TY - GEN
T1 - Using Computational Synthetic Biology Tools to Modulate Gene Expression Within a Microbiome
AU - Chitayat Levi, Liyam
AU - Rippin, Ido
AU - Ben Tulila, Moran
AU - Galron, Rotem
AU - Tuller, Tamir
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The microbiome is an interconnected network of microorganisms, which exist and influence a wide array of natural and synthetic environments. Genetic information is constantly spread across the members of the microbial community in a process called horizontal gene transfer, causing exposure of genetic alterations and modifications to all members of the community. In order to accurately and effectively engineer microbiomes, genetic modifications must be introduced to certain species, as selectivity is a key factor in creating and fixing functional abilities within microbial environments. Moreover, introduction of genes into unwanted hosts may cause unprecedented ecological impacts, posing a major biosafety issue. Technologies in the field are usually experimentally developed for a specific host or environment, and the lack of automization and generalization limit them to a specific microbiome. Additionally, they only deal with the transformation process itself at best and do not modulate the different elements of the genetic material, neglecting considerations related to the interactions between the new genetic material and the population. This work presents a set of computational models that automatically design a microbiome-specific plasmid that is selectively expressed in certain parts of the bacterial population. The underlying algorithm fine-tunes genetic information to be optimally expressed in the wanted hosts of the plasmid, while simultaneously impairing expression in unwanted hosts. We take into account and selectively optimize the main elements linked to gene expression and heredity. In addition, we have provided both in-silico and in-vitro analysis supporting our claim. This study was part of the TAU IGEM 2021 project (https://2021.igem.org/Team:TAU_Israel ).
AB - The microbiome is an interconnected network of microorganisms, which exist and influence a wide array of natural and synthetic environments. Genetic information is constantly spread across the members of the microbial community in a process called horizontal gene transfer, causing exposure of genetic alterations and modifications to all members of the community. In order to accurately and effectively engineer microbiomes, genetic modifications must be introduced to certain species, as selectivity is a key factor in creating and fixing functional abilities within microbial environments. Moreover, introduction of genes into unwanted hosts may cause unprecedented ecological impacts, posing a major biosafety issue. Technologies in the field are usually experimentally developed for a specific host or environment, and the lack of automization and generalization limit them to a specific microbiome. Additionally, they only deal with the transformation process itself at best and do not modulate the different elements of the genetic material, neglecting considerations related to the interactions between the new genetic material and the population. This work presents a set of computational models that automatically design a microbiome-specific plasmid that is selectively expressed in certain parts of the bacterial population. The underlying algorithm fine-tunes genetic information to be optimally expressed in the wanted hosts of the plasmid, while simultaneously impairing expression in unwanted hosts. We take into account and selectively optimize the main elements linked to gene expression and heredity. In addition, we have provided both in-silico and in-vitro analysis supporting our claim. This study was part of the TAU IGEM 2021 project (https://2021.igem.org/Team:TAU_Israel ).
KW - Evolutionary systems biology
KW - Gene expression
KW - Horizontal gene transfer
KW - Microbiome engineering
KW - Population genomics
KW - Synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=85131121526&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06220-9_14
DO - 10.1007/978-3-031-06220-9_14
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AN - SCOPUS:85131121526
SN - 9783031062193
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 235
EP - 259
BT - Comparative Genomics - 19th International Conference, RECOMB-CG 2022, Proceedings
A2 - Jin, Lingling
A2 - Durand, Dannie
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th Annual RECOMB Satellite Workshop on Comparative Genomics, RECOMB-CG 2022
Y2 - 20 May 2022 through 21 May 2022
ER -