika Vešelényiová, Maria Rachele Ceccarini, Bernard Fioretti, Gergana P. Ilieva, M. Cerkez Ergoren, Noursaid Tligui, Serghei Sprincean, Saharuddin Bin Mohamad, Miklos Kellermayer, Esra Arslan Ates, Pembe Savas, Ivana Márová, Martin Koller, Hakan Gurkan, Erhan Parıltay, Ercument Ovalı, Sevda Yesim Ozdemir, Tanil Kocagoz, Hilmi Tozkir, Gunnur Demircan, Eda Tahir Turanli, Havva Cobanogullari, Victor Nedovic, Victor Revin, Recep Eroz, Annika Joy Meitern, Munis Dundar, published by European Biotechnology Thematic Network Association
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