SynBAI - SSynthetic Biology and Artificial Intelligence
SynBAI projects
The SynBAI team research focuses on the development of de novo antimicrobial peptides via a combined approach of machine learning and synthetic biology. Additionally, we use machine learning models to predict and design phase-separating polypeptides and build codon and gene expression language models.
Project 1
To design and screen de novo antimicrobial peptides that are pathogen-specific and proteolytically stable.
Project 2
To design de novo phase-separating peptides for synthetic biology applications.
Project 3
To develop large language models for codon usage and gene expression.
People
Amir Pandi
Team leader
My work includes cell-free synthetic biology, genetic circuits design and AI-based development of antimicrobial/phase separating peptides
Publications
2024
- CodonTransformer: a multispecies codon optimizer using context-aware neural networks.
Fallahpour A, Gureghian V, Filion GJ, Lindner AB, Pandi A. bioRxiv. 2024 09.
doi: 10.1101/2024.09.13.612903.
2023
- Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides.
Pandi A, Adam D, Zare A, Trinh VT, Schaefer SL, Burt M, Klabunde B, Bobkova E, Kushwaha M, Foroughijabbari Y,
Braun P, Spahn C, Preußer C, Pogge von Strandmann E, Bode HB, von Buttlar H, Bertrams W, Jung AL, Abendroth F,
Schmeck B, Hummer G, Vázquez O, Erb TJ. Nat Commun. 2023 Nov 8;14(1):7197. doi: 10.1038/s41467-023-42434-9.
2022
- A versatile active learning workflow for optimization of genetic and metabolic networks.
Pandi A, Diehl C, Yazdizadeh Kharrazi A, Scholz SA, Bobkova E, Faure L, Nattermann M,
Adam D, Chapin N, Foroughijabbari Y, Moritz C. Paczia N, Socorro Cortina N, Faulon JL,
Erb TJ, Nat Commun. 2022 Jul 13(1):1-5. doi: 10.1038/s41467-022-31245-z.